# Table of Contents - [Illumina® DRAGEN™ Secondary Analysis | DRAGEN](#illumina-dragen-secondary-analysis-dragen) - [Illumina® DRAGEN™ Secondary Analysis | DRAGEN 4.5 (Illumina TruPath Genome) | DRAGEN](#illumina-dragen-secondary-analysis-dragen-4-5-illumina-trupath-genome-dragen) - [DRAGEN v4.5 | DRAGEN 4.5 (Illumina TruPath Genome) | DRAGEN](#dragen-v4-5-dragen-4-5-illumina-trupath-genome-dragen) - [Illumina TruPath Genome Prep | DRAGEN 4.5 (Illumina TruPath Genome) | DRAGEN](#illumina-trupath-genome-prep-dragen-4-5-illumina-trupath-genome-dragen) - [Illumina TruPath Genome WGS Recipe | DRAGEN 4.5 (Illumina TruPath Genome) | DRAGEN](#illumina-trupath-genome-wgs-recipe-dragen-4-5-illumina-trupath-genome-dragen) - [DRAGEN Server | DRAGEN](#dragen-server-dragen) - [DRAGEN Application Manager | DRAGEN](#dragen-application-manager-dragen) - [Support | DRAGEN](#support-dragen) - [Resource Files | DRAGEN](#resource-files-dragen) - [DRAGEN Pricing and Licensing | DRAGEN](#dragen-pricing-and-licensing-dragen) - [Troubleshooting | DRAGEN](#troubleshooting-dragen) - [Supplementary Information | DRAGEN](#supplementary-information-dragen) - [F2 Validation | DRAGEN](#f2-validation-dragen) - [DRAGEN Multi-Cloud | DRAGEN](#dragen-multi-cloud-dragen) - [Illumina® DRAGEN™ Secondary Analysis | DRAGEN v4.3 | DRAGEN](#illumina-dragen-secondary-analysis-dragen-v4-3-dragen) - [Citing DRAGEN software | DRAGEN](#citing-dragen-software-dragen) - [Deployment Options | DRAGEN v4.3 | DRAGEN](#deployment-options-dragen-v4-3-dragen) - [DRAGEN Applications | DRAGEN v4.3 | DRAGEN](#dragen-applications-dragen-v4-3-dragen) - [Release Notes | DRAGEN](#release-notes-dragen) - [DRAGEN v4.3 | DRAGEN](#dragen-v4-3-dragen) - [Revision History | DRAGEN](#revision-history-dragen) - [DRAGEN Product Obsolescence Notices | DRAGEN](#dragen-product-obsolescence-notices-dragen) - [DRAGEN Secondary Analysis | DRAGEN v4.3 | DRAGEN](#dragen-secondary-analysis-dragen-v4-3-dragen) - [Resource Files | DRAGEN v4.3 | DRAGEN](#resource-files-dragen-v4-3-dragen) - [Revision History | DRAGEN v4.3 | DRAGEN](#revision-history-dragen-v4-3-dragen) - [DRAGEN Multi-Cloud | DRAGEN v4.3 | DRAGEN](#dragen-multi-cloud-dragen-v4-3-dragen) - [Troubleshooting | DRAGEN v4.3 | DRAGEN](#troubleshooting-dragen-v4-3-dragen) - [DRAGEN Server | DRAGEN v4.3 | DRAGEN](#dragen-server-dragen-v4-3-dragen) - [Getting Started | DRAGEN v4.3 | DRAGEN](#getting-started-dragen-v4-3-dragen) - [Supplementary Information | DRAGEN v4.3 | DRAGEN](#supplementary-information-dragen-v4-3-dragen) - [Deployment Options | DRAGEN](#deployment-options-dragen) - [DRAGEN Reference Support | DRAGEN v4.3 | DRAGEN](#dragen-reference-support-dragen-v4-3-dragen) - [Release Notes | DRAGEN v4.3 | DRAGEN](#release-notes-dragen-v4-3-dragen) - [DRAGEN on AWS Batch | DRAGEN v4.3 | DRAGEN](#dragen-on-aws-batch-dragen-v4-3-dragen) - [Citing DRAGEN software | DRAGEN v4.3 | DRAGEN](#citing-dragen-software-dragen-v4-3-dragen) - [Run DRAGEN VM on Azure | DRAGEN v4.3 | DRAGEN](#run-dragen-vm-on-azure-dragen-v4-3-dragen) - [DRAGEN DNA Pipeline | DRAGEN v4.3 | DRAGEN](#dragen-dna-pipeline-dragen-v4-3-dragen) - [DRAGEN on AWS | DRAGEN v4.3 | DRAGEN](#dragen-on-aws-dragen-v4-3-dragen) - [DRAGEN on Microsoft Azure | DRAGEN v4.3 | DRAGEN](#dragen-on-microsoft-azure-dragen-v4-3-dragen) - [DRAGEN on Microsoft Azure Batch | DRAGEN v4.3 | DRAGEN](#dragen-on-microsoft-azure-batch-dragen-v4-3-dragen) - [DRAGEN Cloud Licensing | DRAGEN v4.3 | DRAGEN](#dragen-cloud-licensing-dragen-v4-3-dragen) - [DRAGEN Applications | DRAGEN](#dragen-applications-dragen) - [F2 Validation | DRAGEN v4.3 | DRAGEN](#f2-validation-dragen-v4-3-dragen) - [DRAGEN Server Licensing | DRAGEN v4.3 | DRAGEN](#dragen-server-licensing-dragen-v4-3-dragen) - [DRAGEN Single-Cell Pipeline | DRAGEN v4.3 | DRAGEN](#dragen-single-cell-pipeline-dragen-v4-3-dragen) - [DRAGEN Licensing | DRAGEN v4.3 | DRAGEN](#dragen-licensing-dragen-v4-3-dragen) - [DRAGEN Amplicon Pipeline | DRAGEN v4.3 | DRAGEN](#dragen-amplicon-pipeline-dragen-v4-3-dragen) - [Kmer Classifier Database Builder | DRAGEN v4.3 | DRAGEN](#kmer-classifier-database-builder-dragen-v4-3-dragen) - [RNA Variant Calling | DRAGEN v4.3 | DRAGEN](#rna-variant-calling-dragen-v4-3-dragen) - [Support | DRAGEN v4.3 | DRAGEN](#support-dragen-v4-3-dragen) - [DRAGEN RNA Pipeline | DRAGEN v4.3 | DRAGEN](#dragen-rna-pipeline-dragen-v4-3-dragen) - [Single-Cell Multiomics | DRAGEN v4.3 | DRAGEN](#single-cell-multiomics-dragen-v4-3-dragen) - [Kmer Classifier | DRAGEN v4.3 | DRAGEN](#kmer-classifier-dragen-v4-3-dragen) - [Prepare a Reference Genome | DRAGEN v4.3 | DRAGEN](#prepare-a-reference-genome-dragen-v4-3-dragen) - [Splice Variant Caller | DRAGEN v4.3 | DRAGEN](#splice-variant-caller-dragen-v4-3-dragen) - [RNA Alignment | DRAGEN v4.3 | DRAGEN](#rna-alignment-dragen-v4-3-dragen) - [Ploidy Calling | DRAGEN v4.3 | DRAGEN](#ploidy-calling-dragen-v4-3-dragen) - [Azure Batch Run Modes | DRAGEN v4.3 | DRAGEN](#azure-batch-run-modes-dragen-v4-3-dragen) - [Gene Expression Quantification | DRAGEN v4.3 | DRAGEN](#gene-expression-quantification-dragen-v4-3-dragen) - [Gene Fusion Detection | DRAGEN v4.3 | DRAGEN](#gene-fusion-detection-dragen-v4-3-dragen) - [scATAC | DRAGEN v4.3 | DRAGEN](#scatac-dragen-v4-3-dragen) - [Noise Baselines | DRAGEN v4.3 | DRAGEN](#noise-baselines-dragen-v4-3-dragen) - [DRAGEN Methylation Pipeline | DRAGEN v4.3 | DRAGEN](#dragen-methylation-pipeline-dragen-v4-3-dragen) - [Illumina Connected Annotations | DRAGEN v4.3 | DRAGEN](#illumina-connected-annotations-dragen-v4-3-dragen) - [High Coverage Analysis | DRAGEN v4.3 | DRAGEN](#high-coverage-analysis-dragen-v4-3-dragen) - [DRAGEN v4.5 | DRAGEN](#dragen-v4-5-dragen) - [Tools and Utilities | DRAGEN v4.3 | DRAGEN](#tools-and-utilities-dragen-v4-3-dragen) - [Sorting and Duplicate Marking | DRAGEN v4.3 | DRAGEN](#sorting-and-duplicate-marking-dragen-v4-3-dragen) - [Filter Duplicate Variants | DRAGEN v4.3 | DRAGEN](#filter-duplicate-variants-dragen-v4-3-dragen) - [Downsampling | DRAGEN v4.3 | DRAGEN](#downsampling-dragen-v4-3-dragen) - [ORA Compression | DRAGEN v4.3 | DRAGEN](#ora-compression-dragen-v4-3-dragen) - [Biomarkers | DRAGEN v4.3 | DRAGEN](#biomarkers-dragen-v4-3-dragen) - [Indel Re-aligner (Beta) | DRAGEN v4.3 | DRAGEN](#indel-re-aligner-beta-dragen-v4-3-dragen) - [DRAGEN Apps | DRAGEN](#dragen-apps-dragen) - [DRAGEN Reports | DRAGEN v4.3 | DRAGEN](#dragen-reports-dragen-v4-3-dragen) - [Repeat Expansion Detection | DRAGEN v4.3 | DRAGEN](#repeat-expansion-detection-dragen-v4-3-dragen) - [DUX4 Rearrangement Caller | DRAGEN v4.3 | DRAGEN](#dux4-rearrangement-caller-dragen-v4-3-dragen) - [DRAGEN FASTQC | DRAGEN v4.3 | DRAGEN](#dragen-fastqc-dragen-v4-3-dragen) - [DRAGEN Host Software | DRAGEN v4.3 | DRAGEN](#dragen-host-software-dragen-v4-3-dragen) - [HLA Typing | DRAGEN v4.3 | DRAGEN](#hla-typing-dragen-v4-3-dragen) - [CheckFingerprint | DRAGEN v4.3 | DRAGEN](#checkfingerprint-dragen-v4-3-dragen) - [DNA Mapping | DRAGEN v4.3 | DRAGEN](#dna-mapping-dragen-v4-3-dragen) - [Multi Caller | DRAGEN v4.3 | DRAGEN](#multi-caller-dragen-v4-3-dragen) - [DRAGEN Recipes | DRAGEN v4.3 | DRAGEN](#dragen-recipes-dragen-v4-3-dragen) - [DRAGEN Secondary Analysis | DRAGEN](#dragen-secondary-analysis-dragen) - [Star Allele Caller | DRAGEN v4.3 | DRAGEN](#star-allele-caller-dragen-v4-3-dragen) - [Read Trimming | DRAGEN v4.3 | DRAGEN](#read-trimming-dragen-v4-3-dragen) - [DNA Germline WGS | DRAGEN v4.3 | DRAGEN](#dna-germline-wgs-dragen-v4-3-dragen) - [RNA WTS | DRAGEN v4.3 | DRAGEN](#rna-wts-dragen-v4-3-dragen) - [Clinical Research Workflows | DRAGEN](#clinical-research-workflows-dragen) - [VNTR Calling | DRAGEN v4.3 | DRAGEN](#vntr-calling-dragen-v4-3-dragen) - [RNA Panel | DRAGEN v4.3 | DRAGEN](#rna-panel-dragen-v4-3-dragen) - [DNA Germline Panel | DRAGEN v4.3 | DRAGEN](#dna-germline-panel-dragen-v4-3-dragen) - [Targeted Caller | DRAGEN v4.3 | DRAGEN](#targeted-caller-dragen-v4-3-dragen) - [DNA Germline Panel UMI | DRAGEN v4.3 | DRAGEN](#dna-germline-panel-umi-dragen-v4-3-dragen) - [DNA Germline WGS UMI | DRAGEN v4.3 | DRAGEN](#dna-germline-wgs-umi-dragen-v4-3-dragen) --- # Illumina® DRAGEN™ Secondary Analysis | DRAGEN ![Page cover](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F25033470-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FG9szlFZupV6Q2DasL98y%252Fuploads%252Fgit-blob-d4d60fd0f9a66c771db8570635722d2083888f52%252FDRAGEN%2520Logo%2520Cover%2520-%2520updated.png%3Falt%3Dmedia&width=1248&dpr=3&quality=100&sign=7aae315d&sv=2) Illumina DRAGEN (Dynamic Read Analysis for GENomics) secondary analysis was developed to address important challenges associated with analyzing NGS (Next Generation Sequencing) data for a range of applications, including genome, exome, transcriptome, and methylome studies. DRAGEN secondary analysis processes NGS data and enables tertiary analysis to drive insights. The available tools make up a highly accurate, comprehensive, and efficient solution that enables labs of all sizes and disciplines to do more with their genomic data. **Product highlights** **Accurate results:** * Pangenome reference genome and machine learning drive unprecedented accuracy * 99.89% accuracy score with the Precision FDA Truth Challenge V2 benchmark data (_2,3_) **Comprehensive platform:** * Analyze NGS data from whole genomes, exomes, methylomes, and transcriptomes * Available on platform of choice and scalable based on needs **Efficient analysis:** * Process a 34x genome in ~ 30 minutes, with all supported callers with DRAGEN server v4 (_1_) * Reduce FASTQ file sizes up to 5x with DRAGEN ORA Compression _References:_ 1. Illumina data on file, 2022. 2. Illumina DRAGEN Secondary Analysis is the first single platform to achieve 99.89% accuracy based on [PrecisionFDA v2 Truth Challenge Benchmark Dataarrow-up-right](https://precision.fda.gov/challenges/10) . Details here [DRAGEN sets new standard for data accuracy in PrecisionFDA benchmark dataarrow-up-right](https://www.illumina.com/science/genomics-research/articles/dragen-shines-again-precisionfda-truth-challenge-v2.html) . Accessed March 22, 2023 3. PrecisionFDA Truth Challenge V2: Calling Variants from Short and Long Reads in Difficult-to-Map Regions. [precision.fda.gov/challenges/10arrow-up-right](https://precision.fda.gov/challenges/10) . Accessed November 3, 2020. [NextDRAGEN Applicationschevron-right](https://help.dragen.illumina.com/overview/key-applications) Last updated 20 days ago Was this helpful? Was this helpful? --- # Illumina® DRAGEN™ Secondary Analysis | DRAGEN 4.5 (Illumina TruPath Genome) | DRAGEN ![Page cover](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3156241411-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FWueVEHA9PEBUPz0J4TCJ%252Fuploads%252Fgit-blob-d4d60fd0f9a66c771db8570635722d2083888f52%252FDRAGEN%2520Logo%2520Cover%2520-%2520updated.png%3Falt%3Dmedia&width=1248&dpr=3&quality=100&sign=6532eb7&sv=2) Illumina DRAGEN (Dynamic Read Analysis for GENomics) secondary analysis was developed to address important challenges associated with analyzing NGS (Next Generation Sequencing) data for a range of applications, including genome, exome, transcriptome, and methylome studies. DRAGEN secondary analysis processes NGS data and enables tertiary analysis to drive insights. The available tools make up a highly accurate, comprehensive, and efficient solution that enables labs of all sizes and disciplines to do more with their genomic data. **Product highlights** **Accurate results:** * Pangenome reference genome and machine learning drive unprecedented accuracy * 99.89% accuracy score with the Precision FDA Truth Challenge V2 benchmark data (_2,3_) **Comprehensive platform:** * Analyze NGS data from whole genomes, exomes, methylomes, and transcriptomes * Available on platform of choice and scalable based on needs **Efficient analysis:** * Process a 34x genome in ~ 30 minutes, with all supported callers with DRAGEN server v4 (_1_) * Reduce FASTQ file sizes up to 5x with DRAGEN ORA Compression _References:_ 1. Illumina data on file, 2022. 2. Illumina DRAGEN Secondary Analysis is the first single platform to achieve 99.89% accuracy based on [PrecisionFDA v2 Truth Challenge Benchmark Dataarrow-up-right](https://precision.fda.gov/challenges/10) . Details here [DRAGEN sets new standard for data accuracy in PrecisionFDA benchmark dataarrow-up-right](https://www.illumina.com/science/genomics-research/articles/dragen-shines-again-precisionfda-truth-challenge-v2.html) . Accessed March 22, 2023 3. PrecisionFDA Truth Challenge V2: Calling Variants from Short and Long Reads in Difficult-to-Map Regions. [precision.fda.gov/challenges/10arrow-up-right](https://precision.fda.gov/challenges/10) . Accessed November 3, 2020. [NextDRAGEN v4.5chevron-right](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5) Last updated 29 days ago Was this helpful? Was this helpful? --- # DRAGEN v4.5 | DRAGEN 4.5 (Illumina TruPath Genome) | DRAGEN [Illumina TruPath Genome Prepchevron-right](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline) [Illumina TruPath Genome WGS Recipechevron-right](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/illumina-trupath-genome-wgs) [PreviousIllumina® DRAGEN™ Secondary Analysischevron-left](https://help.dragen.illumina.com/dragen-v4.5-trupath) [NextIllumina TruPath Genome Prepchevron-right](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline) Last updated 28 days ago Was this helpful? Was this helpful? --- # Illumina TruPath Genome Prep | DRAGEN 4.5 (Illumina TruPath Genome) | DRAGEN DRAGEN’s Germline pipeline integrates proximity mapped reads from the Illumina TruPath Genome prep to enhance genomic analysis using long-range information encoded on the flowcell. This proximity-aware workflow supports highly accurate read mapping, phasing, and variant detection, including structural variants, paralog‑resolved small variants, short tandem repeat (STR) genotyping, and colocation analysis. By modeling and applying read‑to‑read linkage probabilities, the pipeline enables more confident interpretation of complex and low‑mappability genomic regions using standard short‑read data. [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#summary) Summary --------------------------------------------------------------------------------------------------------------------------------------- * **Integrated TruPath proximity mapping**: Enabling `--enable-proximity=true` activates proximity-aware modeling and analysis across the DRAGEN Germline pipeline, allowing reads that are spatially close on the flowcell to be probabilistically linked as originating from the same DNA template. * **Proximity model-driven mapping and alignment**: DRAGEN performs a preliminary mapping pass to collect high‑confidence alignments and fits a non‑linear proximity linking model that relates flowcell spatial distance and genomic distance to read‑to‑read linkage probability. The resulting Phred‑scaled linkage probability lookup table is applied during map/align to resolve ambiguous mappings and improve read placement accuracy in repetitive and complex genomic regions. * **Enhanced phasing support**: Proximity information strengthens read phasing by associating reads from the same original template molecule, enabling longer and more reliable phasing blocks that propagate into variant calling and assembly‑based analyses. * **Structural variant calling**: The Germline SV caller leverages proximity‑derived phasing to support phased assemblies, haplotype‑aware machine‑learning features, and haplotype‑resolved genotyping for single‑sample TruPath whole‑genome analyses. * **Haplotype‑resolved small variant detection in paralogs**: For clinically relevant paralogous regions, Multi‑Region Joint Detection (MRJD) estimates total copy number from read depth, reconstructs individual paralog copies using read sequences and proximity information, assigns each copy to a genomic region or haplotype, and calls small variants from the reconstructed copies. * **STR genotyping with IRR recovery**: Proximity linking enables recovery and placement of in‑repeat reads (IRRs) that would otherwise be unmapped, improving detection and sizing of large STR expansions and supporting phasing‑aware genotyping. * **Colocation analysis and filtering**: Colocation maps summarize long-range genomic interactions using proximity‑linked reads and are used to visualize structural features and filter SV breakends lacking proximity support. * **Specialized outputs and reporting**: The pipeline generates proximity‑aware BAM/CRAM files, VCFs, JSON summaries, cooler files, and TruPath‑specific DRAGEN Reports with dedicated QC metrics and visualizations. [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#overview) Overview ----------------------------------------------------------------------------------------------------------------------------------------- Short‑read DNA sequencing typically captures genomic variation at high accuracy but lacks long-range context needed to confidently resolve complex regions such as repeats, paralogs, and structural variants. The **Illumina TruPath Genome Prep** encodes long-range molecular information directly on the flowcell by preserving spatial proximity between reads derived from the same original DNA molecule. When combined with DRAGEN’s proximity‑aware algorithms, this information enables long-range analysis that extends the power of standard short‑read data. The **DRAGEN Germline pipeline for Illumina TruPath Genome** leverages this flowcell‑encoded proximity information through a probabilistic proximity linking model that assigns read‑to‑read linkage probabilities based on spatial and genomic distance. When proximity mode is enabled, DRAGEN automatically fits this model, generates Phred‑scaled proximity link probability distributions, and applies them across mapping, phasing, and variant calling workflows. These proximity linkage probabilities serve as a foundational signal reused throughout the pipeline—informing alignment scoring, phasing blocks, candidate assemblies, machine‑learning features, and variant filtering—to improve accuracy and confidence in repetitive and structurally complex genomic regions while remaining compatible with standard short‑read sequencing workflows and formats. ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3156241411-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FWueVEHA9PEBUPz0J4TCJ%252Fuploads%252Fgit-blob-57ffca5a19e2d2bb1fa3cdc2c02cbabb5361ab5c%252Fdragen_user_guide_workflow.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=d5036fcc&sv=2) Illumina TruPath Genome Analysis Workflow [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#proximity-mode-analysis-in-dragen) Proximity Mode Analysis in DRAGEN ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- When proximity mode is enabled, DRAGEN automatically performs additional modeling and downstream analyses that integrate proximity information throughout the Germline pipeline. TruPath‑specific proximity analysis is activated by enabling proximity during a DRAGEN Germline run setting `--enable-proximity=true`. This proximity‑aware processing supports the following workflow and features: * High‑accuracy read mapping using linkage‑informed alignment scoring * Enhanced phasing via read‑to‑template association * Structural variant calling using phased assemblies and haplotype‑aware algorithms * Paralog‑resolved small variant detection with Multi‑Region Joint Detection (MRJD) * Improved STR genotyping through in‑repeat read (IRR) recovery * Long-range genomic interaction analysis and SV filtering using colocation maps ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3156241411-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FWueVEHA9PEBUPz0J4TCJ%252Fuploads%252Fgit-blob-36a5a2c50e234e696ba553b2917ecf76d9f36f41%252Foutput_files_graphic.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=a532b1da&sv=2) DRAGEN Analysis Workflow [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#key-benefits-of-trupath-genome-vs-standard-illumina-sbs) Key Benefits of TruPath Genome vs Standard Illumina SBS --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- When DRAGEN Germline with proximity mode is enabled for TruPath Genome data, improvements are observed relative to standard Illumina SBS inputs across multiple performance dimensions. These include improved small variant calling accuracy, longer phasing blocks, a higher proportion of fully phased genes, and improved structural variant recall. The table below summarizes key performance metrics across TruPath and standard Illumina SBS datasets. Benefit TruPath, high molecular weight input DNA TruPath, standard molecular weight input DNA Standard Illumina SBS on DRAGEN 4.4 **Best-in-class small variant calling performance** 36,717 FP+FN 40,267 FP+FN 61,288 **Multi-megabase phasing blocks** 8.1 Mbp 649 kbp NA **Fully phased genes** 98.4% 87.6% 0% **Improved SV recall** 94.0% 93.7% 80.7% ### [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#phased-high-quality-small-variant-calls-in-clinically-relevant-gene-families) Phased, High-Quality Small Variant Calls in Clinically Relevant Gene Families TruPath proximity-aware analysis enables haplotype‑resolved, copy‑number‑aware small variant calling in ten clinically relevant paralogous gene families using [Multi-Region Joint Detection (MRJD)](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#multi-region-joint-detection) , as shown in the table below. With TruPath data, MRJD produces phased variant calls across these supported paralogous regions without reliance on population haplotypes. **Supported Genes** Paralogous Gene Disease Relevance **PMS2** Lynch Syndrome **SMN1–SMN2** Spinal Muscular Atrophy **NCF1** Chronic Granulomatous Disease **CYP21A2** Congenital Adrenal Hyperplasia **TNXB** Ehlers–Danlos syndrome **STRC** Recessive Nonsyndromic Hearing Loss **CYP2D6** Pharmacogenetics **CYP11B1–CYP11B2** Glucocorticoid-remediable Aldosteronism **CFHR1–CFHR2–CFHR3–CFHR4** Atypical Hemolytic Uremic Syndrome **USP18** Type I Interferonopathy The figure below illustrates haplotype‑resolved variant calls generated by MRJD for _PMS2_ and _PMS2CL_, reported as separate copies for each locus, with long‑read data shown for comparison. ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3156241411-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FWueVEHA9PEBUPz0J4TCJ%252Fuploads%252Fgit-blob-fc5e4dda6b362a04402da330984028cb2012ff75%252FTruPath_PMS2.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=7d2b896&sv=2) Haplotype-Resolved Variant Calls in \*PMS2\* and \*PMS2CL\* ### [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#improved-str-expansion-length-and-classification-accuracy) Improved STR Expansion Length and Classification Accuracy TruPath analysis improves short tandem repeat (STR) expansion length estimation by recovering fragments composed entirely of STR sequence and by applying sequencing efficiency correction to account for locus‑specific coverage bias. These improvements result in STR length estimates that more closely track expected repeat sizes and support more accurate expansion classification. The figure below compares STR expansion length estimates generated using standard Illumina sequencing and TruPath analysis across multiple loci. ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3156241411-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FWueVEHA9PEBUPz0J4TCJ%252Fuploads%252Fgit-blob-4790283f59163b99023fe3d3ae7dfff058a700c2%252Fstandard_trupath_wgs_str.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=43b69ab3&sv=2) STR Length Estimation for Standard Illumina Sequencing and TruPath ### [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#improved-bnd-filtering) Improved BND Filtering TruPath proximity information enables more selective filtering of large (>200 kbp) inter‑ and intra‑chromosomal breakend (BND) calls produced by DRAGEN Structural-Variant (SV) Calling. Incorporating colocation evidence reduces the number of reported large BND events while maintaining recall. This effect is observed for both intra‑chromosomal and inter‑chromosomal BNDs across evaluated samples. Summarized below is BND recall and reduction in reported intra‑ and inter‑chromosomal BND calls for TruPath Coriell samples (n=45), with and without colocation filtering. ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3156241411-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FWueVEHA9PEBUPz0J4TCJ%252Fuploads%252Fgit-blob-9b900570a175d12b9fc66c84dac85e01a3d2e459%252Fcolocation_bnd_filter.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=d0329a66&sv=2) DRAGEN-SV BND call reduction with TruPath [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#proximity-linking-model) Proximity Linking Model ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- In Illumina TruPath Genome data, read pairs that are proximal on the flowcell have an increased likelihood of originating from the same original template molecule. To quantify this likelihood, DRAGEN uses a probabilistic proximity linking model that relates genomic distance and flowcell proximity to calculate the probability that two reads originate from the same input DNA molecule. When DRAGEN is run with `--enable-proximity=true`, the mapper estimates the parameters of this proximity linking model and generates a link probability distribution for each TruPath FASTQ input. This process consists of three stages: sample collection, proximity analysis, and model fitting, followed by generation of a link probability lookup table. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#sample-collection) Sample Collection To fit the proximity linking model, DRAGEN first collects a representative subset of preliminary alignments from the input data. During an initial mapping pass, alignments are generated in flowcell‑tile-sized batches and reads meeting suitability requirements are retained for proximity analysis. Eligible preliminary alignments must satisfy the following criteria: * Mapped with MAPQ ≥ 60 * Primary alignments * Non‑duplicate reads * For paired‑end data, first‑in‑pair with a mapped mate and proper pairing DRAGEN continues sampling until one million qualifying preliminary alignments have been collected or until the entire FASTQ input has been processed. If fewer than one million alignments are collected, processing continues with a warning indicating a potentially insufficient sample. If no suitable alignments are found, DRAGEN exits with an error. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#proximity-analysis) Proximity Analysis Once a sufficient set of preliminary alignments has been collected, DRAGEN analyzes read pairs that are both spatially proximal on the flowcell and genomically proximal on the reference genome. Read pairs meeting both criteria have a high likelihood of originating from the same template molecule. Each alignment is associated with a mapped genomic position and a flowcell coordinate (X, Y). For candidate read pairs, DRAGEN computes: * Spatial displacement on the flowcell, represented as (`XD`, `YD`) in nanometers * Genomic displacement, represented as `GDIST` in base pairs and rounded to the nearest 1,000 bp Read pairs whose spatial and genomic displacements fall within configured proximity thresholds are considered likely linked. For these pairs, counts are aggregated across combinations of `XD`, `YD`, and `GDIST`. These aggregated counts form the empirical input to the model fitting stage. A second set of counts is also collected using read pairs that are spatially proximal but genomically distant. These pairs are assumed to represent chance colocation and are used to model background noise. Before proceeding, DRAGEN evaluates both sets of counts to ensure the observed trends are consistent with TruPath data. If the data fails validation, DRAGEN exits with an error. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#model-fitting) Model Fitting The proximity linking model is non‑linear and includes approximately 20 parameters that predict the expected number of linked read pairs (`N`) as a function of `XD`, `YD`, and `GDIST`. The aggregated counts from proximity analysis are submitted to a non‑linear least‑squares solver to estimate these parameters. If the solver fails to converge, DRAGEN exits with an error. When fitting succeeds, the model enables calculation of the expected number of linked read pairs, μ(XD,YD,GDIST)\\mu(\\text{XD}, \\text{YD}, \\text{GDIST})μ(XD,YD,GDIST), which provides a smoothed estimate relative to the empirical counts. A separate background model estimates the expected number of proximal read pairs due to chance, μchance(XD,YD,GDIST)\\mu\_\\text{chance}(\\text{XD}, \\text{YD}, \\text{GDIST})μchance​(XD,YD,GDIST). The link probability is then computed as: 1−μchance(XD,YD,GDIST)μ(XD,YD,GDIST)1 - \\frac{\\mu\_\\text{chance}(\\text{XD}, \\text{YD}, \\text{GDIST})}{\\mu(\\text{XD}, \\text{YD}, \\text{GDIST})}1−μ(XD,YD,GDIST)μchance​(XD,YD,GDIST)​ This probability is typically expressed on a Phred scale as: −10log⁡10(μchance(XD,YD,GDIST)μ(XD,YD,GDIST))\-10 \\log\_{10} \\left(\\frac{\\mu\_\\text{chance}(\\text{XD}, \\text{YD}, \\text{GDIST})}{\\mu(\\text{XD}, \\text{YD}, \\text{GDIST})}\\right)−10log10​(μ(XD,YD,GDIST)μchance​(XD,YD,GDIST)​) Higher values indicate a stronger likelihood that two reads originated from the same template molecule. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#link-probability-distribution-generation) Link Probability Distribution Generation After successful model fitting, DRAGEN evaluates the fitted model across the practical range of spatial and genomic displacements and stores the resulting link probabilities in a lookup table. The table is generated continuously until link probabilities fall below a minimum threshold. This lookup table represents the primary output of the TruPath proximity linking model and is used downstream by the DRAGEN Germline pipeline to incorporate proximity information during mapping, template tagging, and variant calling. In rare cases where the fitted model fails to produce meaningful link probabilities above the minimum threshold, an empty lookup table is generated and DRAGEN exits with an error. [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#map-align) Map/Align ------------------------------------------------------------------------------------------------------------------------------------------- The proximity linking model is used during mapping to improve read alignment accuracy for TruPath samples. In regions of high sequence homology, standard Illumina sequencing reads may align equally well, or nearly so, to multiple genomic locations, resulting in ambiguous mappings. With TruPath data, proximity‑linked read pairs can provide additional context that enables both reads in a pair to be mapped uniquely. Read pairs originating from a region of interest on the flowcell are processed through the standard mapping workflow. Multiple candidate alignments are generated and scored, and key attributes—including alignment score, genomic position, and flowcell position—are stored in an indexed data structure. For each read pair `X` that may benefit from proximity information, the mapper revisits the candidate alignments and searches the data structure for other read pairs `Y` whose alignment and flowcell positions suggest a shared template of origin. The proximity linking model quantifies the likelihood that `X` and `Y` originated from the same original DNA molecule. A Phred‑scaled score derived from this likelihood is incorporated into the corresponding joint alignment hypothesis. [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#template-tagging) Template Tagging --------------------------------------------------------------------------------------------------------------------------------------------------------- During alignment, the mapper assigns each read a set of link probability scores that estimate the likelihood of links between the read and other nearby reads on the flowcell. Template tagging uses these scores to reconstruct the original template DNA molecules from which paired reads originated. Template tagging begins by grouping reads into fragments, where each fragment consists of a paired‑end read pair. For each fragment, outgoing link probability scores are collected from the constituent reads. Links with Phred‑scaled quality below the threshold specified by `--proximity-min-linkq-threshold` (default: 10) are discarded. The remaining high‑quality links are used to connect fragments into templates. Each connected set of fragments represents a reconstructed template molecule. All reads assigned to the same template are annotated with a shared template identifier in the BAM file (`BX:Z`), allowing reads originating from the same original DNA molecule to be identified downstream. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#outputs) Outputs Template tagging generates a set of metrics reports that describe characteristics of all discovered templates and links identified during the DRAGEN run. Reports are produced for whole‑genome data and for any specified QC regions. A template or link is included in QC region metrics if any portion of its genomic span overlaps the QC region. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#template-metrics) Template Metrics **Template Subpair Count Report** The template subpair count report, `._template_subpairs.csv`, summarizes the distribution of discovered templates by the number of fragments (subpairs) they contain. A _subpair_ refers to a read‑pair fragment within a template. Each record in the report describes the number of templates observed with a given fragment count and the corresponding percentage of all templates. Summary statistics, including the mean and selected percentiles of subpair counts, are also reported. Example summary statistics include the mean subpair count and the 25th, 50th, 75th, and 95th percentile subpair counts across all templates. **Template Genomic Distance Report** The template genomic distance report, `._template_gdist.csv`, describes the distribution of template genomic lengths from the 0th to the 100th percentile. Template genomic length is defined as the genomic distance between the smallest and largest mapped genomic positions represented in the template, corresponding to the span from the start of the first fragment to the end of the last fragment. Percentile values are interpolated from the distribution of all discovered template lengths and may therefore be non‑integer base‑pair values. **Template Spatial Distance Reports** Template spatial distance reports describe the distribution of template spatial extents in flowcell units (FCU) from the 0th to the 100th percentile. Two reports are generated: * `._template_xdist.csv`, describing spatial extent along the flowcell X axis * `._template_ydist.csv`, describing spatial extent along the flowcell Y axis Template spatial length is defined as the distance between the smallest and largest flowcell coordinates represented in the template along the corresponding axis. As with genomic distances, percentile values are interpolated from the observed distribution and may be non‑integer FCU values. **Template Length Thresholds Report** The template length thresholds report, `._template_thresholds.csv`, summarizes the count and proportion of discovered templates whose genomic lengths exceed specified thresholds. Template genomic length is defined as the span between the smallest and largest mapped genomic positions within a template. Thresholds reported in this file are defined using the `--template-gdist-thresholds` option (default: 10000, 20000, 60000). Each record reports the threshold value, the number of templates meeting or exceeding that threshold, and the corresponding proportion of all discovered templates. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#link-metrics) Link Metrics Link metrics are generated for each Phred‑scaled link quality threshold specified at runtime. These thresholds control which links are considered when computing proximity‑based metrics. The following options determine link metric generation: * `--proximity-min-linkq-threshold` * Specifies the primary link quality threshold used to accept or reject link hypotheses during template tagging (default: 10). * `--proximity-additional-linkq-thresholds` * Specifies up to two additional link quality thresholds at which link metrics are computed (default: 25). **Link Genomic Distance Report** The link genomic distance report, `._proximity_gdist.csv`, describes the distribution of genomic distances for links that meet or exceed a specified link quality threshold. Link genomic length is defined as the genomic distance between the two fragments connected by the link. Distances are reported from the 0th to the 100th percentile. Percentile values are interpolated from the distribution of all discovered link lengths and may therefore be non‑integer base‑pair values. **Link Spatial Distance Reports** Link spatial distance reports describe the spatial extent of links in flowcell units (FCU) from the 0th to the 100th percentile. Two reports are generated for each link quality threshold: * `._proximity_xdist.csv`, reporting spatial extent along the flowcell X axis * `._proximity_ydist.csv`, reporting spatial extent along the flowcell Y axis Link spatial length is defined as the distance between the flowcell coordinates of the two fragments connected by the link along the corresponding axis. As with genomic distance metrics, percentile values are interpolated from the observed distribution and may be non‑integer flowcell unit values. [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#phasing) Phasing --------------------------------------------------------------------------------------------------------------------------------------- When TruPath data is used, DRAGEN performs read phasing upstream of variant calling and uses haplotype‑phased reads to generate phased variant calls. Phasing is informed by both long‑range proximity linking information provided by the TruPath library preparation and inference of the sample's ancestral haplotypes, which enables robust phasing across long genomic distances. DRAGEN personalization provides the ancestral component of phasing information by inferring the sample’s ancestral haplotypes, such that phasing is typically inferred to be consistent with that observed in the ancestral haplotypes. As in the standard personalization workflow, DRAGEN also uses variants imputed from the haplotype database to inform prior probabilities for variants in the sample, providing a boost to variant calling performance. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#phasing-model-overview) Phasing Model Overview DRAGEN performs phasing at the level of small, contiguous genomic bins, typically 4,096 bp in length. Within each bin, haplotypes are inferred using the haplotype database in the reference hash table, and reads are assigned accordingly. Proximity linking information is used to propagate phasing information across bins. Bins are grouped into larger, non‑overlapping phase blocks when there is sufficient evidence of co‑phasing. Each bin is phased in the context of ancestral haplotypes inferred from neighboring bins and from linked reads elsewhere in the genome. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#phasing-options) Phasing Options Phasing is enabled automatically when proximity mode is enabled using --enable-proximity=true. No additional arguments are required. Default settings are recommended, but phasing behavior can be adjusted using the following options: * `--personalization-phase-block-threshold` * Controls the amount of evidence required to group adjacent bins into a single phase block (default: 20). * `--read-phasing-gene-list` * Specifies an optional GTF file used to compute gene‑based phasing metrics for genes fully contained within phase blocks. Lowering the phase-block threshold parameter will reduce the amount of co-phasing evidence required to group adjacent personalization bins into a single phase block, and vice versa. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#output-files) Output Files #### [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#bam-cram-output) BAM/CRAM Output The phased reads in the map/align output file are annotated with the following tags: Tag Description Values `pp` Phasing probability in Phred-scale log odds: 10∗log⁡10(P(H1)/P(H2))10 \* \\log\_{10}(P(H\_1) / P(H\_2))10∗log10​(P(H1​)/P(H2​)) \[−127,127\]\[-127, 127\]\[−127,127\] `HP` Haplotype tag for all reads where ∥pp∥≥10\\|{pp}\\| \\geq 10∥pp∥≥10 1,21,21,2 `PS` Phase block tag \[0,232)\[0,2^{32})\[0,232)\ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#personalized-haplotypes)\ \ Personalized Haplotypes\ \ Personalized haplotypes for each phased bin are output in tab-delimited format (TSV). A summary of the phase blocks defined in the TSV file is also written in GTF format.\ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#tsv-less-than-sample_id-greater-than.personal_haplotypes.tsv.gz)\ \ TSV (`.personal_haplotypes.tsv.gz`)\ \ The personalized haplotypes TSV file contains the following columns:\ \ Column\ \ Description\ \ `CHROM`\ \ Chromosome name\ \ `START`\ \ Start position of the phased bin (0-based)\ \ `END`\ \ End position of the phased bin (1-based)\ \ `PHASE_BLOCK`\ \ Phase block ID for the bins. Bins with the same IDs are confidently co-phased.\ \ `PHASING_CONFIDENCE`\ \ Phasing confidence for the bin. Lower confidence values indicate a higher likelihood of haplotype switching.\ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#gtf-less-than-sample_id-greater-than.phase_blocks.gtf.gz)\ \ GTF (`.phase_blocks.gtf.gz`)\ \ Regions covered by the phase blocks, as defined in the personalized TSV file's `PHASE_BLOCK` column, are also output in a GTF file with the following fields:\ \ Column\ \ Description\ \ `seqname`\ \ Chromosome name\ \ `source`\ \ Always 'dragen'\ \ `feature`\ \ Always 'phaseblock'\ \ `start`\ \ Start position of the phase block (1-based)\ \ `end`\ \ End position of the phase block (1-based)\ \ `score`\ \ Unused ('.')\ \ `strand`\ \ Unused ('.')\ \ `frame`\ \ Unused ('.')\ \ `attribute`\ \ Always 'phase\_block n'\ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#imputed-variants)\ \ Imputed Variants\ \ Imputed variants for each phased bin are output in a VCF file. This VCF contains only variants imputed from the haplotype database in the reference hash table. It does not include novel variants observed in the sample, and multi‑allelic variants are split into separate records.\ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#vcf-less-than-sample_id-greater-than.personal.vcf.gz)\ \ VCF (`.personal.vcf.gz`)\ \ The VCF follows the 4.2 standard, below is the description of relevant fields:\ \ Tag\ \ Description\ \ `QUAL`\ \ Phred-scale score for the marginal probability of ALT. For example, for a diploid variant: −10∗log10(P(GT=’0∣0’))\-10\*log\_{10}(P(\\text{GT='0}\\vert\\text{0'}))−10∗log10​(P(GT=’0∣0’))\ \ `INFO:HAPS`\ \ Two best haplotype pairs for the bin the variant belongs to\ \ `INFO:PGP`\ \ Marginal probability for P(GT=’0∣0’),P(GT=’1∣0’)+P(GT=’0∣1’),P(GT=’1∣1’)P(\\text{GT='0}\\vert\\text{0'}),P(\\text{GT='1}\\vert\\text{0'}) + P(\\text{GT='0}\\vert\\text{1'}),P(\\text{GT='1}\\vert\\text{1'})P(GT=’0∣0’),P(GT=’1∣0’)+P(GT=’0∣1’),P(GT=’1∣1’)\ \ `FORMAT:PS`\ \ Phase block ID for the bin the variant belongs to\ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#phasing-metrics)\ \ Phasing Metrics\ \ DRAGEN reports a set of phasing metrics for each TruPath analysis and writes them to a summary CSV file. Reported metrics include phase block length statistics (`N50`, `L50`, `NG50`,`LG50`), cumulative phase block lengths, counts of fully phased genomic windows, and counts of fully phased genes. Gene‑based metrics are reported only when a gene list is provided using `--read-phasing-gene-list`.\ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#csv-less-than-sample_id-greater-than.phasing_summary_stats.csv)\ \ CSV (`.phasing_summary_stats.csv`)\ \ Metric\ \ Description\ \ `Phasing chromosomes`\ \ A list of the chromosomes used to calculate the metrics. Only autosomes with phased reads are considered.\ \ `N50`\ \ The length of the shortest phase block where all phase blocks of at least that length account for ≥50% of the cumulative phase block length.\ \ `L50`\ \ The smallest number of phase blocks that account for 50% of the cumulative phase block length.\ \ `NG50`\ \ The length of the shortest phase block where all phase blocks of at least that length account for ≥50% of the cumulative length of the phasing chromosome set.\ \ `LG50`\ \ The smallest number of phase blocks that account for 50% of the cumulative length of the phasing chromosome set.\ \ `Total phase block length for L50/N50`\ \ The cumulative length of the phase-block assembly.\ \ `Total phase block length for LG50/NG50`\ \ The cumulative length of the chromosome set.\ \ `Number of fully phased 300 kbp windows`\ \ After partitioning each chromosome into 300 kbp windows, the number of such windows that are each fully contained within a single phase block.\ \ `Number of fully phased genes`\ \ The number of genes that are each fully contained within a single phase block.\ \ `Gene list`\ \ The filename of the gene list used to calculate the number of fully phased genes\ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#structural-variant-calling)\ \ Structural Variant Calling\ \ \ -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------\ \ TruPath‑specific structural variant (SV) calling is supported only in single‑sample whole‑genome germline SV discovery mode. DRAGEN‑SV leverages proximity information indirectly through phasing information encoded in the reads, rather than using proximity links directly during SV detection.\ \ This approach provides several key advantages. Candidate regions are assembled separately by haplotype, which reduces assembly graph complexity and produces higher‑quality contigs. Features used by the machine‑learning (ML) model are also segregated by haplotype, enabling improved training and inference. As a result, heterozygous SVs can be distinguished and assigned to specific local haplotypes.\ \ ### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#leveraging-trupath-proximity-linked-features)\ \ Leveraging TruPath Proximity-Linked Features\ \ DRAGEN‑SV currently incorporates proximity information indirectly by using phasing information during candidate assembly and ML‑based filtering. For best accuracy, ML filtering should remain enabled.\ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#phased-assembly)\ \ Phased Assembly\ \ Reads collected for candidate assembly are partitioned into two haplotypes based on available phasing information. Each haplotype is assembled independently, resulting in at most one contig per haplotype. Up to two contigs per candidate are propagated through downstream stages of the pipeline.\ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#ml-processing)\ \ ML Processing\ \ When run with TruPath data, DRAGEN‑SV uses an ML model trained on TruPath‑derived features that depend on read‑level phasing, in addition to features used with standard Illumina sequencing data. Enabling ML processing is critical for achieving optimal SV calling accuracy.\ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#collapsing-deduplication-regenotyping)\ \ Collapsing, Deduplication, Regenotyping\ \ Structural variants of certain types, including insertions and deletions, may be produced from multiple phased assembly rounds. These SVs are collapsed and deduplicated when they are inferred to represent the same event before being written to the VCF output. SV type, length, genomic location, genotype scores, and haplotype of origin are used to determine equivalence.\ \ During this process, genotypes may be updated. For example, if a heterozygous SV is produced only from reads phased to the first haplotype, the genotype `GT` field is set to `1/0`. If two SVs originating from different haplotypes are collapsed into a single event, the resulting SV is re‑genotyped as `1/1`.\ \ ### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#sv-vcf-outputs)\ \ SV VCF Outputs\ \ The following VCF fields are added for TruPath\ \ INFO Fields\ \ ID\ \ Description\ \ `PHASEDASM`\ \ Haplotype of the reads used for the assembly yielding the SV (only with `--enable-proximity=true`)\ \ `ML_UPDATED`\ \ The FILTER status has changed from PASS to non-PASS or non-PASS to PASS after QUAL being recalibrated by ML\ \ FORMAT Fields\ \ ID\ \ Description\ \ `MLQS`\ \ ML recalibrated QUAL for indels\ \ FILTER Fields\ \ ID\ \ Level\ \ Description\ \ `MLFail`\ \ Record\ \ Prob(TP) is less than SV\_ML\_MIN\_PASS\_DEL\_PROB for deletions or Prob(TP) is less than SV\_ML\_MIN\_PASS\_INS\_PROB ([default values](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#default-values)\ ).\ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#multi-region-joint-detection)\ \ Multi-Region Joint Detection\ \ \ ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\ \ DRAGEN Multi-Region Joint Detection (MRJD) is a germline small variant caller for paralogous regions. When used with TruPath data, MRJD produces haplotype‑resolved variant calls by leveraging proximity linking information enabled by TruPath. This approach does not rely on known population haplotypes.\ \ With TruPath data, MRJD currently supports nine sets of paralogous regions encompassing 15 clinically relevant genes. Table 1 lists the hg38 genomic coordinates covered by MRJD. MRJD is compatible only with the **hg38** reference genome.\ \ Chromosome\ \ Start\ \ End\ \ Region name\ \ Paralog set name\ \ Paralog type\ \ chr1\ \ 196786972\ \ 196827189\ \ CFHR3-CFHR1\ \ CFHR3-CFHR1-CFHR4-CFHR2\ \ Non-tandem\ \ chr1\ \ 196911497\ \ 196951222\ \ CFHR4-CFHR2\ \ CFHR3-CFHR1-CFHR4-CFHR2\ \ Non-tandem\ \ chr5\ \ 70924941\ \ 70966375\ \ SMN1\ \ SMN1-SMN2\ \ Non-tandem\ \ chr5\ \ 70049523\ \ 70090528\ \ SMN2\ \ SMN1-SMN2\ \ Non-tandem\ \ chr6\ \ 32037415\ \ 32045473\ \ CYP21A2-TNXB\ \ CYP21A2\ \ Tandem\ \ chr6\ \ 32004679\ \ 32012619\ \ CYP21A1P-TNXA\ \ CYP21A2\ \ Tandem\ \ chr7\ \ 5969485\ \ 5987844\ \ PMS2\ \ PMS2-PMS2CL\ \ Non-tandem\ \ chr7\ \ 6736851\ \ 6755308\ \ PMS2CL\ \ PMS2-PMS2CL\ \ Non-tandem\ \ chr7\ \ 74771000\ \ 74791999\ \ NCF1\ \ NCF1-NCF1B-NCF1C\ \ Non-tandem\ \ chr7\ \ 73217606\ \ 73238630\ \ NCF1B\ \ NCF1-NCF1B-NCF1C\ \ Non-tandem\ \ chr7\ \ 75153934\ \ 75174978\ \ NCF1C\ \ NCF1-NCF1B-NCF1C\ \ Non-tandem\ \ chr8\ \ 142873164\ \ 142879856\ \ CYP11B1\ \ CYP11B1-CYP11B2\ \ Tandem\ \ chr8\ \ 142910764\ \ 142917883\ \ CYP11B2\ \ CYP11B1-CYP11B2\ \ Tandem\ \ chr15\ \ 43599563\ \ 43618800\ \ STRC\ \ STRC-STRCP1\ \ Tandem\ \ chr15\ \ 43699418\ \ 43718260\ \ STRCP1\ \ STRC-STRCP1\ \ Tandem\ \ chr22\ \ 18159724\ \ 18174315\ \ USP18\ \ USP18-USP41P\ \ Non-tandem\ \ chr22\ \ 20362649\ \ 20377695\ \ USP41P\ \ USP18-USP41P\ \ Non-tandem\ \ chr22\ \ 42123192\ \ 42132193\ \ CYP2D6\ \ CYP2D6-CYP2D7\ \ Tandem\ \ chr22\ \ 42135344\ \ 42145873\ \ CYP2D7\ \ CYP2D6-CYP2D7\ \ Tandem\ \ Table 1. Paralogous regions covered by MRJD.\ \ ### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#method)\ \ Method\ \ MRJD begins by collecting all primary alignments within the paralogous regions of interest, regardless of mapping quality. For each paralogous region set (for example, _SMN1–SMN2_), MRJD estimates the total copy number by leveraging read depth across the regions of interest and a set of pre‑selected stable regions elsewhere in the genome.\ \ Using the estimated total copy number, read sequences, and proximity linking information, MRJD constructs the corresponding number of copies for each paralogous region set. For non‑tandem paralogous regions, proximity information is used to assign each constructed copy to the genomic region from which it most likely originated (for example, _PMS2_ versus _PMS2CL_). For tandem paralogous regions, proximity information is instead used to assign each copy to the maternal or paternal haplotype.\ \ Finally, MRJD calls small variants based on the constructed copies and reports variant calls together with their assigned genomic regions or haplotypes.\ \ The figure below provides an overview of the MRJD Workflow using TruPath data.\ \ ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3156241411-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FWueVEHA9PEBUPz0J4TCJ%252Fuploads%252Fgit-blob-659ae9976e6cad4d7b6f8cec18828358a2a1de41%252Fmrjd_constellation_workflow.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=d5592536&sv=2)\ \ ### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#outputs-1)\ \ Outputs\ \ Upon analysis completion, DRAGEN produces the following MRJD output files in the directory specified by `--output-directory`, using the prefix defined by `--output-file-prefix`:\ \ * `.mrjd.hard-filtered.vcf.gz`\ \ * VCF file containing small variants called by MRJD in paralogous regions.\ \ \ * `.mrjd.json`\ \ * JSON file containing MRJD results, including copy number estimates, region or haplotype assignments for each copy, and run status for each paralogous region.\ \ \ * `.mrjd.phased.bam`\ \ * BAM file containing phased read alignments within paralogous regions.\ \ \ * `mrjd_supporting_files/`\ \ * A directory containing additional files that support MRJD visualization, including:\ \ * `.mrjd..vcf.gz`\ \ * Multi‑column VCF file containing MRJD variant calls for each paralogous region (one column per copy). One file is generated for each paralogous region set.\ \ \ * `.mrjd.reference_region_alignments.sam`\ \ * SAM file containing reference region alignments used by MRJD.\ \ \ \ \ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#mrjd-vcf-output)\ \ MRJD VCF Output\ \ The MRJD caller generates a gzip‑compressed VCFv4.2 file, `.mrjd.hard-filtered.vcf.gz`, containing small variants derived from the inferred genotypes.\ \ For a given set of paralogous regions, all copies are reported under each region. Each copy is annotated with its assigned genomic region or haplotype in the FORMAT fields, depending on the paralog structure.\ \ For non‑tandem paralogous regions, the `REGION_PLACEMENT` field in the `FORMAT` column indicates the genomic region assignment for each copy, following the order of entries in the genotype field. Values indicate assignment to the current region, assignment to an alternate region, or an unplaced copy.\ \ #CHROM\ \ POS\ \ ID\ \ REF\ \ ALT\ \ QUAL\ \ FILTER\ \ INFO\ \ FORMAT\ \ \ \ chr5\ \ 70052190\ \ .\ \ C\ \ CA\ \ 500\ \ .\ \ regionGroupName=SMN1-SMN2;REF\_DIFF\_SITE\ \ GT:REGION\_PLACEMENT:RPQL:PQ:JAD:JAF:JDP:PS\ \ 1|0|0|0:A,A,I,I:.:500:90,30:0.250:120:70052190\ \ chr5\ \ 70052613\ \ .\ \ T\ \ C\ \ 500\ \ .\ \ regionGroupName=SMN1-SMN2\ \ GT:REGION\_PLACEMENT:RPQL:PQ:JAD:JAF:JDP:PS\ \ 1|0|0|0:A,A,I,I:.:500:86,35:0.289:121:70052190\ \ chr5\ \ 70052881\ \ .\ \ C\ \ CAAAAA\ \ 500\ \ .\ \ regionGroupName=SMN1-SMN2;REF\_DIFF\_SITE\ \ GT:REGION\_PLACEMENT:RPQL:PQ:JAD:JAF:JDP:PS\ \ 1|0|0|0:A,A,I,I:.:500:93,28:0.231:121:70052190\ \ chr5\ \ 70053733\ \ .\ \ TC\ \ T\ \ 500\ \ .\ \ regionGroupName=SMN1-SMN2\ \ GT:REGION\_PLACEMENT:RPQL:PQ:JAD:JAF:JDP:PS\ \ 0|1|0|0:A,A,I,I:.:500:85,32:0.274:117:70052190\ \ chr5\ \ 70053985\ \ .\ \ CT\ \ C\ \ 500\ \ .\ \ regionGroupName=SMN1-SMN2\ \ GT:REGION\_PLACEMENT:RPQL:PQ:JAD:JAF:JDP:PS\ \ 0|1|0|1:A,A,I,I:.:500:67,65:0.492:132:70052190\ \ chr5\ \ 70054456\ \ .\ \ TA\ \ T\ \ 500\ \ .\ \ regionGroupName=SMN1-SMN2\ \ GT:REGION\_PLACEMENT:RPQL:PQ:JAD:JAF:JDP:PS\ \ 0|1|1|1:A,A,I,I:.:500:22,105:0.827:127:70052190\ \ For tandem paralogous regions, the `PSL` field in the `FORMAT` column indicates haplotype assignment for each copy, again following the order of entries in the genotype field. `hap1` and `hap2` correspond to assignment to the first and second haplotypes, respectively. Because tandem copies cannot be assigned to specific genomic regions, the `REGION_PLACEMENT` field is not applicable and is populated with `U` (unplaced) for all copies.\ \ #CHROM\ \ POS\ \ ID\ \ REF\ \ ALT\ \ QUAL\ \ FILTER\ \ INFO\ \ FORMAT\ \ \ \ chr6\ \ 32004754\ \ .\ \ T\ \ C\ \ 63.01\ \ .\ \ regionGroupName=CYP21A2;REF\_DIFF\_SITE\ \ GT:PSL:REGION\_PLACEMENT:AGQL:PQ:JAD:JAF:JDP:PS\ \ 1|0|1|0:copy1\_hap1,copy2\_hap1,copy3\_hap2,copy4\_hap2:U,U,U,U:0.78:1:57,54:0.486:111:32004754\ \ chr6\ \ 32004791\ \ .\ \ G\ \ A\ \ 63.01\ \ .\ \ regionGroupName=CYP21A2;REF\_DIFF\_SITE\ \ GT:PSL:REGION\_PLACEMENT:AGQL:PQ:JAD:JAF:JDP:PS\ \ 1|0|1|0:copy1\_hap1,copy2\_hap1,copy3\_hap2,copy4\_hap2:U,U,U,U:0.78:1:62,56:0.475:118:32004754\ \ chr6\ \ 32004857\ \ .\ \ C\ \ T\ \ 63.01\ \ .\ \ regionGroupName=CYP21A2;REF\_DIFF\_SITE\ \ GT:PSL:REGION\_PLACEMENT:AGQL:PQ:JAD:JAF:JDP:PS\ \ 1|0|1|0:copy1\_hap1,copy2\_hap1,copy3\_hap2,copy4\_hap2:U,U,U,U:0.78:1:51,53:0.510:104:32004754\ \ chr6\ \ 32004862\ \ .\ \ C\ \ T\ \ 63.01\ \ .\ \ regionGroupName=CYP21A2;REF\_DIFF\_SITE\ \ GT:PSL:REGION\_PLACEMENT:AGQL:PQ:JAD:JAF:JDP:PS\ \ 1|0|1|0:copy1\_hap1,copy2\_hap1,copy3\_hap2,copy4\_hap2:U,U,U,U:0.78:1:48,55:0.534:103:32004754\ \ chr6\ \ 32004868\ \ .\ \ G\ \ A\ \ 63.01\ \ .\ \ regionGroupName=CYP21A2;REF\_DIFF\_SITE\ \ GT:PSL:REGION\_PLACEMENT:AGQL:PQ:JAD:JAF:JDP:PS\ \ 1|0|1|0:copy1\_hap1,copy2\_hap1,copy3\_hap2,copy4\_hap2:U,U,U,U:0.78:1:49,55:0.529:104:32004754\ \ chr6\ \ 32005002\ \ .\ \ G\ \ A\ \ 63.01\ \ .\ \ regionGroupName=CYP21A2\ \ GT:PSL:REGION\_PLACEMENT:AGQL:PQ:JAD:JAF:JDP:PS\ \ 1|0|0|0:copy1\_hap1,copy2\_hap1,copy3\_hap2,copy4\_hap2:U,U,U,U:0.78:1:102,30:0.227:132:32004754\ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#mrjd-json-output)\ \ MRJD JSON Output\ \ The MRJD caller generates a `.mrjd.json` file in the output directory. This JSON‑formatted file contains detailed information for each paralogous region analyzed, including total copy number estimates, genomic region assignment for each copy, and haplotype assignment where applicable.\ \ For each paralogous region, the total copy number is reported under `jointCopyNumber`. The `mrjdRunStatus` field indicates whether MRJD completed successfully for the region, with `Success` indicating a successful run and `Aborted` indicating a failure.\ \ For non‑tandem paralogous regions, the JSON output includes copy‑to‑region assignments. For each copy reported in the corresponding VCF file (following the order of entries in the genotype field), the `regionPlacement` field indicates which genomic region the copy is assigned to.\ \ For tandem paralogous regions, the JSON output reports haplotype assignments rather than genomic region placement. For each copy reported in the VCF file, the `locusStructure` field indicates the haplotype to which the copy is assigned. Because tandem copies cannot be uniquely mapped to specific genomic locations, all copies are listed as `unplaced` under `regionPlacement`. Example JSON output shown here illustrate these differences for non-tandem and tandem paralogous regions.\ \ Below is an example of the JSON output for a non-tandem paralogous region:\ \ Below is an example of the JSON output for a tandem paralogous region:\ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#mrjd-phased-bam-output)\ \ MRJD Phased BAM Output\ \ The MRJD caller generates a phased alignment file, `.mrjd.phased.bam`, in the output directory. This file contains phased read alignments within paralogous regions.\ \ As with the MRJD VCF output, all copies for a given set of paralogous regions are reported under each corresponding region. The phased BAM file enables inspection of read‑to‑copy assignments and phasing relationships within paralogous loci.\ \ The following tags are added to the BAM records in the phased BAM file:\ \ * `HP` - Copy label assigned to the read. For non-tandem paralogs, copy labels correspond to genomic regions (for example, `copy1_SMN1`, `copy2_SMN2`). For tandem paralogs, copy labels correspond to haplotypes (for example, `copy1_hap1`, `copy2_hap1`).\ \ * `PC` - Phred-scaled confidence score for the read-to-copy assignment.\ \ * `PS` - Phasing set identifier.\ \ * `BX` - Template identifier based on proximity linking information. Fragments with the same `BX` tag are likely to originate from the same original DNA molecule.\ \ \ The output format may be BAM, CRAM, or SAM, depending on the value specified for the `--output-format` option in the DRAGEN run.\ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#mrjd-supporting-files)\ \ MRJD Supporting Files\ \ The MRJD caller generates an `mrjd_supporting_files/` directory in the output directory. This directory contains files that support MRJD variant interpretation and visualization.\ \ The following files are produced:\ \ * `.mrjd..vcf.gz`\ \ * A multi‑column VCF file containing small variants called by MRJD for each paralogous region. Each copy is represented as a separate column. This file is suitable for visualizing haplotype‑resolved variants in genome browsers, such as IGV, that support multi‑column VCF format.\ \ \ * `.mrjd.reference_region_alignments.sam`\ \ * A SAM file containing reference region alignments used by MRJD. This file provides context for reference sequence differences between paralogous regions and can aid in interpreting variant calls, including the identification of gene conversion events.\ \ \ \ ### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#visualize-mrjd-results-in-igv)\ \ Visualize MRJD Results in IGV\ \ MRJD results can be inspected in IGV by loading the multi‑column VCF file, the phased BAM file, and the reference region alignments SAM file generated by the pipeline:\ \ * `mrjd_supporting_files/.mrjd.SMN1-SMN2.vcf.gz`\ \ * `.mrjd.phased.bam`\ \ * `.mrjd.reference_region_alignments.sam`\ \ \ In the multi‑column VCF file, all _SMN1_ and _SMN2_ copies are reported under the _SMN1_ region and are also listed under the _SMN2_ region. Copy‑to‑region assignments are indicated in the sample column. In the example shown below, copies 1, 2, and 3 are assigned to the _SMN1_ region, while copy 4 is assigned to the _SMN2_ region.\ \ The phased BAM file displays reads assigned to each copy. In IGV, this can be visualized by loading the BAM file and grouping alignments by phase.\ \ The reference region alignments SAM file highlights sequence differences between the _SMN1_ and _SMN2_ reference regions, providing context for interpreting copy‑specific variant assignments.\ \ ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3156241411-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FWueVEHA9PEBUPz0J4TCJ%252Fuploads%252Fgit-blob-2d12effc1d0d1aab860bdeb2ff594cec4ec8eaa4%252Fmrjd_constellation_igv_example.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=26db8634&sv=2)\ \ ### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#visualize-mrjd-results-in-dragen-reports)\ \ Visualize MRJD Results in DRAGEN Reports\ \ MRJD results are integrated into DRAGEN Reports. For sample‑level reports, MRJD results are available under the **Paralogs** tab.\ \ The **Paralog Sets** table provides an overview of each paralogous region analyzed, including the estimated total copy number. Selecting a region opens the **Paralogous regions** view, which displays haplotype‑resolved variant calls within each paralogous region.\ \ The example shown below illustrates MRJD phased variant calls for **PMS2–PMS2CL**. In this visualization, dark orange indicates the alternative allele at a reference difference site between paralogous regions, light orange indicates the reference allele at a reference difference site, and gray indicates a non‑reference difference site variant.\ \ ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3156241411-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FWueVEHA9PEBUPz0J4TCJ%252Fuploads%252Fgit-blob-05cdbb8bfa0e06da6d9d6183985a4208143891dc%252Fmrjd_dragen_reports_example.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=ca3778b9&sv=2)\ \ MRJD DRAGEN reports Example\ \ ### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#notes)\ \ Notes\ \ * MRJD supports paralogous region calling only when the estimated total copy number is less than eight. Regions with higher copy numbers are skipped, and no variants are called; however, total copy number estimates are still reported in the JSON output.\ \ * MRJD supports only the hg38 reference genome.\ \ * Variant calling is supported only when the sample average linked coverage (excluding duplicates) is ≥16×.\ \ * MRJD currently supports small variant calling only.\ \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#str-calling)\ \ STR Calling\ \ \ -----------------------------------------------------------------------------------------------------------------------------------------------\ \ TruPath data improves mapping accuracy for long short tandem repeats (STRs) by leveraging proximity linking information to place repetitive read pairs, including in‑repeat reads (IRRs), at their correct genomic locations. This enables more accurate sizing of STR expansions, particularly for large repeats that exceed the fragment length.\ \ DRAGEN also uses phasing information to improve STR genotyping accuracy, which is especially important for large heterozygous expansions. When IRR recovery, proximity linking, and phasing‑aware genotyping are combined, improvements to STR calling are applied automatically when running the DRAGEN Germline pipeline.\ \ All required resource files are automatically detected for supported reference genomes.\ \ ### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#in-repeat-read-irr-recovery)\ \ In-Repeat Read (IRR) Recovery\ \ IRR recovery is supported for repeat motifs with lengths between 2 and 6 bases. Motifs outside this range are not evaluated by IRR recovery, even if they are present in the catalog.\ \ DRAGEN uses proximity information to recover in‑repeat reads (IRRs) that would otherwise remain unmapped or misaligned. This capability is particularly important for detecting large repeat expansions that exceed the fragment length. Although the mapper accounts for proximity information to improve alignment, IRRs require additional handling due to their low‑complexity sequence content.\ \ IRR recovery is enabled by default when DRAGEN is run in proximity mode. DRAGEN‑STR automatically adjusts its parameters accordingly, and disabling IRR recovery is not recommended when analyzing samples for repeat expansions.\ \ IRR recovery relies on a BED catalog that defines candidate STR regions and their associated repeat motifs. The catalog may include multiple entries for the same genomic region, allowing different motifs to be specified for a single STR locus.\ \ For example, the _RFC1_ locus can be represented in the catalog as follows:\ \ Chromosome\ \ Start\ \ End\ \ Sequence\ \ Name\ \ 4\ \ 39348424\ \ 39348479\ \ AAAAG\ \ RFC1\ \ 4\ \ 39348424\ \ 39348479\ \ AAAGG\ \ RFC1\ \ 4\ \ 39348424\ \ 39348479\ \ AAGGG\ \ RFC1\ \ 4\ \ 39348424\ \ 39348479\ \ AAGAG\ \ RFC1\ \ 4\ \ 39348424\ \ 39348479\ \ AACGG\ \ RFC1\ \ 4\ \ 39348424\ \ 39348479\ \ ACGGG\ \ RFC1\ \ 4\ \ 39348424\ \ 39348479\ \ ACAGG\ \ RFC1\ \ 4\ \ 39348424\ \ 39348479\ \ AAAGGG\ \ RFC1\ \ DRAGEN provides BED catalogs for IRR recovery that cover all the locus of the default DRAGEN-STR catalogs. The default BED catalogs are located in the `/resources/irr_recovery/` directory.\ \ When using a supported reference genome and the default catalogs, IRR recovery is enabled automatically and does not require additional command‑line arguments.\ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#custom-catalogs)\ \ Custom Catalogs\ \ DRAGEN supports custom BED catalogs for in-repeat read (IRR) recovery through the `--irr-recovery-str-bed` command‑line option. Custom catalogs must follow the same format as the default catalogs provided by DRAGEN.\ \ When a custom catalog is supplied, DRAGEN uses it in place of the default catalog for the selected reference genome. It is important to ensure that the custom catalog includes all loci of interest for repeat expansion detection. If a locus is missing from the catalog, IRR recovery is not performed for that locus, which may reduce sensitivity.\ \ DRAGEN-provided built‑in catalogs are available for download from the [DRAGEN Product Files Sitearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html)\ and can serve as a starting point for generating custom catalogs.\ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#irr-recovery-bam-tags)\ \ IRR Recovery BAM Tags\ \ Remapped IRRs are annotated in the output BAM file using the `tr` tag. The `tr` tag encodes the repeat motif and motif length in a 16‑bit packed representation:\ \ * The lower 12 bits encode the motif bases using 2‑bit encoding `[A=00,C=01,G=10,T=11]`\ \ * The upper 4 bits encode the motif length.\ \ * Bases are ordered from least significant to most significant bit.\ \ \ For example, the motif _AAGGG_ with length 5 is encoded accordingly in the packed `tr` representation.\ \ To avoid redundant motif representations, the packed form always corresponds to the shortest motif pattern and the lexicographically smallest rotation across the forward motif and its reverse complement. For example, the motif _CACA_ is represented as _AC_.\ \ The `tr` tag is applied to all IRRs recovered using proximity information. Remapped IRRs are assigned a single alignment position corresponding to the first base of the associated STR region in the reference genome and are marked as unmapped with MAPQ 0.\ \ ### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#phasing-1)\ \ Phasing\ \ When proximity mode is enabled, DRAGEN uses available phasing information to improve the accuracy of repeat expansion genotyping. Phasing helps resolve ambiguities in assigning reads to haplotypes in diploid regions, which is particularly important for accurately estimating repeat sizes in large heterozygous expansions.\ \ Output calls remain unphased and are reported using the standard VCF format for short tandem repeat (STR) variants. However, the underlying genotyping model incorporates phasing information to improve repeat size estimates.\ \ ### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#sequencing-efficiency-correction)\ \ Sequencing Efficiency Correction\ \ Some loci are affected by sequencing biases that result in uneven coverage across alleles. These biases can reduce the accuracy of repeat expansion genotyping.\ \ When proximity mode is enabled, DRAGEN applies a sequencing efficiency correction to adjust expected coverage at each locus based on empirical data. This correction improves repeat size estimates by compensating for systematic sequencing bias. To minimize confounding effects from mapping bias, sequencing efficiency correction is enabled only for TruPath samples.\ \ Sequencing efficiency correction can be applied on a per-locus basis by adding the `SequencingEfficiencyCorrection` field to the respective catalog entry. For example:\ \ Correction factors should be determined empirically based on a set of control samples with known repeat sizes through orthogonal methods. DRAGEN provides precomputed correction factors in the default catalogs that were calibrated for the following loci:\ \ * _FMR1_\ \ * _DMPK_\ \ * _FXN_\ \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#colocation-maps)\ \ Colocation Maps\ \ \ -------------------------------------------------------------------------------------------------------------------------------------------------------\ \ Colocation maps capture proximity information to characterize long‑range interactions within a sample. The output of the colocation module is a matrix of interaction counts, where each cell represents the number of observed interactions between two genomic regions.\ \ Colocation maps are typically visualized as heatmaps. The example shown illustrates a small region on chromosome 5. Darker pixels indicate a higher number of interactions between the corresponding genomic regions.\ \ ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3156241411-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FWueVEHA9PEBUPz0J4TCJ%252Fuploads%252Fgit-blob-946f6cac6c169ef73e2044924d91c8733c2c2359%252Fpretty-colo.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=5ad18250&sv=2)\ \ Example of a Colocation Plot\ \ Several common features can be observed in colocation heatmaps:\ \ * The main diagonal reflects interactions among fragments originating from the same long template molecules and landing in nearby genomic bins.\ \ * Triangular or off‑diagonal structures may indicate structural variants, such as large deletions or breakends.\ \ * Most off‑diagonal pixels are either empty (white) or represent low‑level background signal (green).\ \ \ ### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#colocation-map-generation)\ \ Colocation Map Generation\ \ Colocation map generation is a three-step process.\ \ * Collect relevant alignments\ \ * Compute the colocation matrix\ \ * Generate output files\ \ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#alignment-collection)\ \ Alignment Collection\ \ During alignment collection, DRAGEN gathers all reads eligible for analysis. Alignments are excluded if mapped to decoy contigs, fall below the mapping quality threshold, or are marked as duplicates. The remaining reads are assigned to genomic bins, with each bin representing approximately 2,000 bp of the genome.\ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#matrix-construction)\ \ Matrix Construction\ \ The colocation matrix is then constructed by evaluating spatial relationships between reads. For each read (`read1`), DRAGEN identifies nearby reads (`read2`) and increments the matrix entry corresponding to their respective bins. A read is considered nearby if it falls within a rectangular region centered on `read1`. The size of this region is determined by the proximity linkage characteristics of the sample and is selected to balance sensitivity and performance.\ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#additional-options)\ \ Additional Options\ \ Several options are available to control colocation matrix generation:\ \ * The genome is partitioned into fixed‑size bins of equal length, and alignments are assigned to bins based on their starting position. Bin size can be adjusted using the `--colocation-bin-size` option.\ \ * Alignments with specific BAM flags can be excluded using `--colocation-alignment-filter-flags`, which accepts an integer bitmask specifying flags to ignore.\ \ * A minimum mapping quality can be enforced using `--colocation-alignment-min-mapq`.\ \ \ ### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#cooler-file)\ \ Cooler File\ \ Colocation output is written as a cooler file containing a sparse representation of the colocation matrix.\ \ The file conforms to schema 3 of the [official cooler specificationarrow-up-right](https://cooler.readthedocs.io/en/latest/schema.html)\ . DRAGEN produces a single‑resolution cooler file. The colocation matrix is stored in square mode and is symmetric, with each pixel containing a single integer `count` field of type `int32`.\ \ The resulting cooler file can be processed using the cooler CLI or Python API.\ \ ### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#colocation-filter)\ \ Colocation Filter\ \ The colocation filter uses colocation map data to assess proximity support for structural variant (SV) breakends and to flag events that are not supported by proximity evidence.\ \ For each candidate breakend defined by coordinates `chrom1:pos1` and `chrom2:pos2`, the filter evaluates a localized region of the colocation map. A bounding box centered on these coordinates is applied, with a default size of 200 kb, and the values of all bins within this region are summed to quantify local interaction support.\ \ To account for variation in sequencing depth and data quality, the regional sum is normalized using the median non‑zero diagonal value of the colocation map. If the normalized value is below the configured threshold (default:1.0), the `ColocationSum` filter is applied to the breakend in the VCF output.\ \ Filter application follows paired-event semantics:\ \ * If the `ColocationSum` filter is applied to one breakend of a paired SV event, it is also applied to the corresponding mate breakend record.\ \ \ ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3156241411-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FWueVEHA9PEBUPz0J4TCJ%252Fuploads%252Fgit-blob-d648db5f1548b498746013a362a5d355417acf3d%252Fcolocation_artifact.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=8ff4c222&sv=2)\ \ DRAGEN-SV BND Call Reduction using Colocation Filtering\ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#running-dragen-sv-with-colocation-filter)\ \ Running DRAGEN SV with Colocation Filter\ \ Colocation filtering is enabled by default if `enable-colocation` and `enable-sv` are both set to `true`. To disable the filter manually, set `--sv-enable-colocation-filter` to `false` when starting the DRAGEN analysis with TruPath enabled.\ \ Additional Options:\ \ * `sv-colocation-filter-normalize-by-median`: If true, colocation filter will normalize the region sum by the median diagonal value of the colocation matrix (default: true)\ \ * `sv-colocation-filter-threshold`: Minimum (normalized) sum of region in colocation matrix to pass filter (default: 1.0)\ \ * `sv-colocation-filter-region-width`: Width (in bp) of square region in colocation matrix to compute sum of (default: 200kbp)\ \ * `sv-colocation-filter-min-svlen`: If true, Colocation filter will not run on intra-chromosomal breakend pairs that are within this distance of each other (default: 200kbp)\ \ * `sv-colocation-filter-inter-bnd`: If true, colocation filter will be applied to inter-chromosomal breakends (default: true)\ \ * `sv-colocation-filter-intra-bnd`: If true, colocation filter will be applied to intra-chromosomal breakends (default: true)\ \ \ #### \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#output)\ \ Output\ \ The SV VCF file will have the additional headers if the colocation filter is enabled:\ \ Examples of VCF records can be seen below. The first breakend pair has the `ColocationSum` filter applied, as there was no colocation signal at all (`NORMALIZED_COLOC_SUM=0.0000`).\ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#targeted-calling-from-trupath-data)\ \ Targeted Calling from TruPath Data\ \ \ ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\ \ For WGS TruPath data, only `lpa`, `hba`, and `smn` will run when the Targeted Caller is enabled. A custom list of supported targets can be enabled via the command line.\ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#proximity-coverage-reports)\ \ Proximity Coverage Reports\ \ \ -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------\ \ When proximity mapping is enabled, DRAGEN generates a parallel set of coverage reports filtered to include only linked reads.\ \ During template reconstruction, each read‑pair fragment is assigned a link‑quality score equal to the highest‑quality link connecting it to other fragments. Only reads from fragments with link‑quality scores meeting or exceeding a specified threshold are included in proximity coverage reports.\ \ Proximity coverage reports are generated for each link‑quality threshold specified using `--proximity-min-linkq-threshold` (default: 10) and `--proximity-additional-linkq-thresholds` (default: 25; maximum of two values). These reports are available for WGS and all defined QC coverage regions.\ \ Report Name\ \ Output File\ \ Notes\ \ Proximity coverage metrics\ \ \_proximity\_linkqual\_coverage\_metrics.csv\ \ Coverage statistics for linked reads\ \ Proximity fine histogram coverage\ \ \_proximity\_linkqual\_fine\_hist.csv\ \ Detailed coverage histogram for linked reads\ \ Proximity histogram coverage\ \ \_proximity\_linkqual\_hist.csv\ \ Binned coverage histogram for linked reads\ \ Proximity overall mean coverage\ \ \_proximity\_linkqual\_overall\_mean\_cov.csv\ \ Overall mean coverage for linked reads\ \ Proximity per contig mean coverage\ \ \_proximity\_linkqual\_contig\_mean\_cov.csv\ \ Per-contig mean coverage for linked reads\ \ These reports use the same format and metrics as standard coverage reports but reflect statistics computed exclusively from linked reads meeting the specified threshold.\ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#reports)\ \ Reports\ \ \ ---------------------------------------------------------------------------------------------------------------------------------------\ \ DRAGEN‑Reports includes a TruPath‑specific manifest to generate reports for TruPath WGS analysis. The manifest file, trupath/germline\_wgs.json, is located in the /opt/dragen-reports/manifests directory. In addition to the standard QC metrics and visualizations provided in DRAGEN WGS reports, the TruPath report includes an additional `Proximity` tab highlighting metrics and visualizations specific to TruPath proximity‑enabled analysis, including:\ \ * `Model Fit` – Root mean square error indicating how well the proximity model fits the data.\ \ * `Q25 Proximity Rate` – Percentage of read pairs with at least one neighbor above Q25.\ \ * `Q25 Proximity Coverage` – Average autosomal coverage of read pairs with link quality above Q25.\ \ * `P75 Template Size` – Size of linked template molecules at the 75th percentile.\ \ * `Phase Block NG50` – Size of the smallest phasing block required to cover at least 50% of the genome. The `Proximity` tab also includes several visualizations summarizing proximity‑specific characteristics, including:\ \ \ The distribution of template genomic lengths from `.wgs_template_gdist.csv`\ \ ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3156241411-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FWueVEHA9PEBUPz0J4TCJ%252Fuploads%252Fgit-blob-9965cc9795da7f149e9c1f7d107ea1b951fa1724%252Fproximity_genomic_span.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=9afe00f5&sv=2)\ \ Proximity 1\ \ The genomic coverage of variant phasing blocks by minimum block size, from `.phase_blocks.gtf`\ \ ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3156241411-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FWueVEHA9PEBUPz0J4TCJ%252Fuploads%252Fgit-blob-7e6960737415a5ec2f38c11f1e3156364002ca48%252Fproximity_phase_blocks.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=10edd1af&sv=2)\ \ Proximity 2\ \ The distribution of templates by sub-read count from `._template_gdist.csv`\ \ ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3156241411-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FWueVEHA9PEBUPz0J4TCJ%252Fuploads%252Fgit-blob-06897294fdd297eb87752e0a73ec05c2c7b4ed49%252Fproximity_subpair_counts.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=5063aecb&sv=2)\ \ Proximity 3\ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#limitations)\ \ Limitations\ \ \ -----------------------------------------------------------------------------------------------------------------------------------------------\ \ Illumina TruPath proximity enabled analysis has the following limitations:\ \ * Illumina TruPath proximity mode is currently supported for the DRAGEN Germline pipeline. The Somatic, RNA, UMI, MRD, and Methylation pipelines are not supported.\ \ * DRAGEN downsampling is not supported. In order to maintain the proximity property of the TruPath assay, FASTQs should not be randomly downsampled.\ \ * Only human samples using hg38 have been verified.\ \ * Only TruPath data inputs from the Illumina TruPath Genome prep are supported at this time. Running `--enable-proximity=true` with non-TruPath data inputs will halt analysis.\ \ * Phasing requires the use of a pangenome reference hash table with personalization enabled. Analysis will halt with low coverage to support personalization.\ \ * For on-premises analyses, TruPath analysis requires a v4 DRAGEN server due to FPGA memory limitations. For reference, v4 servers have a server serial number which begins with the letters "AC".\ \ * MRJD requires at least 16x coverage to make calls; the caller will abort any attempt to call genes with insufficient aligned read coverage.\ \ \ [hashtag](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#trupath-genome-licensing)\ \ TruPath Genome Licensing\ \ \ -------------------------------------------------------------------------------------------------------------------------------------------------------------------------\ \ Illumina TruPath proximity‑enabled analysis can be run in the cloud or on supported on‑premises systems.\ \ * Cloud analysis is supported via Illumina Connected Analytics (ICA), BaseSpace Sequence Hub (BSSH) Run Planning with AutoLaunch, and DRAGEN FPGA Cloud BYOL on AWS EC2 f2.6xlarge instances.\ \ * Local analysis is supported on Phase 4 DRAGEN On‑Prem servers.\ \ * For DRAGEN On‑Prem servers and DRAGEN FPGA Cloud BYOL customers, the pipeline requires a Proximity license.\ \ * The Proximity license is included with the purchase of the Illumina TruPath Genome prep kit and is automatically assigned.\ \ * Due to FPGA memory constraints, the Proximity license for on‑premises use is supported only on Phase 4 servers. Phase 4 servers can be identified by a server serial number beginning with the letters “AC.”\ \ \ [PreviousDRAGEN v4.5chevron-left](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5)\ [NextIllumina TruPath Genome WGS Recipechevron-right](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/illumina-trupath-genome-wgs)\ \ Last updated 27 days ago\ \ Was this helpful?\ \ * [Summary](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#summary)\ \ * [Overview](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#overview)\ \ * [Proximity Mode Analysis in DRAGEN](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#proximity-mode-analysis-in-dragen)\ \ * [Key Benefits of TruPath Genome vs Standard Illumina SBS](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#key-benefits-of-trupath-genome-vs-standard-illumina-sbs)\ \ * [Phased, High-Quality Small Variant Calls in Clinically Relevant Gene Families](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#phased-high-quality-small-variant-calls-in-clinically-relevant-gene-families)\ \ * [Improved STR Expansion Length and Classification Accuracy](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#improved-str-expansion-length-and-classification-accuracy)\ \ * [Improved BND Filtering](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#improved-bnd-filtering)\ \ * [Proximity Linking Model](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#proximity-linking-model)\ \ * [Sample Collection](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#sample-collection)\ \ * [Proximity Analysis](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#proximity-analysis)\ \ * [Model Fitting](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#model-fitting)\ \ * [Link Probability Distribution Generation](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#link-probability-distribution-generation)\ \ * [Map/Align](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#map-align)\ \ * [Template Tagging](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#template-tagging)\ \ * [Outputs](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#outputs)\ \ * [Phasing](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#phasing)\ \ * [Phasing Model Overview](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#phasing-model-overview)\ \ * [Phasing Options](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#phasing-options)\ \ * [Output Files](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#output-files)\ \ * [Structural Variant Calling](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#structural-variant-calling)\ \ * [Leveraging TruPath Proximity-Linked Features](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#leveraging-trupath-proximity-linked-features)\ \ * [SV VCF Outputs](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#sv-vcf-outputs)\ \ * [Multi-Region Joint Detection](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#multi-region-joint-detection)\ \ * [Method](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#method)\ \ * [Outputs](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#outputs-1)\ \ * [Visualize MRJD Results in IGV](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#visualize-mrjd-results-in-igv)\ \ * [Visualize MRJD Results in DRAGEN Reports](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#visualize-mrjd-results-in-dragen-reports)\ \ * [Notes](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#notes)\ \ * [STR Calling](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#str-calling)\ \ * [In-Repeat Read (IRR) Recovery](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#in-repeat-read-irr-recovery)\ \ * [Phasing](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#phasing-1)\ \ * [Sequencing Efficiency Correction](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#sequencing-efficiency-correction)\ \ * [Colocation Maps](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#colocation-maps)\ \ * [Colocation Map Generation](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#colocation-map-generation)\ \ * [Cooler File](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#cooler-file)\ \ * [Colocation Filter](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#colocation-filter)\ \ * [Targeted Calling from TruPath Data](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#targeted-calling-from-trupath-data)\ \ * [Proximity Coverage Reports](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#proximity-coverage-reports)\ \ * [Reports](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#reports)\ \ * [Limitations](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#limitations)\ \ * [TruPath Genome Licensing](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline#trupath-genome-licensing)\ \ \ Was this helpful?\ \ Copy\ \ {\ "regionGroupName": "SMN1-SMN2",\ "region1Coord": "chr5:70924941-70965975",\ "region1Name": "SMN1",\ "region2Coord": "chr5:70049523-70090528",\ "region2Name": "SMN2",\ "jointCopyNumber": "4",\ "jointCopyNumberFloat": "3.972865",\ "regionPlacement": {\ "SMN1": [\ "copy1",\ "copy2"\ ],\ "SMN2": [\ "copy3",\ "copy4"\ ]\ },\ "mrjdRunStatus": "Success"\ }\ \ Copy\ \ {\ "regionGroupName": "CYP21A2",\ "region1Coord": "chr6:32037415-32045473",\ "region1Name": "CYP21A2-TNXB",\ "region2Coord": "chr6:32004679-32012619",\ "region2Name": "CYP21A1P-TNXA",\ "jointCopyNumber": "4",\ "jointCopyNumberFloat": "3.892923",\ "locusStructure": {\ "hap1": [\ [\ "copy1"\ ],\ [\ "copy2"\ ]\ ],\ "hap2": [\ [\ "copy3"\ ],\ [\ "copy4"\ ]\ ]\ },\ "regionPlacement": {\ "unplaced": [\ [\ "copy1",\ "copy2",\ "copy3",\ "copy4"\ ]\ ]\ },\ "mrjdRunStatus": "Success"\ }\ \ Copy\ \ {\ "LocusId": "DMPK",\ "LocusStructure": "(CAG)*",\ "ReferenceRegion": "chr4:3076600-3076625",\ "VariantType": "Repeat",\ "SequencingEfficiencyCorrection": 1.2345 # example correction factor\ }\ \ Copy\ \ ##INFO=\ ##FILTER=\ \ Copy\ \ chr1 94900000 DRAGEN:BND:12587:0:1:0:0:0:0 A A[chr2:39900000[ 280 ColocationSum SVTYPE=BND;MATEID=DRAGEN:BND:12587:0:1:0:0:0:1;BND_DEPTH=52;MATE_BND_DEPTH=54;NORMALIZED_COLOC_SUM=0.0000 GT:GQ:PL:PR:MLQS:VF:VF1:VAF1:VF2:VAF2 0/1:280:330,0,637:38,3:.:38,3:23,3:0.115385:15,3:0.166667\ chr2 39900000 DRAGEN:BND:12587:0:1:0:0:0:1 C ]chr1:94900000]C 280 ColocationSum SVTYPE=BND;MATEID=DRAGEN:BND:12587:0:1:0:0:0:0;BND_DEPTH=54;MATE_BND_DEPTH=52;NORMALIZED_COLOC_SUM=0.0000 GT:GQ:PL:PR:MLQS:VF:VF1:VAF1:VF2:VAF2 0/1:280:330,0,637:38,3:.:38,3:15,3:0.166667:23,3:0.115385\ chr3 52000000 DRAGEN:BND:65926:0:1:0:0:0:1 C C]chr3:72000000] 955 PASS SVTYPE=BND;MATEID=DRAGEN:BND:65926:0:1:0:0:0:0;BND_DEPTH=53;MATE_BND_DEPTH=54;NORMALIZED_COLOC_SUM=40.1980 GT:GQ:PL:PR:SR:SB:FS:MLQS:VF:VF1:VAF1:VF2:VAF2 0/1:715:999,0,712:29,8:38,23:21,17,1,22:44.774:.:48,31:21,19:0.475000:27,20:0.425532\ chr3 72000000 DRAGEN:BND:65926:0:1:0:0:0:0 A A]chr3:52000000] 955 PASS SVTYPE=BND;MATEID=DRAGEN:BND:65926:0:1:0:0:0:1;BND_DEPTH=54;MATE_BND_DEPTH=53;NORMALIZED_COLOC_SUM=40.1980 GT:GQ:PL:PR:SR:SB:FS:MLQS:VF:VF1:VAF1:VF2:VAF2 0/1:715:999,0,712:29,8:38,23:21,17,1,22:44.774:.:48,31:27,20:0.425532:21,19:0.475000 --- # Illumina TruPath Genome WGS Recipe | DRAGEN 4.5 (Illumina TruPath Genome) | DRAGEN A DRAGEN recipe, like this one, is a predefined set of analysis parameters and workflow settings tailored to a specific type of genomic analysis. For clarity, some default parameters are explicitly included and annotated with comments. Copy /opt/dragen/$VERSION/bin/dragen #DRAGEN install path --ref-dir $REF_DIR #path to DRAGEN pangenome hashtable --output-directory $OUTPUT --intermediate-results-dir $PATH #e.g. SSD /staging --output-file-prefix $PREFIX # Inputs --fastq-list $PATH #see 'Input Options' for FQ, BAM or CRAM --fastq-list-sample-id $STRING # Illumina TruPath Genome --enable-proximity true # Mapper --enable-map-align true #optional with BAM/CRAM input --enable-map-align-output true #optionally save the output BAM --enable-sort true #default=true --enable-duplicate-marking true #default=true # Small variant caller --enable-variant-caller true --vc-phasing-min-fragments 0 # SV --enable-sv true # CNV --enable-cnv true --cnv-enable-self-normalization true # Short tandem repeats --repeat-genotype-enable true # Multi-Region Joint Detection (MRJD) --enable-mrjd true [PreviousIllumina TruPath Genome Prepchevron-left](https://help.dragen.illumina.com/dragen-v4.5-trupath/product-guides/dragen-v4.5/dragen-trupath-pipeline) Last updated 26 days ago Was this helpful? Was this helpful? --- # DRAGEN Server | DRAGEN [hashtag](https://help.dragen.illumina.com/reference/platform-maintenance#server-specifications) Server Specifications --------------------------------------------------------------------------------------------------------------------------- Component DRAGEN V4 server CPU Dual Intel Xeon Gold 6226R 2.9Ghz. 32 Cores, 64 Threads Memory 512GB Scratch Drive 2x 7.68TB NVMe OS Drives 2x 480 GB SSD (RAID 1) FPGA Card DRAGEN Form Factor 2U Dimensions H 8.8cm (3.5in), W 43.8cm (17.2in), D 76.4cm (29.9in) Power Supply 1968W Dual, Hotswap redundant power supply [hashtag](https://help.dragen.illumina.com/reference/platform-maintenance#system-installation) System Installation ----------------------------------------------------------------------------------------------------------------------- See the [Site Prep and Installation Guidearrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/documentation/software_documentation/dragen-bio-it/200015717_03_dragen-server-v4-site-prep-and-installation-guide.pdf) for more information. [![Illumina DRAGENᵀᴹ Server v4 - Unboxing, assembly and installation](https://help.dragen.illumina.com/~gitbook/image?url=http%3A%2F%2Fimg.youtube.com%2Fvi%2FSo-kWUCBvQ0%2F0.jpg&width=300&dpr=3&quality=100&sign=8889aa3&sv=2)arrow-up-right](https://www.youtube.com/watch?v=So-kWUCBvQ0) _Illumina DRAGENᵀᴹ Server v4 - Unboxing, assembly and installation_ [hashtag](https://help.dragen.illumina.com/reference/platform-maintenance#system-updates) System Updates ------------------------------------------------------------------------------------------------------------- Any software on the system, including the kernel, can be updated. **Note**: Please pay special attention steps which backup the License files to avoid any disruptions. See [OS Upgrade Instructionsarrow-up-right](https://knowledge.illumina.com/software/on-premises-software/software-on-premises-software-reference_material-list/000007439) ### [hashtag](https://help.dragen.illumina.com/reference/platform-maintenance#os-image-and-kernel-patches) OS Image and Kernel patches Illumina strongly recommends the following guidelines: * Kernel packages should come from official Oracle8 updates only. * Using experimental kernels or compiling the kernel from source is not recommended. * Use OS Images provided by Illumina. * Keep up to date with security patches from Illumina. See [Linux-based OS Security Patchesarrow-up-right](https://support.illumina.com/support-content/os-patches.html) * Run the [System Checkarrow-up-right](https://github.com/illumina-swi/dragen-docs/blob/release/4.5-prod/user-guide/dragen-platform/getting-started.md#running-the-system-check) after system updates. [PreviousTools and Utilitieschevron-left](https://help.dragen.illumina.com/product-guides/dragen-v4.5/tools-and-utilities) [NextDRAGEN Application Managerchevron-right](https://help.dragen.illumina.com/reference/dragen-application-manager) Last updated 7 months ago Was this helpful? * [Server Specifications](https://help.dragen.illumina.com/reference/platform-maintenance#server-specifications) * [System Installation](https://help.dragen.illumina.com/reference/platform-maintenance#system-installation) * [System Updates](https://help.dragen.illumina.com/reference/platform-maintenance#system-updates) * [OS Image and Kernel patches](https://help.dragen.illumina.com/reference/platform-maintenance#os-image-and-kernel-patches) Was this helpful? --- # DRAGEN Application Manager | DRAGEN [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#overview) Overview ------------------------------------------------------------------------------------------------------- Dragen Application Manager is a framework for installing and running DRAGEN command line, and DRAGEN applications with Nextflow, in a containerized environment. [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#features) Features ------------------------------------------------------------------------------------------------------- Nextflow and DRAGEN versions exist independently. Only one version of DRAGEN need be installed on the OS, and compatible versions of DRAGEN can exist in containers. Run an application or DRAGEN CLI through Dragen Application Manager with JSON, or command line arguments. Integrated application help system. The Dragen Application Manager CLI provides JSON responses for simplifid automation. Keeps a history of past runs. [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#installation-and-maintenance) Installation and Maintenance ----------------------------------------------------------------------------------------------------------------------------------------------- ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#prerequisites) Prerequisites * Oracle Linux 8, or 9 * Docker 26 to 28 * DRAGEN 4.4 ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#system-maintenance) System Maintenance When performing system maintenance that may include updating Docker or restarting the system, it is recommended to wait until any in progress analysis is completed. The command line provides details about an in progress analysis if there is one. If there is an analysis in progress, wait until it's completed, and then stop the service, perform the maintenance, and then restart the service. Stopping and starting the service is accomplished with the following commands: Note that manually teminating the dragen-app-manager service, or any of its dependencies, while it is busy, may result in unintended outcomes. ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#uninstallation-procedure) Uninstallation Procedure An uninstaller is included, and is the recommended way to remove the software. It is recommended to remove any installed applications and resources first if desired. ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#docker-configuration) Docker Configuration Official instructions can be found here: https://docs.docker.com/engine/install/linux-postinstall/ The minimal requirement for a Docker configuration is that it is running as a service, there is a Docker group, and users that will use Dragen Application Manager will be in that group. To create the group: Add a user to that group You may need to logout and login for the group changes to take effect. #### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#changing-docker-file-locations) Changing Docker File Locations By default, Docker will place its data in /var. Depending on the number of applications and resources you plan to install, it may be preferable to change this setting to a location with more space available. You can change this default location by creating or modifying the file /etc/docker/daemon.json with the following content. Change the contents of deamon.json to include a new data-root setting. Further documentation on this setting can be found at: [https://docs.docker.com/reference/cli/dockerd/arrow-up-right](https://docs.docker.com/reference/cli/dockerd/) . ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#dragen-application-manager-configuration) Dragen Application Manager Configuration #### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#data-directories) Data Directories You have the option to change some settings in Dragen Application Manager. Upon changing these settings, the service must be restarted. The configuration file is found at /etc/dragen-app-manager/config.toml. Editing the file requires sudo. A full list of settings can be seen in the Configuration section of the health check. In most cases, the default values do not need to be changed; however, your system configuration may require this. If changing directories, it is recommended to mimic the file permissions of the default directories. The recommended process to change any settings is to: 1. Validate that Dragen Application Manager is not currently in use 2. Stop the service with "sudo stop dragen-app-manager" 3. Make changes to the configuration with "sudo vim /etc/dragen-app-manager/config.toml" 4. Move the contents of application, resources, and temp directories when appropriate 5. Start the service with "sudo start dragen-app-manager" Setting Default Permissions directory.applications /var/illumina/dragen-app-manager/applications 0755 directory.resources /var/illumina/dragen-app-manager/resources 0755 directory.temp /var/illumina/dragen-app-manager/temp 0777 These settings can be verified from the health check. #### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#output-directories) Output Directories If appropriate, consider implementing a disk quota on output directories. ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#reserving-cpu-and-memory-for-the-host-os) Reserving CPU and Memory for the Host OS To avoid an overuse of resources by a container, memory and CPU are reserved for the Host OS. By default, 4 CPUs and 6 GiB of memory are reserved. These values can be changed with the following configuration settings. This sets a hard limit on the resources an application can use inside a container based on the available memory at the time it starts. CPU is a decimal between 1 and one less than the number of logical CPU's. Memory is the amount of memory in bytes. Applications can override these settings only for the containers they create directly. [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#cli-usage) CLI Usage --------------------------------------------------------------------------------------------------------- Dragen Application Manager, as with the DRAGEN executable, is intended to handle one command at a time. This means that if one user starts an analysis, a second user will be prevented from starting an analysis at the same time. This applies to all command line operations. In most cases, if Dragen Application Manager is busy, it will pause any additional requests, and handle them when the initial operation is completed. If a Dragen Application Manager command does not succeed, it will return non-zero, and depending on the command and type of failure, may include JSON in the response, and may also send data to STDOUT and/or STDERR. A successful command will return 0, and send a JSON response to STDOUT, and may also send appropriate informational data to STDERR. Providing a JSON response allows for easy, and reliable, extraction of specific data. In the case of wanting to view a formatted response, piping the output to "jq" is the preferred option. Examples of this will be provided. ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#help) Help To start using Dragen Application Manager, the first command to become acquainted with is the "help" command. With any command, you can add the "–help" flag to get further usage information. For example: If any applications are installed, a command to show additional usage information for that application will be displayed. For example: Running the command that specifies an application provides the following: ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#install) Install This command installs applications and resources. If an application has any resource dependencies, they must be installed before the application can be installed. Installation of both applications and resources include file validation. The speed of this operation depends on the size of the files that are validated. ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#remove) Remove This command removes an application or resource. If a resource is required by an application, the application must be removed first. It is possible to force remove a resource that is a dependency for an application; however, once it is used, the resource should be immediately installed again. ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#health) Health The health check provides general information about application health, settings, and system status. The health check will return a non-zero value if it is unable to perform the health check. This is an example of the most relevant parts of the response to a health check. ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#list) List This command will provide a list of all applications and resources. This is an example response for the default list statement piped to "jq". It shows that the SimpleApp is installed, its Nextflow dependency, and a DRAGEN resource. ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#dry-run) Dry-Run The dry-run command provides a quick way to validate input before running an application. It is best used while creating the input for an application, be it command line arguments or a JSON payload. The help command for an application creates the dry-run command for you as a starting point. The input can be command line arguments, or Standard Input. If no arguments are given, and nothing is sent to standard input, you will be prompted to manually enter the payload. Ctrl-d will end the input in this situation. ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#run) Run This command accepts the same input as the dry-run command. There are two ways to run an application from the command line. The first is strictly with command line arguments, the second is with a JSON payload. Once the dry-run command has validated the input, replace "dry-run" with "run" to begin an analysis. This is an example of having run the dry-run command, setting the input and output directory, and removing arguments based on the documentation. Based on the arguments, a JSON payload representation looks like this. If you prefer to use JSON instead of command line arguments, Dragen Application Manager will have a JSON version of the input for every run. That topic will be covered later. #### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#payload.json) payload.json The payload.json can be redirected into the run command. See help for the run command for more usage details. ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#dragen-native) Dragen Native Dragen Native requires that DRAGEN 4.4 is installed on the host operating system. In addition to this, a DRAGEN IRES must also be installed. With the "--DRAGEN" command line option, the following can be used to run native DRAGEN commands. The above command line options are equivalent to this payload. ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#run-status) Run Status Without a specified ID, this command is used to determine if an application is currently running. It will return 0 if there is a current run, non-zero if there isn't. With an ID specified, this command will return details about a specific run. The result provides details about a run. Payload, Stdout, and Stderr are binary data. Binary data is encoded in base64, and can be pulled out manually with jq and piped to base64; however this can also be done with additional arguments. When, instead of using the command line, it is preferable to use a JSON payload to run an application, the payload, and the data provided by the status, can be used as a starting point. To automatically decode binary data, three flags are available: stdout, stderr, and payload. When decoding the payload, it serves as a JSON template that can be reused by changing the input and output directories. ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#run-history) Run History This provides a list of archived runs no longer in progress. You may refer to the help screen for the active default settings: The default behavior is to provide a list of archived runs, over a short period of time, with the latest at the end of the list. The duration of time as well as the sort order is configurable based on command line arguments, and all dates are based on the start time. If there are conflicting arguments, only one will be chosen. ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#stop) Stop This provides a way for a user to prematurely terminate a running analysis. It is best to always let an analysis to run to completion; however, it can be used when a run must be stopped early. This command initates a safe shutdown, and may take up to 10 minutes (or possibly longer) to complete. It is always recommended to use this to stop an analysis. [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#troubleshooting) Troubleshooting --------------------------------------------------------------------------------------------------------------------- ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#log-files) Log Files Log files are located in "/var/log/dragen-app-manager/" You may also see some logged information with journalctl or systemctl: ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#health-check-dev-log-is-missing) Health Check /dev/log is Missing This happens when the logging service on the Host OS is missing the log socket. If an error appears duing these steps, it's usually okay. ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#access-denied) Access Denied You may see Access Denied messages if your user account does not have appropriate file permissions for: * input directories * output directories * read access to the applications, and resources on the file system * full access to the temp directory * Docker socket Work with an administrator to obtain the necessary file permissions. ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#error-dial-unix-dragen-app-manager.sock-connect-no-such-file-or-directory) Error: dial unix dragen-app-manager.sock: connect: no such file or directory This may indicate a few things: * Dragen App Manager is under maintenance and is temporarily stopped * Dragen App Manager is not running for another reason * The socket file was removed In all the above cases, the remedy is to restart the service with ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#cannot-run-or-stop-an-analysis) Cannot Run or Stop An Analysis Dragen App Manager will only interact with a Docker container if it knows for certain that it was the creator. If it becomes necessary to reset everything related to running an analysis, follow this procedure: If containers cannot be removed from Docker, restart the Docker services. Start the service and check the health. ### [hashtag](https://help.dragen.illumina.com/reference/dragen-application-manager#dragen-license-not-found) DRAGEN License Not Found This can occur when running an application using DRAGEN on a machine without a license dongle, or if missing a required license key. [PreviousDRAGEN Serverchevron-left](https://help.dragen.illumina.com/reference/platform-maintenance) [NextDRAGEN Multi-Cloudchevron-right](https://help.dragen.illumina.com/reference/dragen-multi-cloud) Last updated 7 months ago Was this helpful? * [Overview](https://help.dragen.illumina.com/reference/dragen-application-manager#overview) * [Features](https://help.dragen.illumina.com/reference/dragen-application-manager#features) * [Installation and Maintenance](https://help.dragen.illumina.com/reference/dragen-application-manager#installation-and-maintenance) * [Prerequisites](https://help.dragen.illumina.com/reference/dragen-application-manager#prerequisites) * [System Maintenance](https://help.dragen.illumina.com/reference/dragen-application-manager#system-maintenance) * [Uninstallation Procedure](https://help.dragen.illumina.com/reference/dragen-application-manager#uninstallation-procedure) * [Docker Configuration](https://help.dragen.illumina.com/reference/dragen-application-manager#docker-configuration) * [Dragen Application Manager Configuration](https://help.dragen.illumina.com/reference/dragen-application-manager#dragen-application-manager-configuration) * [Reserving CPU and Memory for the Host OS](https://help.dragen.illumina.com/reference/dragen-application-manager#reserving-cpu-and-memory-for-the-host-os) * [CLI Usage](https://help.dragen.illumina.com/reference/dragen-application-manager#cli-usage) * [Help](https://help.dragen.illumina.com/reference/dragen-application-manager#help) * [Install](https://help.dragen.illumina.com/reference/dragen-application-manager#install) * [Remove](https://help.dragen.illumina.com/reference/dragen-application-manager#remove) * [Health](https://help.dragen.illumina.com/reference/dragen-application-manager#health) * [List](https://help.dragen.illumina.com/reference/dragen-application-manager#list) * [Dry-Run](https://help.dragen.illumina.com/reference/dragen-application-manager#dry-run) * [Run](https://help.dragen.illumina.com/reference/dragen-application-manager#run) * [Dragen Native](https://help.dragen.illumina.com/reference/dragen-application-manager#dragen-native) * [Run Status](https://help.dragen.illumina.com/reference/dragen-application-manager#run-status) * [Run History](https://help.dragen.illumina.com/reference/dragen-application-manager#run-history) * [Stop](https://help.dragen.illumina.com/reference/dragen-application-manager#stop) * [Troubleshooting](https://help.dragen.illumina.com/reference/dragen-application-manager#troubleshooting) * [Log Files](https://help.dragen.illumina.com/reference/dragen-application-manager#log-files) * [Health Check /dev/log is Missing](https://help.dragen.illumina.com/reference/dragen-application-manager#health-check-dev-log-is-missing) * [Access Denied](https://help.dragen.illumina.com/reference/dragen-application-manager#access-denied) * [Error: dial unix dragen-app-manager.sock: connect: no such file or directory](https://help.dragen.illumina.com/reference/dragen-application-manager#error-dial-unix-dragen-app-manager.sock-connect-no-such-file-or-directory) * [Cannot Run or Stop An Analysis](https://help.dragen.illumina.com/reference/dragen-application-manager#cannot-run-or-stop-an-analysis) * [DRAGEN License Not Found](https://help.dragen.illumina.com/reference/dragen-application-manager#dragen-license-not-found) Was this helpful? Copy dragen-app-manager run status Copy sudo systemctl stop dragen-app-manager sudo systemctl start dragen-app-manager Copy sudo uninstall_dragen_app_manager.sh Copy sudo groupadd docker Copy sudo usermod -aG docker $USER Copy sudo vim /etc/docker/daemon.json Copy { "data-root": "/staging/.docker" } Copy sudo systemctl restart dragen-app-manager Copy sudo vim /etc/dragen-app-manager/config.toml Copy [hardware] reserveCPU=4.0 reserveMemory=6442450944 Copy dragen-app-manager help Copy dragen-app-manager --help dragen-app-manager help --help dragen-app-manager run --help Copy > dragen-app-manager help How to get application help. [Application] - Provider: Illumina - Name: SimpleApp - Version: 1.0.0 Usage: dragen-app-manager help --provider Illumina --name SimpleApp --version 1.0.0 Copy > dragen-app-manager help --provider Illumina --name SimpleApp --version 1.0.0 SimpleApp version 1.0.0 provided by Illumina This requires the parameter "A" to be set with the value "B", a Flag must be explicitly set to either true or false, and a Count with a value greater than 0. There is also an example of representing a number as a string. [Environment] --environment TZ= Description: Timezone [Settings] --setting simple= Description: A simple setting that is a filename [Parameters] --param.A Description: A required parameter with an required value of B. --param.Flag Description: A flag must be explicitly set. --param.Count Description: A number. --param.NumberString Description: A number as a string [Resources] --resource opt= Description: the /opt directory Usage: dragen-app-manager dry-run --provider Illumina --name SimpleApp --version 1.0.0 --inputDirectory input --outputDirectory output --environment TZ=EST --setting simple=config.json --resource opt=/opt --param.A B --param.Flag true --param.Count 1 --param.NumberString '"1532554234"' Copy dragen-app-manager install --file ./application.iapp dragen-app-manager install --file ./resource.ires Copy dragen-app-manager remove application --provider Illumina -name SimpleApp -version 1.0.0 dragen-app-manager remove resource --provider Illumina -name SimpleResource -version 1.0.0 Copy dragen-app-manager health dragen-app-manager health | jq Copy { "HostOS": { "Release": "Oracle Linux Server release 8.10", "Healthy": true, "Partitions": [\ {\ "Mount": "/staging",\ "Size": "11 TB",\ "Used": "263 GB",\ "Available": "11 TB",\ "AvailablePercent": "97.8 %"\ },\ {\ "Mount": "/var",\ "Size": "251 GB",\ "Used": "97 GB",\ "Available": "154 GB",\ "AvailablePercent": "61.3 %"\ }\ ] }, "Dragen": { "Version": "07.031.773.4.4.2", "HashTableVersion": "10", "Healthy": true, "Status": "Okay", "FileSystem": { "Healthy": true } }, "Docker": { "Healthy": true, "Info": { "Version": "27.5.1", "Security": [\ "name=seccomp,profile=builtin"\ ] }, "Parameters": { "APIVersion": "1.47", "OSType": "linux", "Experimental": false, "BuilderVersion": "2" } }, ... } Copy dragen-app-manager list dragen-app-manager list | jq dragen-app-manager list --detailed | jq Copy { "Applications": [\ {\ "Name": "SimpleApp",\ "Version": "1.0.0",\ "Provider": "Illumina",\ "Dependencies": {\ "Resources": [\ {\ "Name": "nextflow",\ "Version": "24.10.5",\ "Provider": "Illumina"\ }\ ]\ },\ "Type": "NEXTFLOW"\ }\ ], "Resources": [\ {\ "Name": "dragen",\ "Version": "4.4.4",\ "Provider": "Illumina",\ "Type": "IMAGE"\ },\ {\ "Name": "nextflow",\ "Version": "24.10.5",\ "Provider": "Illumina",\ "Type": "IMAGE"\ }\ ] } Copy dragen-app-manager dry-run --provider Illumina --name SimpleApp --version 1.0.0 --inputDirectory input --outputDirectory output --environment TZ=EST --setting simple=config.json --resource opt=/opt --param.A B --param.Flag true --param.Count 1 --param.NumberString '"1532554234"' dragen-app-manager dry-run < payload.json cat payload.json | dragen-app-manager run Copy dragen-app-manager run --provider Illumina --name SimpleApp --version 1.0.0 --inputDirectory /staging/data/input --outputDirectory /staging/data/output --param.A B Copy { "Application": { "Name": "SimpleApp", "Version": "1.0.0", "Provider": "Illumina" }, "InputDirectory": "/staging/data/input", "OutputDirectory": "/staging/data/output", "Params": { "A": "B" } } Copy dragen-app-manager run < payload.json cat payload.json | dragen-app-manager run Copy dragen-app-manager run --version 4.4.4 --inputDirectory test/input --outputDirectory output --DRAGEN --build-hash-table true --ht-reference {{.InputDirectory}}/refdir/8/hg19_chrM.fa --output-directory {{.OutputDirectory}} Copy { "InputDirectory": "test/input", "OutputDirectory": "output", "Application": { "Version": "4.4.4" }, "CommandArguments": [\ "--build-hash-table",\ "true",\ "--ht-reference",\ "{{.InputDirectory}}/refdir/8/hg19_chrM.fa",\ "--output-directory",\ "{{.OutputDirectory}}"\ ] } Copy dragen-app-manager run status Copy run status --id 1cef740d-8fa5-4316-9f37-5b41457cccc5 | jq Copy { "Start": "2025-03-14T20:40:00.00Z", "End": "2025-03-14T20:45:00.00Z", "Status": "COMPLETED", "Input": { "Application": { "Name": "SimpleApp", "Version": "1.0.0", "Provider": "Illumina" }, "Payload": "eyJBcHBs...", "InputDirectory": "/staging/data/input", "OutputDirectory": "/staging/data/output", "UserId": "1001", "GroupId": "1001", "GroupIds": [\ "1001",\ "10",\ "3241",\ "6252",\ "988"\ ] }, "Id": "1cef740d-8fa5-4316-9f37-5b41457cccc5", "ContainerId": "9b637ec...", "Stdout": "CiBOIEUgWCB...", "Stderr": "" } Copy run status --id 1cef740d-8fa5-4316-9f37-5b41457cccc5 --stdout --stderr --payload Copy { "Application": { "Name": "SimpleApp", "Version": "1.0.0", "Provider": "Illumina" }, "InputDirectory": "input", "OutputDirectory": "output", "Params": { "A": "B" } } Copy dragen-app-manager run history --help Copy dragen-app-manager run status list --last 8h dragen-app-manager run status list --last-days 3 dragen-app-manager run status list --since 2024-12-01T00:00:00Z --until 2025-01-01T00:00:00Z dragen-app-manager run status list --descending --limit 1 dragen-app-manager run status list --last 8h | jq Copy dragen-app-manager stop Copy sudo tail -f /var/log/dragen-app-manager/dragen-app-manager.log systemctl status dragen-app-manager journalctl -u dragen-app-manager Copy sudo systemctl restart systemd-journald-dev-log.socket sudo systemctl restart systemd-journald.socket Copy sudo systemctl restart dragen-app-manager Copy sudo systemctl stop dragen-app-manager docker container stop DragenAppManager docker container rm DragenAppManager Copy sudo systemctl restart containerd.socket docker.socket Copy sudo systemctl start dragen-app-manager dragen-app-manager health | jq --- # Support | DRAGEN #### [hashtag](https://help.dragen.illumina.com/reference/technical-assistance#for-technical-assistance-contact-illumina-technical-support) For technical assistance, contact Illumina Technical Support. Website: [www.illumina.comarrow-up-right](http://www.illumina.com/) Email: [\[email protected\]envelope](https://help.dragen.illumina.com/cdn-cgi/l/email-protection#daaebfb9b2a9afaaaab5a8ae9ab3b6b6afb7b3b4bbf4b9b5b7) #### [hashtag](https://help.dragen.illumina.com/reference/technical-assistance#illumina-customer-support-telephone-numbers) Illumina Customer Support Telephone Numbers Region Toll Free Regional North America +1.800.809.4566 Australia +1.800.775.688 Austria +43 800006249 +43 19286540 Belgium +32 80077160 +32 34002973 China 400.066.5835 Denmark +45 80820183 +45 89871156 Finland +358 800918363 +358 974790110 France +33 805102193 +33 170770446 Germany +49 8001014940 +49 8938035677 Hong Kong, China 800960230 Ireland +353 1800936608 +353 016950506 Italy +39 800985513 +39 236003759 Japan 0800.111.5011 Netherlands +31 8000222493 +31 207132960 New Zealand 0800.451.650 Norway +47 800 16836 +47 21939693 Singapore +1.800.579.2745 South Korea +82 80 234 5300 Spain +34 911899417 +34 800300143 Sweden +46 850619671 +46 200883979 Switzerland +41 565800000 +41 800200442 Taiwan, China 00806651752 United Kingdom +44 8000126019 +44 2073057197 Other countries +44.1799.534000 **Safety data sheets (SDSs)**\---Available on the Illumina website at [support.illumina.com/sds.htmlarrow-up-right](http://support.illumina.com/sds.html) . **Product documentation**\---Available for download from [support.illumina.comarrow-up-right](http://support.illumina.com/) . [PreviousDRAGEN Cloud FPGA BYOLchevron-left](https://help.dragen.illumina.com/reference/licensing/cloud_licensing) [NextResource Fileschevron-right](https://help.dragen.illumina.com/reference/resource-files) Last updated 20 days ago Was this helpful? Was this helpful? --- # Resource Files | DRAGEN [hashtag](https://help.dragen.illumina.com/reference/resource-files#overview) Overview ------------------------------------------------------------------------------------------- The following sub-pages contain more information about resource files used by the software. [PreviousSupportchevron-left](https://help.dragen.illumina.com/reference/technical-assistance) [NextNoise Baselineschevron-right](https://help.dragen.illumina.com/reference/resource-files/prebuilt-baseline-files) Last updated 7 months ago Was this helpful? Was this helpful? --- # DRAGEN Pricing and Licensing | DRAGEN [hashtag](https://help.dragen.illumina.com/reference/licensing#overview) Overview -------------------------------------------------------------------------------------- DRAGEN uses a usage-based licensing model across most pipelines and features. Usage is typically measured in gigabases (Gbases) of input from FASTQ or BAM files and is calculated per execution of DRAGEN. If you are interested in learning more about DRAGEN pricing or are experiencing any issues - please [contact usarrow-up-right](https://www.illumina.com/company/contact-us.html) . circle-exclamation Running multiple pipelines in a single DRAGEN run consumes quota once, which can reduce total usage compared to executing each pipeline separately. ### [hashtag](https://help.dragen.illumina.com/reference/licensing#pipelines-not-metered) Pipelines not metered The following DRAGEN pipelines do not consume usage quota: * Germline / Somatic - CNV Only or SV Only * Germline - Joint Genotyping * BCL Convert [hashtag](https://help.dragen.illumina.com/reference/licensing#platform-specific-pricing-and-licensing) Platform specific Pricing and Licensing ---------------------------------------------------------------------------------------------------------------------------------------------------- The licensing experience differs depending on where DRAGEN is being used. Use the sections below to find the model that applies to your environment. ### [hashtag](https://help.dragen.illumina.com/reference/licensing#dragen-on-premises-server) DRAGEN On-Premises Server DRAGEN servers use prepaid annual licenses that are assigned to a specific DRAGEN server. As DRAGEN runs, usage is metered against the server's licensed quota. For more information on DRAGEN Server On-Premises Licensing, refer to the [DRAGEN OnPrem Server License Reference Section](https://help.dragen.illumina.com/reference/licensing/onprem_licensing) . ### [hashtag](https://help.dragen.illumina.com/reference/licensing#dragen-cloud-fpga-byol-on-aws-or-azure) DRAGEN Cloud FPGA (BYOL) on AWS or Azure Running DRAGEN in your own cloud environment (AWS or Azure) requires a prepaid annual license which are assigned to credentials provided to you. As DRAGEN runs, usage is metered against your credentials licensed quota. For more information on DRAGEN Cloud Licensing, refer to the [DRAGEN Cloud FPGA (BYOL) Reference Section](https://help.dragen.illumina.com/reference/licensing/cloud_licensing) . ### [hashtag](https://help.dragen.illumina.com/reference/licensing#illumina-connected-analytics-ica-and-basespace-sequene-hub-bssh) Illumina Connected Analytics (ICA) and BaseSpace Sequene Hub (BSSH) When running DRAGEN within ICA or BSSH, billing is handled automatically in those platforms. As DRAGEN runs, usage is metered as described on the [ICA Pricing pagearrow-up-right](https://help.ica.illumina.com/reference/r-pricing) . ### [hashtag](https://help.dragen.illumina.com/reference/licensing#dragen-onboard-novaseqx-series-miseq-i100-series-and-nextseq-1000-2000) DRAGEN onboard NovaSeqX series, MiSeq i100 Series, and NextSeq 1000/2000 DRAGEN usage on supported sequencing instruments is included with the instrument and is not metered. Learn more at: * [NovaSeqX Series software and informatics pagearrow-up-right](https://www.illumina.com/systems/sequencing-platforms/novaseq-x-plus/products-services/software.html) * [MiSeqi100 software and informatics pagearrow-up-right](https://www.illumina.com/systems/sequencing-platforms/miseq-i100/products-services/software.html) * [NextSeq 1000/2000 services pagearrow-up-right](https://www.illumina.com/systems/sequencing-platforms/nextseq-1000-2000/products-services.html) [hashtag](https://help.dragen.illumina.com/reference/licensing#frequently-asked-questions) Frequently Asked Questions -------------------------------------------------------------------------------------------------------------------------- ### [hashtag](https://help.dragen.illumina.com/reference/licensing#q-how-do-i-see-how-many-gbases-a-run-consumed) Q: How do I see how many Gbases a run consumed? A: Gigabase consumption per run can be found in any of the following three places; * .metadata.licenseInfo section of the \*.metrics.json file in that runs output directory * \*\_usage.txt file in that runs output directory * License report in that runs stdout. [PreviousAzure Batch Run Modeschevron-left](https://help.dragen.illumina.com/reference/dragen-multi-cloud/azure-batch/run-azure-modes) [NextDRAGEN OnPrem Server Licensingchevron-right](https://help.dragen.illumina.com/reference/licensing/onprem_licensing) Last updated 20 days ago Was this helpful? * [Overview](https://help.dragen.illumina.com/reference/licensing#overview) * [Pipelines not metered](https://help.dragen.illumina.com/reference/licensing#pipelines-not-metered) * [Platform specific Pricing and Licensing](https://help.dragen.illumina.com/reference/licensing#platform-specific-pricing-and-licensing) * [DRAGEN On-Premises Server](https://help.dragen.illumina.com/reference/licensing#dragen-on-premises-server) * [DRAGEN Cloud FPGA (BYOL) on AWS or Azure](https://help.dragen.illumina.com/reference/licensing#dragen-cloud-fpga-byol-on-aws-or-azure) * [Illumina Connected Analytics (ICA) and BaseSpace Sequene Hub (BSSH)](https://help.dragen.illumina.com/reference/licensing#illumina-connected-analytics-ica-and-basespace-sequene-hub-bssh) * [DRAGEN onboard NovaSeqX series, MiSeq i100 Series, and NextSeq 1000/2000](https://help.dragen.illumina.com/reference/licensing#dragen-onboard-novaseqx-series-miseq-i100-series-and-nextseq-1000-2000) * [Frequently Asked Questions](https://help.dragen.illumina.com/reference/licensing#frequently-asked-questions) * [Q: How do I see how many Gbases a run consumed?](https://help.dragen.illumina.com/reference/licensing#q-how-do-i-see-how-many-gbases-a-run-consumed) Was this helpful? --- # Troubleshooting | DRAGEN If the DRAGEN system does not seem to be responding, do the following: 1. To determine if the DRAGEN system is hanging, follow the instructions in [How to Determine if the System is Hanging](https://help.dragen.illumina.com/reference/troubleshooting#how-to-determine-if-the-system-is-hanging) . 2. Collect diagnostic information after a hang, or a crash, as described in [Sending Diagnostic Information to Illumina Support](https://help.dragen.illumina.com/reference/troubleshooting#sending-diagnostic-information-to-illumina-support) . 3. After all information has been collected, reset your system. if needed, as described in [Resetting Your System after a Crash or Hang](https://help.dragen.illumina.com/reference/troubleshooting#resetting-your-system-after-a-crash-or-hang) . [hashtag](https://help.dragen.illumina.com/reference/troubleshooting#how-to-determine-if-the-system-is-hanging) How to Determine if the System is Hanging -------------------------------------------------------------------------------------------------------------------------------------------------------------- The DRAGEN system has a watchdog to monitor the system for hangs. If a run seems to be taking longer than it should, the watchdog may not be detecting the hang. Here are some things to try: * Run the top command to find the active DRAGEN process. If your run is healthy, you should expect to see it consuming over 100% of the CPU. If it is consuming 100% or less, then your system may be hanging. * Run the du -s command in the directory of the output BAM/SAM file. During a normal run, this directory should be growing with either intermediate output data (when sort is enabled) or BAM/SAM data. [hashtag](https://help.dragen.illumina.com/reference/troubleshooting#sending-diagnostic-information-to-illumina-support) Sending Diagnostic Information to Illumina Support -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Illumina would like your feedback on your DRAGEN system, including any reports of system malfunction. In the event of a crash, hang, or watchdog fault, run the sos report command to collect diagnostic and configuration information, as follows: `sudo sos report --batch --tmp-dir /staging/tmp` This command takes several minutes to execute and reports the location where it has saved the diagnostic information in /staging/tmp. Please include the report when you submit a support ticket for Illumina Technical Support. [hashtag](https://help.dragen.illumina.com/reference/troubleshooting#resetting-your-system-after-a-crash-or-hang) Resetting Your System after a Crash or Hang ------------------------------------------------------------------------------------------------------------------------------------------------------------------ If the DRAGEN system crashes or hangs, the dragen\_reset utility must be run to reinitialize the hardware and software. This utility is automatically executed by the host software any time it detects an unexpected condition. In this case, the host software shows the following message: `Running dragen_reset to reset DRAGEN Bio-IT processor and software` If the software is hanging, please collect diagnostic information as described in subsection \[Sending Diagnostic Information to Illumina Support\]{.underline} and then execute dragen\_reset manually, as follows: `/bin/dragen_reset` Any execution of dragen\_reset requires the reference genome to be reloaded to the DRAGEN board. The host software automatically reloads the reference on the next execution. [PreviousF2 Validationchevron-left](https://help.dragen.illumina.com/reference/f2-validation) [NextCiting DRAGEN softwarechevron-right](https://help.dragen.illumina.com/reference/citing-dragen) Last updated 20 days ago Was this helpful? * [How to Determine if the System is Hanging](https://help.dragen.illumina.com/reference/troubleshooting#how-to-determine-if-the-system-is-hanging) * [Sending Diagnostic Information to Illumina Support](https://help.dragen.illumina.com/reference/troubleshooting#sending-diagnostic-information-to-illumina-support) * [Resetting Your System after a Crash or Hang](https://help.dragen.illumina.com/reference/troubleshooting#resetting-your-system-after-a-crash-or-hang) Was this helpful? --- # Supplementary Information | DRAGEN Resource Description [Product Pagearrow-up-right](https://www.illumina.com/products/by-type/informatics-products/dragen-secondary-analysis.html) Product Overview and sales information [Software Download Sitearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/downloads.html) DRAGEN and DRAGEN On-Prem App Downloads and supporting documentation [DRAGEN Product Files Sitearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) DRAGEN Reference and Resource Files [Support Site Pagearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform.html) Documentation, FAQs, Training [Research Articlesarrow-up-right](https://www.illumina.com/science/genomics-research/articles.html) Genomics articles highlighting breakthroughs and advances in bioinformatics and clinical research from Illumina scientists and thought leaders [Resources for Population Genomicsarrow-up-right](https://developer.illumina.com/dragen/dragen-popgen) An informational page to get the latest DRAGEN pipeline versions and command lines used in large population genomics programs. [DRAGEN v4.4 Webinar Slidesarrow-up-right](https://www.illumina.com/content/dam/illumina-marketing/emailers/2025/DRAGEN_v4.4_Webinar_Presentation.pdf.) An overview of changes and new features released with DRAGEN Secondary Analysis v4.4. [PreviousNoise Baselineschevron-left](https://help.dragen.illumina.com/reference/resource-files/prebuilt-baseline-files) [NextDRAGEN Product Obsolescence Noticeschevron-right](https://help.dragen.illumina.com/reference/eol-transition) Last updated 20 days ago Was this helpful? Was this helpful? --- # F2 Validation | DRAGEN [hashtag](https://help.dragen.illumina.com/reference/f2-validation#background) Background ---------------------------------------------------------------------------------------------- DRAGEN supports various FPGA types for accelerated analyses of on-premises, on-instrument and cloud pipelines. The software automatically detects the platform and selects the appropriate method to interact with the FPGA type. Subsequently, it loads the appropriate FPGA images and configures the system for the analysis. The FPGA images for different platforms are unique, but they are built from the same logic, and they all produce the same results across platforms - per design. The software releases are tested and validated across all supported platforms, and concordance between platforms is validated. [hashtag](https://help.dragen.illumina.com/reference/f2-validation#aws-f2-instance-type-support) AWS F2 Instance Type Support ---------------------------------------------------------------------------------------------------------------------------------- AWS has launched a new FPGA instance type (F2) and has announced obsolescence of the existing FPGA instance type (F1) used by DRAGEN on BSSH and ICA platforms. To support customers on existing versions of the DRAGEN software which do not have built-in support for the new FPGA type on the F2 instance, Illumina is adding F2 instance support to older DRAGEN versions. As a result, Illumina is publishing updated BSSH and ICA apps based on the same versions of workflows, pipelines and bioinformatics features - but with added F2 instance type support. The updated DRAGEN versions are designed to produce the exact same analysis outputs on F2 instances as on F1 instances. This includes exact same analysis outputs of the mapper (BAM/CRAM) all variant callers and QC metrics. The updated BSSH and ICA apps are exact replicas of the existing F1 versions and only add F2 instance type support with an associated update to the DRAGEN software. The updated BSSH and ICA apps produce the exact same outputs as the F1 versions. [hashtag](https://help.dragen.illumina.com/reference/f2-validation#development-methods) Development Methods ---------------------------------------------------------------------------------------------------------------- ### [hashtag](https://help.dragen.illumina.com/reference/f2-validation#dragen-r-and-d) DRAGEN R&D * Support for the AWS F2 instance type is developed in partnership with AWS. * Extensive test campaigns are executed to ensure that stability, robustness and exact concordance are achieved. * Validation of the software on AWS F2 is performed according to existing software life cycle and QA processes at Illumina ### [hashtag](https://help.dragen.illumina.com/reference/f2-validation#dragen-releases) DRAGEN Releases * The source code for a specific version is branched. * Support for the AWS F2 instance type is added. * The software version is tested and validated for robustness, run time, and exact concordance of outputs on F2 vs. F1, over a set of test cases. * No changes are made to any bioinformatics components. ### [hashtag](https://help.dragen.illumina.com/reference/f2-validation#bssh-ica-applications) BSSH/ICA applications * The DRAGEN software is added as a new version. * F2 instance type support is added to the app configuration. * The app version is tested and validated for functionality and concordant outputs, over a set of samples. [hashtag](https://help.dragen.illumina.com/reference/f2-validation#validation-methodology) Validation Methodology ---------------------------------------------------------------------------------------------------------------------- * **Test Samples**: Whole Genome Sequencing (WGS) samples from **HG002, HG003, and HG004** at 35x coverage. * **Versions Tested**: DRAGEN versions **4.2.4**, **4.3.6**, and **4.4.4**. * **Platforms**: Each sample was analyzed with **F1** and **F2** instances using identical command lines and configurations. [hashtag](https://help.dragen.illumina.com/reference/f2-validation#concordance-metrics) Concordance Metrics ---------------------------------------------------------------------------------------------------------------- * **BAM Files**: Verified using md5sum checksums, excluding metadata headers (e.g., timestamps, version strings). * **VCF Files**: Variant caller outputs were also validated using md5sum comparisons. * **Output Metrics**: All summary metrics were compared to confirm that results match. [hashtag](https://help.dragen.illumina.com/reference/f2-validation#results) Results ---------------------------------------------------------------------------------------- * **Bit-exact**: All tested DRAGEN versions produced identical outputs on F1 and F2 instances. * **Known Exceptions**: * repeats.bam files differ in sorting order, but alignment outputs are identical. * Differences in pcr-model-0.log file content is expected due to multi-threading and have no impact on the results [hashtag](https://help.dragen.illumina.com/reference/f2-validation#input-and-output-locations) Input and Output Locations ------------------------------------------------------------------------------------------------------------------------------ Data is available here: * **Input File Location**: [https://basespace.illumina.com/s/qSSsl5ViSZwjarrow-up-right](https://basespace.illumina.com/s/qSSsl5ViSZwj) * **Output Concordance Data**: [https://ilmn-sso.basespace.illumina.com/s/NgwKhpHlhbmBarrow-up-right](https://ilmn-sso.basespace.illumina.com/s/NgwKhpHlhbmB) * versions/HG002.novaseq.pcr-free.35x/f1/ * versions/HG002.novaseq.pcr-free.35x/f2/ * versions/HG003.novaseq.pcr-free.35x/f1/ * versions/HG003.novaseq.pcr-free.35x/f2/ * versions/HG004.novaseq.pcr-free.35x/f1/ * versions/HG004.novaseq.pcr-free.35x/f2/ [hashtag](https://help.dragen.illumina.com/reference/f2-validation#conclusion) Conclusion ---------------------------------------------------------------------------------------------- Illumina has confirmed that DRAGEN Secondary Analysis produces bit-exact outputs across AWS F1 and F2 FPGA instances when using the same software versions, sample data, and commands. Validation results on whole genome sequencing samples from HG002, HG003, and HG004 at 35x coverage with DRAGEN versions 4.2.4, 4.3.6, and 4.4.4, are shared. BAM and VCF file concordance have been validated through checksum comparisons and metric analysis. Customers can transition confidently to faster F2 instances without compromising results. [PreviousDRAGEN Product Obsolescence Noticeschevron-left](https://help.dragen.illumina.com/reference/eol-transition) [NextTroubleshootingchevron-right](https://help.dragen.illumina.com/reference/troubleshooting) Last updated 20 days ago Was this helpful? * [Background](https://help.dragen.illumina.com/reference/f2-validation#background) * [AWS F2 Instance Type Support](https://help.dragen.illumina.com/reference/f2-validation#aws-f2-instance-type-support) * [Development Methods](https://help.dragen.illumina.com/reference/f2-validation#development-methods) * [DRAGEN R&D](https://help.dragen.illumina.com/reference/f2-validation#dragen-r-and-d) * [DRAGEN Releases](https://help.dragen.illumina.com/reference/f2-validation#dragen-releases) * [BSSH/ICA applications](https://help.dragen.illumina.com/reference/f2-validation#bssh-ica-applications) * [Validation Methodology](https://help.dragen.illumina.com/reference/f2-validation#validation-methodology) * [Concordance Metrics](https://help.dragen.illumina.com/reference/f2-validation#concordance-metrics) * [Results](https://help.dragen.illumina.com/reference/f2-validation#results) * [Input and Output Locations](https://help.dragen.illumina.com/reference/f2-validation#input-and-output-locations) * [Conclusion](https://help.dragen.illumina.com/reference/f2-validation#conclusion) Was this helpful? --- # DRAGEN Multi-Cloud | DRAGEN [DRAGEN on AWSchevron-right](https://help.dragen.illumina.com/reference/dragen-multi-cloud/dragen-on-aws) [DRAGEN on AWS Batchchevron-right](https://help.dragen.illumina.com/reference/dragen-multi-cloud/dragen-on-aws-batch) [DRAGEN on Microsoft Azurechevron-right](https://help.dragen.illumina.com/reference/dragen-multi-cloud/dragen-on-azure) [DRAGEN on Microsoft Azure Batchchevron-right](https://help.dragen.illumina.com/reference/dragen-multi-cloud/azure-batch) [PreviousDRAGEN Application Managerchevron-left](https://help.dragen.illumina.com/reference/dragen-application-manager) [NextDRAGEN on AWSchevron-right](https://help.dragen.illumina.com/reference/dragen-multi-cloud/dragen-on-aws) Last updated 20 days ago Was this helpful? Was this helpful? --- # Illumina® DRAGEN™ Secondary Analysis | DRAGEN v4.3 | DRAGEN ![Page cover](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3660538983-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FpFn0gLe5V859SXejQNAW%252Fuploads%252Fgit-blob-d4d60fd0f9a66c771db8570635722d2083888f52%252FDRAGEN%2520Logo%2520Cover%2520-%2520updated.png%3Falt%3Dmedia&width=1248&dpr=3&quality=100&sign=c9f348d7&sv=2) Illumina DRAGEN (Dynamic Read Analysis for GENomics) secondary analysis was developed to address important challenges associated with analyzing NGS (Next Generation Sequencing) data for a range of applications, including genome, exome, transcriptome, and methylome studies. DRAGEN secondary analysis processes NGS data and enables tertiary analysis to drive insights. The available tools make up a highly accurate, comprehensive, and efficient solution that enables labs of all sizes and disciplines to do more with their genomic data. **Product highlights** **Accurate results:** * Pangenome reference genome and machine learning drive unprecedented accuracy * 99.89% accuracy score with the Precision FDA Truth Challenge V2 benchmark data (_2,3_) **Comprehensive platform:** * Analyze NGS data from whole genomes, exomes, methylomes, and transcriptomes * Available on platform of choice and scalable based on needs **Efficient analysis:** * Process a 34x genome in ~ 30 minutes, with all supported callers with DRAGEN server v4 (_1_) * Reduce FASTQ file sizes up to 5x with DRAGEN ORA Compression _References:_ 1. Illumina data on file, 2022. 2. Illumina DRAGEN Secondary Analysis is the first single platform to achieve 99.89% accuracy based on [PrecisionFDA v2 Truth Challenge Benchmark Dataarrow-up-right](https://precision.fda.gov/challenges/10) . Details here [DRAGEN sets new standard for data accuracy in PrecisionFDA benchmark dataarrow-up-right](https://www.illumina.com/science/genomics-research/articles/dragen-shines-again-precisionfda-truth-challenge-v2.html) . Accessed March 22, 2023 3. PrecisionFDA Truth Challenge V2: Calling Variants from Short and Long Reads in Difficult-to-Map Regions. [precision.fda.gov/challenges/10arrow-up-right](https://precision.fda.gov/challenges/10) . Accessed November 3, 2020. [NextDRAGEN Applicationschevron-right](https://help.dragen.illumina.com/dragen-v4.3/overview/key-applications) Last updated 6 months ago Was this helpful? Was this helpful? --- # Citing DRAGEN software | DRAGEN There are two preferred methods to cite DRAGEN software. [hashtag](https://help.dragen.illumina.com/reference/citing-dragen#method-1-cite-dragen-secondary-analysis-software-in-text-or-in-reference-bibliography-list) Method 1: Cite DRAGEN secondary analysis software in-text or in reference/bibliography list --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ### [hashtag](https://help.dragen.illumina.com/reference/citing-dragen#in-text) In-text Proper in-text citation for DRAGEN software must include the Illumina DRAGEN software product used and the version number at the time of data analysis. #### [hashtag](https://help.dragen.illumina.com/reference/citing-dragen#example) Example Secondary analysis was performed using Illumina DRAGEN software, v4.3. ### [hashtag](https://help.dragen.illumina.com/reference/citing-dragen#reference-or-bibliography-list) Reference or bibliography list Citing DRAGEN software in a bibliography or reference list should include the company name, copyright date, name of the DRAGEN software product, version number, format, and link to product website. #### [hashtag](https://help.dragen.illumina.com/reference/citing-dragen#example-1) Example Illumina (2024). DRAGEN secondary analysis (Version 4.3) \[Computer software\]. [https://www.illumina.com/products/by-type/informatics-products/dragen-secondary-analysis.htmlarrow-up-right](file:///Users/mdelrosar1/Library/CloudStorage/OneDrive-Illumina,Inc/Documents/22_Documentation/Gitbook/%2522) [hashtag](https://help.dragen.illumina.com/reference/citing-dragen#method-2-cite-a-specific-algorithm-using-one-of-the-papers-listed-below) Method 2: Cite a specific algorithm using one of the papers listed below ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ### [hashtag](https://help.dragen.illumina.com/reference/citing-dragen#dragen-germline-algorithms) DRAGEN Germline Algorithms Behera, S., Catreux, S., Rossi, M. _et al._, Comprehensive genome analysis and variant detection at scale using DRAGEN, _Nat Biotechnol_ (2024). [https://doi.org/10.1038/s41587-024-02382-1arrow-up-right](https://doi.org/10.1038/s41587-024-02382-1) ### [hashtag](https://help.dragen.illumina.com/reference/citing-dragen#germline-cnv-caller) Germline CNV Caller De La Vega, F.M., _et al._, Benchmarking of Germline Copy Number Variant Callers from Whole Genome Sequencing Data for Clinical Applications, _Bioinformatics Advances_ (2025). [https://doi.org/10.1093/bioadv/vbaf071arrow-up-right](https://doi.org/10.1093/bioadv/vbaf071) Gao, Y., _et al._, Whole-Genome Sequencing is a Viable Replacement for Chromosomal Microarray and Fragile X PCR Testing, _medRxiv_ (2025): 2025-05. [https://doi.org/10.1101/2025.05.24.25328260arrow-up-right](https://doi.org/10.1101/2025.05.24.25328260) ### [hashtag](https://help.dragen.illumina.com/reference/citing-dragen#somatic-cnv-caller) Somatic CNV Caller Masood, D., Ren, L., Nguyen, C., Brundu, F.G., Zheng, L. _et al._, Evaluation of somatic copy number variation detection by NGS technologies and bioinformatics tools on a hyper-diploid cancer genome, _Genome Biology_, **25(1)**, 163 (2024). [https://doi.org/10.1186/s13059-024-03294-8arrow-up-right](https://doi.org/10.1186/s13059-024-03294-8) ### [hashtag](https://help.dragen.illumina.com/reference/citing-dragen#str-expansion-detection) STR Expansion Detection Dolzhenko et al. Detection of long repeat expansions from PCR-free whole-genome sequence data. Genome Res. 2017 Nov;27(11):1895-1903. [https://doi.org/10.1101/gr.225672.117arrow-up-right](https://doi.org/10.1101/gr.225672.117) Dolzhenko, E. _et al._, ExpansionHunter: a sequence-graph-based tool to analyze variation in short tandem repeat regions, _Bioinformatics_, Volume 35, Issue 22, November 2019, Pages 4754–4756, [https://doi.org/10.1093/bioinformatics/btz431arrow-up-right](https://doi.org/10.1093/bioinformatics/btz431) Dolzhenko, E., Bennett, M.F., Richmond, P.A. et al. ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data. Genome Biol 21, 102 (2020). [https://doi.org/10.1186/s13059-020-02017-zarrow-up-right](https://doi.org/10.1186/s13059-020-02017-z) ### [hashtag](https://help.dragen.illumina.com/reference/citing-dragen#lpa-targeted-caller) LPA Targeted Caller Behera, S., Belyeu, J.R., Chen, X. _et al._, Identification of allele-specific KIV-2 repeats and impact on Lp(a) measurements for cardiovascular disease risk, _BMC Med Genomics_ **17**, 255 (2024). [https://doi.org/10.1186/s12920-024-02024-0arrow-up-right](https://doi.org/10.1186/s12920-024-02024-0) ### [hashtag](https://help.dragen.illumina.com/reference/citing-dragen#smn-targeted-caller) SMN Targeted Caller Chen, X., Sanchis-Juan, A., French, C.E. _et al._, Spinal muscular atrophy diagnosis and carrier screening from genome sequencing data, _Genet Med_ **22**, 945–953 (2020). [https://doi.org/10.1038/s41436-020-0754-0arrow-up-right](https://doi.org/10.1038/s41436-020-0754-0) ### [hashtag](https://help.dragen.illumina.com/reference/citing-dragen#cyp2d6-targeted-caller) CYP2D6 Targeted Caller Chen, X., Shen, F., Gonzaludo, N. _et al._, Cyrius: accurate _CYP2D6_ genotyping using whole-genome sequencing data, _Pharmacogenomics J_ **21**, 251–261 (2021). [https://doi.org/10.1038/s41397-020-00205-5arrow-up-right](https://doi.org/10.1038/s41397-020-00205-5) ### [hashtag](https://help.dragen.illumina.com/reference/citing-dragen#gba-targeted-caller) GBA Targeted Caller Toffoli, M., Chen, X., Sedlazeck, F.J. _et al._, Comprehensive short and long read sequencing analysis for the Gaucher and Parkinson’s disease-associated _GBA_ gene, _Commun Biol_ **5**, 670 (2022). [https://doi.org/10.1038/s42003-022-03610-7arrow-up-right](https://doi.org/10.1038/s42003-022-03610-7) ### [hashtag](https://help.dragen.illumina.com/reference/citing-dragen#dux4-rearrangements-caller) DUX4 Rearrangements Caller Grobecker, P., Berri, S., Peden, J.F. _et al._ A dedicated caller for DUX4 rearrangements from whole-genome sequencing data. _BMC Med Genomics_ **18**, 24 (2025). [https://doi.org/10.1186/s12920-024-02069-1arrow-up-right](https://doi.org/10.1186/s12920-024-02069-1) [PreviousTroubleshootingchevron-left](https://help.dragen.illumina.com/reference/troubleshooting) [NextDRAGEN Publicationschevron-right](https://help.dragen.illumina.com/reference/dragen-publications) Last updated 7 months ago Was this helpful? * [Method 1: Cite DRAGEN secondary analysis software in-text or in reference/bibliography list](https://help.dragen.illumina.com/reference/citing-dragen#method-1-cite-dragen-secondary-analysis-software-in-text-or-in-reference-bibliography-list) * [In-text](https://help.dragen.illumina.com/reference/citing-dragen#in-text) * [Reference or bibliography list](https://help.dragen.illumina.com/reference/citing-dragen#reference-or-bibliography-list) * [Method 2: Cite a specific algorithm using one of the papers listed below](https://help.dragen.illumina.com/reference/citing-dragen#method-2-cite-a-specific-algorithm-using-one-of-the-papers-listed-below) * [DRAGEN Germline Algorithms](https://help.dragen.illumina.com/reference/citing-dragen#dragen-germline-algorithms) * [Germline CNV Caller](https://help.dragen.illumina.com/reference/citing-dragen#germline-cnv-caller) * [Somatic CNV Caller](https://help.dragen.illumina.com/reference/citing-dragen#somatic-cnv-caller) * [STR Expansion Detection](https://help.dragen.illumina.com/reference/citing-dragen#str-expansion-detection) * [LPA Targeted Caller](https://help.dragen.illumina.com/reference/citing-dragen#lpa-targeted-caller) * [SMN Targeted Caller](https://help.dragen.illumina.com/reference/citing-dragen#smn-targeted-caller) * [CYP2D6 Targeted Caller](https://help.dragen.illumina.com/reference/citing-dragen#cyp2d6-targeted-caller) * [GBA Targeted Caller](https://help.dragen.illumina.com/reference/citing-dragen#gba-targeted-caller) * [DUX4 Rearrangements Caller](https://help.dragen.illumina.com/reference/citing-dragen#dux4-rearrangements-caller) Was this helpful? --- # Deployment Options | DRAGEN v4.3 | DRAGEN DRAGEN analysis is available on multiple platforms. Platform Description DRAGEN on-premises server DRAGEN on-premises server offers highly accurate secondary analysis in a fraction of time compared with a traditional CPU-based system. - Analyze and store data locally - Supports varying levels of command line interface - Replace up to 30 traditional compute instances - Fully process a 34× whole human genome in ~30 minutes. _(1)_ - One unit supports two NovaSeq 6000 Systems running at full capacity DRAGEN analysis on Illumina Connected Analytics Couples the accuracy and speed of the DRAGEN with the ability to customize analysis pipeline to operationalize informatics on a secure platform. DRAGEN on BaseSpace Sequence Hub (BSSH) Push button analysis capability in an intuitive, easy-to-use interface with compliance, and storage features of BaseSpace Sequence Hub and Amazon Web Services (AWS). DRAGEN onboard NovaSeq X Series \- Flexibly runs multiple secondary analysis pipelines in parallel. - Performs up to four simultaneous applications per flow cell in a single run. - Brings up to 5x lossless data compression, and analysis with supported applications - Provides savings on analysis, which over five years can exceed the price of the sequencer DRAGEN onboard NextSeq 1000 and NextSeq 2000 Systems \- Provides access to select DRAGEN analysis informatics pipelines - Enables users to generate results in as little as two hours - Uses intuitive pipeline algorithms to reduce reliance on external informatics experts DRAGEN onboard MiSeq i100 Series Intuitive, ultra-rapid analysis including DRAGEN BCL convert, DRAGEN Library QC, DRAGEN small WGS and DRAGEN Microbial Enrichment Plus. - Rapid results with comprehensive secondary analysis generated in two hours or less _(2)_ - Highly efficient workflow with a single user touchpoint to VCF and/or html report and no intermediate file transfers - Exceptionally easy with an intuitive interface for non-expert users DRAGEN on AWS, Azure DRAGEN supports the FPGA enabled instance types of AWS, Azure. Rpm installers and the Kernel driver can be installed on images managed by the user, and DRAGEN can be run by purchasing a license. DRAGEN on AWS and Azure Marketplace Pre-configured Amazon Machine Images (AMI) and Azure Virtual Machines with DRAGEN installed can be accessed from the respective marketplace offerings in a Pay-As-You-Use model. DRAGEN on GCP DRAGEN is made available on the Google Cloud Platform. Pre-configured instances with DRAGEN installed can be accessed through the GCP application interface. Limited availability. Please reach out to your Illumina representative for access. > (1) HG002 from PrecisionFDA truth challenge V2 run with DRAGEN analysis v4.0 on DRAGEN server v4, all callers > (2) When run according to sample recommendations [PreviousDRAGEN Applicationschevron-left](https://help.dragen.illumina.com/dragen-v4.3/overview/key-applications) [NextDRAGEN v4.3chevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3) Last updated 7 months ago Was this helpful? Was this helpful? --- # DRAGEN Applications | DRAGEN v4.3 | DRAGEN [hashtag](https://help.dragen.illumina.com/dragen-v4.3/overview/key-applications#applications) Applications ---------------------------------------------------------------------------------------------------------------- DRAGEN analysis offers a large selection of application pipelines. Pipeline Description Variant Types Detected Metrics Provided DRAGEN Demultiplexing Rapid demultiplexing of NGS analysis N/A N/A DRAGEN ORA Compression DRAGEN ORA compression is optimized for high compression ratios of FASTQ files, as well as rapid compression and decompression, all while preserving data integrity. N/A Compression Ratio Run Time DRAGEN Map + Align The DRAGEN Map + Align can be run as a standalone or as part of DRAGEN’s suite of pipelines N/A Mapping metrics Duration Metrics Coverage Metrics DRAGEN Germline The DRAGEN Germline Pipeline provides end-to-end NGS analysis, including advanced error model calibration for increased accuracy, and repeat expansion detection and genotyping through Illumina Expansion Hunter. SNV/Indel CNV SV Repeat Expansions Mapping metrics Duration Metrics Coverage Metrics Variant Metrics Callability Report DRAGEN Somatic The DRAGEN Somatic Pipeline includes tumor-only and tumor–normal modes, designed for detecting somatic variants in tumor samples. Both modes make no ploidy assumptions, enabling detection of low-frequency alleles. SNV/Indel CNV SV TMB MSI HLA Mapping metrics Duration Metrics Coverage Metrics Variant Metrics Callability Report DRAGEN Enrichment The DRAGEN Enrichment Pipeline combines DRAGEN’s germline and somatic callers into a pipeline designed specifically for analyzing enrichment samples. Includes a full suite of enrichment metrics and reporting. SNV/Indel CNV SV Mapping metrics Duration Metrics Coverage Metrics Variant Metrics Callability Report DRAGEN RNA The DRAGEN RNA Pipeline performs transcriptome analysis starting with splice junction discovery and alignment, followed by rapid alignment and splice junction mapping and quantification. For differential expression, Illumina recommends the DRAGEN Differential Expression app on BaseSpace Sequence Hub. Gene fusion SNV/Indel Mapping metrics Duration Metrics Coverage Metrics Variant Metrics Callability Report DRAGEN Single Cell RNA The DRAGEN Single Cell RNA pipeline performs demultiplexing, cell-barcode and UMI error correction, sequence alignment, and quantification of gene expression. N/A Mapping Metrics Duration Metrics Coverage Metrics Callability Report Cell Metrics DRAGEN Joint Genotyping The DRAGEN Joint Genotyping/Population Pipeline calls variants jointly across multiple genomes and scales to large cohorts of samples at expedited speeds with uncompromising accuracy. SNV/Indel CNV SV Repeat Expansions Mapping metrics Duration Metrics Coverage Metrics Variant Metrics Callability Report DRAGEN Methylation The DRAGEN Methylation Pipeline performs alignment, methyl calling, and calculates alignment and methylation metrics. N/A Mapping metrics Duration Metrics Coverage Metrics Variant Metrics Callability Report DRAGEN Reference Builder Accepts FASTA files, and builds the proprietary reference used by the DRAGEN apps. N/A N/A DRAGEN TruSight Oncology 500 ctDNA Analysis Software Secondary analysis support for Illumina’s TruSight Oncology 500 ctDNA. Available on the local DRAGEN Server version 3 and later. SNV/Indel CNV DNA fusions MSI TMB Mapping metrics Duration Metrics Coverage Metrics Variant Metrics Callability Report DRAGEN Imputation The DRAGEN Imputation pipeline is an end to end user friendly tool that enables scalable low pass whole genome sequencing analysis N/A Impute ≤100 samples simultaneously 1.7x faster compared to original GLIMPSE code [hashtag](https://help.dragen.illumina.com/dragen-v4.3/overview/key-applications#analysis-uses) Analysis Uses ------------------------------------------------------------------------------------------------------------------ DRAGEN analysis can be used in numerous fields in the biological sciences. Analysis Description Genetic Diseases Reduce time required for genomic analysis, with high accuracy and comprehensiveness Oncology Analyze tumor-only and tumor/normal samples with accuracy, comprehensiveness, and efficiency Cell and Molecular Biology Advance understanding of cellular mechanisms with rapid analysis pipelines for bulk and single cell samples Population Genomics Accurately and efficiently analyze sequenced genomes at scale. Accelerate re-analysis as computational tools improve over time Infectious Disease Detect and characterize infectious diseases with a comprehensive solution Agrigenomics Efficiently analyze animals and plants of varying genomic complexities with custom reference [PreviousIllumina® DRAGEN™ Secondary Analysischevron-left](https://help.dragen.illumina.com/dragen-v4.3) [NextDeployment Optionschevron-right](https://help.dragen.illumina.com/dragen-v4.3/overview/deployment-options) Last updated 7 months ago Was this helpful? * [Applications](https://help.dragen.illumina.com/dragen-v4.3/overview/key-applications#applications) * [Analysis Uses](https://help.dragen.illumina.com/dragen-v4.3/overview/key-applications#analysis-uses) Was this helpful? --- # Release Notes | DRAGEN Release notes for prior DRAGEN versions DRAGEN v4.5 * [DRAGEN v4.5.4 Release Notes](https://help.dragen.illumina.com/reference/release-notes-readme/dragen-crn-v4.5.4) * [DRAGEN v4.5.2 TruPath Release Notesarrow-up-right](https://www.illumina.com/content/dam/illumina-support/documents/downloads/software/illumina-trupath-genome/200078754_00_Customer_Release_Notes_DRAGEN_4.5.2.pdf) DRAGEN v4.4 * [DRAGEN v4.4.7 Release Notesarrow-up-right](https://www.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200077195_00_DRAGEN_4_4_7_Customer_Release_Notes.pdf) * [DRAGEN v4.4.6 Release Notesarrow-up-right](https://www.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200074412_00_DRAGEN_4_4_6_Customer_Release_Notes.pdf) * [DRAGEN v4.4.4 Release Notesarrow-up-right](https://www.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200068065_00_DRAGEN-4_4_4-Customer-Release-Notes.pdf) DRAGEN v4.3 * [DRAGEN v4.3.17 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200071549_00_DRAGEN_4_3_17_Customer_Release_Notes.pdf) * [DRAGEN v4.3.16 Release Notesarrow-up-right](https://www.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200067026_00_DRAGEN-4.3.16-Customer-Release-Notes.pdf) * [DRAGEN v4.3.13 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200063347_00_DRAGEN_v4.3.13_Customer_Release_Notes.pdf) * [DRAGEN v4.3.6 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200056923_00_DRAGEN_4_3_6_Customer-Release-Notes.pdf) DRAGEN v4.2 * [DRAGEN v4.2.9 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200054960_00_DRAGEN_v4.2.9_CRN.pdf) * [DRAGEN v4.2.7 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200049372_00_DRAGEN_v4_2_7_CRN.pdf) * [DRAGEN v4.2.4 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200040845_01_DRAGEN-4.2-Customer-Release-Notes.pdf) DRAGEN v4.1 * [DRAGEN v4.1.23 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200046067_00_DRAGEN-4.1.23-Customer-Release-Notes.pdf) * [DRAGEN v4.1.7 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200040752_00_DRAGEN_v4.1.7_Customer_Release_Notes.pdf) * [DRAGEN v4.1.5 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200034017_00_DRAGEN-4.1.5-Customer-Release-Notes.pdf) DRAGEN v4.0 * [DRAGEN v4.0.5 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200037976_00_DRAGEN-4.0.5-Customer-Release-Notes.pdf) * [DRAGEN v4.0.3 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200024449_02_DRAGEN_4_0_3Customer_Release_Notes.pdf) DRAGEN v3.10 * [DRAGEN v3.10.17 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200054720_00_DRAGEN-3.10.17-Customer-Release-Notes.pdf) * [DRAGEN v3.10.16 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200045317_00_DRAGEN-3.10.16-Customer-Release-Notes.pdf) * [DRAGEN v3.10.12 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200031686_01_DRAGEN_3_10_12_Customer_Release_Notes.pdf) * [DRAGEN v3.10.11 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200031429_01_DRAGEN_3_10_11_Customer_Release_Notes.pdf) * [DRAGEN v3.10.10 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200026470_01_DRAGEN_v3_10_10_Customer_Release_Notes.pdf) * [DRAGEN v3.10.9 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200022616_01_DRAGEN_3_10_9_Customer_Release_Notes.pdf) * [DRAGEN v3.10.8 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200018949_01_DRAGEN_3_10_8_Customer_Release_Notes.pdf) * [DRAGEN v3.10.4 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200016065_01_DRAGEN_3_10_4_Customer_Release_Notes.pdf) [PreviousDRAGEN Publicationschevron-left](https://help.dragen.illumina.com/reference/dragen-publications) [NextDRAGEN v4.5.4 Release Noteschevron-right](https://help.dragen.illumina.com/reference/release-notes-readme/dragen-crn-v4.5.4) Last updated 20 days ago Was this helpful? Was this helpful? --- # DRAGEN v4.3 | DRAGEN [Getting Startedchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started) [DRAGEN Host Softwarechevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software) [DRAGEN Reference Supportchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support) [DRAGEN DNA Pipelinechevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline) [DRAGEN RNA Pipelinechevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline) [DRAGEN Single-Cell Pipelinechevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline) [DRAGEN Methylation Pipelinechevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-methylation-pipeline) [DRAGEN Amplicon Pipelinechevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-amplicon-pipeline) [Explify Analysis Pipelinechevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline) [DRAGEN Recipeschevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes) [BCL conversionchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/bcl-conversion) [Illumina Connected Annotationschevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/nirvana) [ORA Compressionchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/ora-compression) [Command Line Optionschevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/command-line-options) [DRAGEN Reportschevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reports) [Tools and Utilitieschevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/tools-and-utilities) [PreviousDeployment Optionschevron-left](https://help.dragen.illumina.com/dragen-v4.3/overview/deployment-options) [NextGetting Startedchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started) Last updated 7 months ago Was this helpful? Was this helpful? --- # Revision History | DRAGEN Revision history of the DRAGEN product documentation Version Date Description of Change 05 May 2025 Add v4.4 user guide 04 Feb 2025 Minor updates, errata and clarifications. 03 Dec 2024 Add v4.4 pre-release information for single cell. 02 Sept 2024 Minor updates, errata and clarifications. 01 May 2024 Initial release. [PreviousDRAGEN v4.5.4 Release Noteschevron-left](https://help.dragen.illumina.com/reference/release-notes-readme/dragen-crn-v4.5.4) Last updated 7 months ago Was this helpful? Was this helpful? --- # DRAGEN Product Obsolescence Notices | DRAGEN [hashtag](https://help.dragen.illumina.com/reference/eol-transition#dragen-secondary-analysis-end-of-life-roadmap) DRAGEN Secondary Analysis - End of Life Roadmap ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- This section refers to DRAGEN Secondary Analysis package versions for use on-prem and BYOL Version Date of Obsolescence v3.3 and lower June 1, 2025 v3.9 and lower\* December 1, 2025 v3.10 and lower\* June 1, 2026 \*v3.7.8 and v3.9.5 will continue to be supported by Illumina indefinitely After the dates of obsolescence, there will be no further updates, improvements, bug fixes, or support for the affected versions. Customers will no longer have access to download installers, release notes, and user guides after date of obsolescence. [hashtag](https://help.dragen.illumina.com/reference/eol-transition#dragen-bssh-ica-workflows-end-of-life-roadmap) DRAGEN BSSH/ICA Workflows - End of Life Roadmap ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- **The DRAGEN Support Team strongly recommends transitioning to F2 enabled workflows by November 20, 2025 to ensure a smooth transition and minimize analysis delays as F1 instances are taken offline.** Amazon Web Services’ announcement in 2024 that their F1 instances will be replaced by a new generation of FPGA-powered cloud hardware, Amazon EC2 F2 instances, throughout 2025. F1 instances will be phased out of service and made obsolete by December 20, 2025. DRAGEN pipelines on ICA and BSSH make use of these F instances to run FPGA-accelerated DRAGEN analysis workflows in the cloud. To ensure uninterrupted service, Illumina is releasing updated versions of DRAGEN pipelines compatible with F2 instances within the same time frame. This change impacts all DRAGEN pipelines before v4.4.4. In addition to having ongoing AWS support, F2 instances introduce capacity and performance improvements with up to 40% reduced turnaround time for analysis (runtime and queue times). F2-compatible DRAGEN versions of BSSH and ICA apps are being made available for a subset of current versions (see tables below). Upgrade pathways described in Tables 1 and 2 will produce read, variant, and metric exact results on F2 instances. The transition from F1 to F2 will be accompanied by pricing changes for BSSH and ICA. DRAGEN pipelines running on F2 will incur the following DRAGEN License cost per gigabase of data processed in addition to an hourly [F-instance computer costarrow-up-right](https://help.ica.illumina.com/reference/r-pricing) . Gigabases per sample iCredits per gigabase <=80  0.10  \>80 and <=160  0.08  \>160 and <=240  0.07  \>240 and <=320  0.06  \>320  0.05  Note: If your DRAGEN job fails, no DRAGEN license cost will be charged.  **F2 Regional Availability** Region Region Identifier Are F2 Instances Available? Australia - Sydney AU Yes Canada - Central CA Yes Germany - Frankfurt EU Yes India - Mumbai IN Yes Indonesia - Jakarta ID No\* Israel - Tel Aviv IL No\* Japan - Tokyo JP Yes South Korea - Seoul KR No\* Singapore SG Yes United Arab Emirates AE No\* United Kingdom - London GB Yes N. Virginia US Yes \*We are coordinating with our underlying cloud provider and will share updates to this page as they become available **Table 1, ICA workflows + F2 patched versions** Current Version F2 Patched Version Availability DRAGEN Amplicon DNA and RNA 4-2-4-v2 DRAGEN\_Amplicon\_DNA\_and\_RNA\_4-2-4-2 Available now DRAGEN Amplicon DNA and RNA 4-3-6 DRAGEN\_Amplicon\_DNA\_and\_RNA\_4-3-6-1 Available now DRAGEN CNV Baseline Builder 4-2-4-v2 DRAGEN\_CNV\_Baseline\_Builder\_4-2-4-2 Available now DRAGEN CNV Baseline Builder 4-3-6 DRAGEN\_CNV\_Baseline\_Builder\_4-3-6-1 Available now DRAGEN Germline Whole Genome 4-2-4-v2 DRAGEN\_Germline\_Whole\_Genome\_4-2-4-2 Available now DRAGEN\_Germline\_Whole\_Genome\_4-3-6\_v2 DRAGEN\_Germline\_Whole\_Genome\_4-3-6-1 Available now DRAGEN\_Germline\_Whole\_Genome\_4-3-13 DRAGEN\_Germline\_Whole\_Genome\_4-3-13-1 Available now DRAGEN Germline Enrichment 4-2-4-v2 DRAGEN\_Germline\_Enrichment\_4-2-4-2 Available now DRAGEN Germline Enrichment 4-3-6 DRAGEN\_Germline\_Enrichment\_4-3-6-1 Available now DRAGEN\_Germline\_Enrichment\_4-3-13 DRAGEN\_Germline\_Enrichment\_4-3-13-1 Available now DRAGEN for ILMN cfDNA Prep with Enrichment 4-0-3 DRAGEN\_for\_ILMN\_cfDNA\_Prep\_with\_Enrichment\_4-0-3-1 Available now DRAGEN Imputation 4-2-4 DRAGEN\_Imputation\_4-2-4-2 Available now DRAGEN Imputation 4-2-7-v3 DRAGEN\_Imputation\_4-2-7-1 Available now DRAGEN Joint Pedigree Calling 4-2-4-v2 DRAGEN\_Joint\_Pedigree\_4-2-4-2 Available now DRAGEN Joint Pedigree Calling 4-3-6 DRAGEN\_Joint\_Pedigree\_4-3-6-1 Available now DRAGEN End to End Joint Pedigree Calling 4-2-7 DRAGEN\_End-to-End\_Joint\_Pedigree\_Calling\_4-2-7-1 Available now DRAGEN End to End Joint Pedigree Calling 4-3-6 DRAGEN\_End-to-End\_Joint\_Pedigree\_Calling\_4-3-6-1 Available now DRAGEN\_End-to-End\_Joint\_Pedigree\_Calling\_4-3-13 DRAGEN\_End-to-End\_Joint\_Pedigree\_Calling\_4-3-13-1 Available now DRAGEN\_End-to-End\_Joint\_Pedigree\_Calling\_4-3-17 F2 compatible, no upgrade required Available now DRAGEN Methylation 4-3-6 DRAGEN\_Methylation\_4-3-6-1 Available now DRAGEN\_Methylation\_4-3-13 DRAGEN\_Methylation\_4-3-13-1 Available now DRAGEN MSI Baseline Builder 4-3-6 DRAGEN\_MSI\_Baseline\_Builder\_4-3-6-1 Available now DRAGEN\_Multigenome\_Reference\_Builder\_4-3-6 DRAGEN\_Pangenome\_Reference\_Builder\_4-3-6 Available now DRAGEN\_Pangenome\_VCF\_Builder\_4-3-6 DRAGEN\_Pangenome\_VCF\_Builder\_4-3-6 Available now DRAGEN RNA 4-2-4-v2 DRAGEN\_RNA\_4-2-4-2 Available now DRAGEN RNA 4-3-6 DRAGEN\_RNA\_4-3-6-1 Available now DRAGEN\_RNA\_4-3-13 DRAGEN\_RNA\_4-3-13-1 Available now DRAGEN\_Small\_WGS\_4-3-13 DRAGEN\_Small\_WGS\_4-3-13-1 Available now DRAGEN Somatic Whole Genome 4-2-4-v2 DRAGEN\_Somatic\_Whole\_Genome\_4-2-4-2 Available now DRAGEN Somatic Whole Genome 4-3-6 DRAGEN\_Somatic\_Whole\_Genome\_4-3-6-1 Available now DRAGEN\_Somatic\_Whole\_Genome\_4-3-13 DRAGEN\_Somatic\_Whole\_Genome\_4-3-13-1 Available now DRAGEN\_Somatic\_Whole\_Genome\_4-3-17 F2 compatible, no upgrade required Available now DRAGEN Somatic Enrichment 4-2-4-v2 DRAGEN\_Somatic\_Enrichment\_4-2-4-2 Available now DRAGEN Somatic Enrichment 4-3-6 DRAGEN\_Somatic\_Enrichment\_4-3-6-1 Available now DRAGEN\_Somatic\_Enrichment\_4-3-13 DRAGEN\_Somatic\_Enrichment\_4-3-13-1 Available now DRAGEN\_Somatic\_Enrichment\_4-3-17 F2 compatible, no upgrade required Available now DRAGEN Systematic Noise File Builder 4-2-4-v2 DRAGEN\_Systematic\_Noise\_File\_Builder\_4-2-4-2 Available now DRAGEN Systematic Noise File Builder 4-3-6 DRAGEN\_Systematic\_Noise\_File\_Builder\_4-3-6-1 Available now DRAGEN Reference Builder 4-2-4-v2 DRAGEN\_Reference\_Builder\_4-2-4-2 Available now DRAGEN Reference Builder 4-3-6 DRAGEN\_Reference\_Builder\_4-3-6-1 (SW only mode) Available now **Table 2, BSSH apps + F2 patched versions** Current Version F2 Patched Version Availability DRAGEN Baseline Builder v4.2.4 DRAGEN Baseline Builder v4.2.4002 Available now DRAGEN Baseline Builder v4.3.6 DRAGEN Baseline Builder v4.3.6001 Available now DRAGEN Enrichment v4.2.4 DRAGEN Enrichment v4.2.4002 Available now DRAGEN Enrichment v4.2.7 DRAGEN Enrichment v4.2.7001 Available now DRAGEN Enrichment v4.3.6 DRAGEN Enrichment v4.3.6001 Available now DRAGEN Enrichment v4.3.13 DRAGEN Enrichment v4.3.13001 Available now DRAGEN Germline v4.2.4 DRAGEN Germline v4.2.4002 Available Now DRAGEN Germline v4.3.6 DRAGEN Germline v4.3.6001 Available now DRAGEN Germline v4.3.13 DRAGEN Germline v4.3.13001 Available now DRAGEN ILMN cfDNA Prep v4.0.3 DRAGEN ILMN cfDNA Prep v4.0.3001 Available now DRAGEN Imputation v4.2.7 DRAGEN Imputation v4.2.7001 Available now DRAGEN Imputation v4.3.6 DRAGEN Imputation v4.3.6001 Available now DRAGEN Joint Genotyping v4.2.4 DRAGEN Joint Genotyping v4.2.4002 Available now DRAGEN Joint Genotyping v4.3.6 DRAGEN Joint Genotyping v4.3.6001 Available now DRAGEN Methylation v4.3.6 DRAGEN Methylation v4.3.6001 Available now DRAGEN Methylation v4.3.13 DRAGEN Methylation v4.3.13001 Available now DRAGEN Microbial Enrichment Plus v1.1.0 DRAGEN Microbial Enrichment Plus v1.1.1 Available now DRAGEN Pangenome Reference Builder v4.3.6 DRAGEN Pangenome Reference Builder v4.3.6001 Available now DRAGEN Pangenome VCF Builder v4.3.6 DRAGEN Pangenome VCF Builder v5.0.0 Available now DRAGEN Reference Builder v4.2.4 DRAGEN Reference Builder v4.2.4002 Available Now DRAGEN Reference Builder v4.3.6 DRAGEN Reference Builder v4.3.6001 Available now DRAGEN small WGS v4.3.13 DRAGEN small WGS v4.3.13001 Available now DRAGEN Somatic v4.2.4 DRAGEN Somatic v4.2.4002 Available now DRAGEN Somatic v4.2.7 DRAGEN Somatic v4.2.7001 Available now DRAGEN Somatic v4.3.6 DRAGEN Somatic v4.3.6001 Available now DRAGEN Somatic v4.3.13 DRAGEN Somatic v4.3.13001 Available now DRAGEN Somatic v4.3.17 DRAGEN Somatic v4.3.17001 Available now Recommended transition paths for all other versions are summarized in tables 3 and 4 below. Applications using these transition paths are not receiving bit exact equivalent apps, and Illumina recommends upgrading to a more recent version of the release workflow when available. Because these upgrades use different underlying DRAGEN versions, the results will not have bit exact outputs. **Table 3, Recommended transition paths for all other DRAGEN ICA workflows using F1 instances** Current Version New F2 Patched Version Availability DRAGEN Amplicon v3.10.4, v4.0.3 DRAGEN Amplicon DNA + RNA v4.4.4 Available now DRAGEN CNV Baseline Builder v3.10.4, v4.0.3 DRAGEN CNV Baseline Builder v4.4.4 Available now DRAGEN FASTQC + MultiQC DRAGEN FASTQC/MultiQC v3.9.5 DRAGEN Germline WGS v4.4.4 (Map/Align provides the same functionality as this app) Available now DRAGEN Germline WGS v3.8.4, v3.9.5, v3.10.4, v3.10.8, v4.0.3, v4.0.5, v4.1.5, v4.1.23 DRAGEN Germline WGS v4.4.4 Available now DRAGEN Germline Enrichment v3.9.5, v3.10.4, v3.10.8, v4.0.3, v4.1.5, v4.1.23 DRAGEN Germline Enrichment v4.4.4 Available now DRAGEN Imputation v4.0.3 DRAGEN Imputation v4.2.7-1 Available now DRAGEN Joint Genotyping v3.9.5 DRAGEN Joint Pedigree v3.10.4, v4.0.5 DRAGEN Joint Pedigree v4.4.4 Available now DRAGEN E2E Joint Pedigree v3.10.4, v4.0.3 DRAGEN E2E Joint Pedigree v4.4.4 Available now DRAGEN Methylation v3.9.5, v3.10.4, v4.0.3, v4.1.23 DRAGEN Methylation v4.4.4 Available now DRAGEN RNA v3.9.5, v3.10.4, v4.0.3, v4.1.5, v4.1.23 DRAGEN RNA v4.4.4 Available now DRAGEN scRNA v3.9.5, v4.0.3, v4.4.2 DRAGEN scRNA v4.4.6 Available now DRAGEN Somatic WGS v3.9.5, v3.10.4, v4.0.3, v4.0.5, v4.1.23 DRAGEN Somatic WGS v4.4.4 Available now DRAGEN Somatic Enrichment v3.10.4, v4.0.3, v4.1.5, v4.1.23 DRAGEN Somatic Enrichment v4.4.4 Available now DRAGEN Reference Builder v3.9.5, v3.10.4, v4.0.3 No alternative app planned. Recommend moving to newer v4.3/v4.4 workflows. N/A **Table 4, Recommended transition paths for all other DRAGEN BSSH apps using F1 instances** Current Version New F2 Patched Version Availability DRAGEN Amplicon v3.8.6, v3.9.5, v3.10.4, v4.2.4, v4.3.6, v4.3.13 DRAGEN Amplicon v4.4.4 Available now DRAGEN Baseline Builder v3.7.5, v3.8.4, v3.9.5, v3.10.4, v4.0.3, v4.1.23 DRAGEN Baseline Builder v4.4.4 Available now DRAGEN Baseline Builder v4.3.7, v4.3.8 DRAGEN Baseline Builder v4.3.6001 Available Now DRAGEN CNV Baseline Builder v3.4.5, v3.4.6, v3.5.7, v3.6.3 DRAGEN Baseline Builder v4.3.6001 Available Now DRAGEN Covid Lineage - All versions DRAGEN Microbial Amplicon v1.1.0 Available Now DRAGEN Enrichment v3.4.5, v3.5.7, v3.6.3, v3.7.5, v3.8.4, v3.9.5, v3.10.4, v4.0.3, v4.1.23 DRAGEN Enrichment v4.4.4 Available now DRAGEN FASTQ Toolkit v1.0.0, v1.1.0, v1.2.0, v1.3.1 New app to be developed 2026 DRAGEN FASTQC + MultiQC v3.6.3, v3.9.5 DRAGEN Germline v4.4.4 (Map/Align only provides the same functionality as this app) Available now DRAGEN Germline v3.3.7, v3.4.5, v3.5.7, v3.6.3, v3.7.5, v3.8.4, v3.9.5, v3.10.4, v4.0.3, v4.1.23 DRAGEN Germline v4.4.4 Available now DRAGEN Germline v4.3.7 DRAGEN Germline v4.3.6001 Available Now DRAGEN Germline All Callers v3.9.5 DRAGEN Germline v4.4.4 Available now DRAGEN Germline for Pop Gen v3.7.8 DRAGEN Germline v4.4.4 Available now DRAGEN Imputation v4.0.3 DRAGEN Imputation v4.3.6001 Available now DRAGEN Joint Genotyping v3.2.8, v3.3.7, v3.4.5, v3.4.12, v3.5.7, v3.6.3, v3.7.5, v3.9.5, v3.10.4, v4.3.7 DRAGEN Joint Genotyping v4.3.6001 Available now DRAGEN Metagenomics Pipeline v3.5.8, v3.5.9, v3.5.10, v3.5.11, v3.5.12, v3.5.13 DRAGEN 16s Plus v1.0.0, DRAGEN Metagenomics v3.7.0 Both apps available now DRAGEN Microbial Amplicon v1.0.0, v1.1.0 No transition required - not impacted by F2 change Available Now DRAGEN PopGen v3.10.4, v3.10.5 DRAGEN PopGen v4.3.6 Available now DRAGEN Reference Builder v3.2.8, v3.3.7, v3.4.5, v3.5.7, v3.6.3, v3.7.5, v3.8.4, v3.9.5, v3.10.4, v4.0.3, v4.1.23 DRAGEN Reference Builder v4.4.4 Available now DRAGEN RNA v3.3.7, v3.4.5, v3.5.7, v3.6.3, v3.7.5, v3.8.4, v3.9.5, v3.10.4, v4.0.3, v4.0.4, v4.1.23, v4.2.4, v4.2.7, v4.3.6, v4.3.7, v4.3.13 DRAGEN RNA v4.4.4 Available now DRAGEN scATAC v4.0.3, v4.2.4 End of life, no replacement planned N/A DRAGEN scRNA v3.7.4, v3.8.4, v3.9.5, v3.10.4, v4.0.3, v4.2.4, v4.3.6, v4.4.2 DRAGEN scRNA v4.4.6000 Available now DRAGEN Somatic v3.2.8, v3.3.7, v3.4.5, v3.5.7, v3.6.3, v3.7.5, v3.8.4, v3.9.5, v3.10.4, v4.0.3, v4.1.23 DRAGEN Somatic v4.4.4 Available now Reference Builder (Instrument) on BSSH v1.0.0, v1.1.0, v1.2.0, v2.0.0, v3.0.0 DRAGEN Reference Builder v4.4.6 Available now Nanostring GeoMx NGS Pipeline v2.0.21 Nanostring GeoMx NGS Pipeline NextSeq 1000/2000 local workflow (on instrument only) Nanostring GeoMx NGS Pipeline (standalone application, non-DRAGEN, provided by Bruker) Available Now Information on TSO 500 and TSO 500 ctDNA F2 transition may be found in the [TSO 500 Software User Guidearrow-up-right](https://help.tso500software.illumina.com/f2-support-on-ica) . [hashtag](https://help.dragen.illumina.com/reference/eol-transition#bcl-convert-bssh-workflow-end-of-life-roadmap) BCL Convert BSSH Workflow End of Life Roadmap --------------------------------------------------------------------------------------------------------------------------------------------------------------------- Several versions of BCL Convert on BSSH will be obsolesced on December 20, 2025. These apps are not impacted by F instance migration but are being obsolesced as part of an ongoing effort to streamline our software products and portfolio. **Recommended transition paths for BCL Convert EOL versions** Current Version New F2 Patched Version Availability BCL Convert v1.2.0, v1.2.1, v1.3.0, v2.0.0, v2.1.0, v2.2.0, v2.3.0, v2.4.0 BCL EA v1.1.0 BCL for ICA Beta v1.0.0 BCL v2.7.0 Available now [hashtag](https://help.dragen.illumina.com/reference/eol-transition#dragen-bssh-run-planning-workflow-end-of-life-notifications) DRAGEN BSSH Run Planning Workflow End of Life Notifications ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The following run planning workflow versions have been scheduled for end of life related to F instance transition and ongoing efforts to streamline our software products and portfolio. **New DRAGEN Cloud Analysis Auto-Launch Pipelines F2-Compatible Versions** Instrument(s) Pipeline Name Current Version New Version Availability (2025) NovaSeqX DRAGEN Enrichment 4.3.16 4.3.16-1 Available Now NextSeq1000/2000, NextSeq 500/550 MiniSeq, MiSeq DRAGEN Enrichment 4.2.7 4.2.7-1 Available Now NovaSeqX DRAGEN Germline 4.3.16 4.3.16-1 Available Now NextSeq1000/2000 DRAGEN Germline 4.2.7 4.2.7-1 Available Now NovaSeqX DRAGEN Methylation 4.3.16 4.3.16-1 Available Now NovaSeqX DRAGEN RNA 4.3.16 4.3.16-1 Available Now NextSeq1000/2000 DRAGEN RNA 4.2.7 4.2.7-1 Available Now NovaSeqX DRAGEN Somatic 4.3.16 4.3.16-1 Available Now NextSeq1000/2000, NextSeq 500/550 iSeq, MiniSeq, MiSeq DRAGEN Amplicon 4.3.13 4.3.13-1 Available Now NextSeq 500/550, iSeq, MiniSeq, MiSeq DRAGEN Amplicon for ICA 4.3.13 4.3.13-1 Available Now **Recommended Transition Paths for all other DRAGEN Cloud Analysis Auto-Launch Pipelines using F1-instances** Instrument Pipeline Name Current Version(s) New Version Availability (2025) NovaSeqX DRAGEN Enrichment 4.1.5, 4.1.7, 4.1.23, 4.3.13 4.3.16-1 Available Now NovaSeqX DRAGEN Germline 4.1.5, 4.1.7, 4.1.23, 4.3.13 4.3.16-1 Available Now NovaSeqX DRAGEN Methylation 4.1.23, 4.3.13 4.3.16-1 Available Now NovaSeqX DRAGEN RNA 4.1.5, 4.1.7, 4.1.23, 4.3.13 4.3.16-1 Available Now NovaSeqX DRAGEN Somatic 4.1.23, 4.3.13 4.3.16-1 Available Now NovaSeqX NovaSeq 6000 Heme WGS 1.0.0 4.4.4 Available now NovaSeqX NovaSeq 6000 DRAGEN scRNA 4.4.2 4.4.6 Available Now NextSeq 1000/2000 DRAGEN Amplicon 3.8.4, 3.10.4, 4.2.7 4.3.13-1 Available Now NextSeq 1000/2000 DRAGEN Enrichment 3.5.6, 3.5.8, 3.7.4, 3.8.4, 3.10.4 4.2.7-1 Available Now NextSeq 1000/2000 DRAGEN Germline 3.5.6, 3.5.8, 3.7.4, 3.8.4, 3.10.4 4.2.7-1 Available Now NextSeq 1000/2000 DRAGEN RNA 3.5.6, 3.5.8, 3.7.4, 3.8.4, 3.10.4 4.2.7-1 Available Now NextSeq 1000/2000 DRAGEN scRNA 3.7.4, 3.8.4, 3.10.4, 4.2.7, 4.4.2 4.4.6 Available Now NextSeq 1000/2000 Nanostring GeoMx NGS 2.0.21 Nanostring GeoMx NGS 4.2.11 Local Instrument App Available now NextSeq 1000/2000 COVIDSeq Test 1.3.0-RUO DRAGEN Microbial Amplicon 1.1.0 BSSH Cloud App Available now MiSeq i100 LibraryQC 4.3.13 4.4.6 Available Now MiSeq i100 Small WGS 4.3.13 4.4.6 Available Now MiSeq i100 DME+ 4.3.13 4.4.6 Available Now NextSeq 500/550 iSeq, MiniSeq, MiSeq Amplicon for ICA 4.2.7 4.3.13-1 Available Now **Recommended Transition Paths for BCL Convert Cloud Analysis Auto-Launch Pipelines** Instrument Pipeline Name Current Version(s) New Version Availability (2025) NextSeq1000/2000 BCL Convert 3.5.6, 3.5.8, 3.7.4, 3.8.4, 3.10.4 4.3.13 Available now NovaSeq 6000 BCL Convert 3.8.4 4.3.13 Available now NextSeq1000/2000 NovaSeq 6000 BCL Convert for ICA 3.9.5 4.1.5 Available now **DRAGEN cloud analysis auto-launch apps** _**not**_ **impacted by either infrastructure change – No change required by customer** Pipeline Name Current Version(s) Instrument(s) BCL Convert 3.10.9, 4.1.5, 4.1.7, 4.1.23, 4.2.7, 4.3.13, 4.3.16 All platforms BCL Convert for ICA 4.1.5 NextSeq 1000/2000, NextSeq 500/550, NovaSeq 6000 Germline Enrichment 4.4.4 NextSeq 1000/2000, NovaSeq 6000, NovaSeqX Heme WGS 4.4.4 NovaSeq 6000, NovaSeqX Protein Quantification 1.8.33, 2.0.0, 2.1.0, 2.2.2 NovaSeq 6000, NovaSeqX Spatial Transcriptome 1.0.0 NovaSeq 6000, NovaSeqX [hashtag](https://help.dragen.illumina.com/reference/eol-transition#additional-dragen-bssh-ica-end-of-life-notifications) Additional DRAGEN BSSH/ICA End of Life Notifications ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The following apps have been scheduled for end of life unrelated to F-instance transition. Applications to be made Obsolete Alternative Application Date of Last Support DRAGEN Covid Lineage - All versions DRAGEN Microbial Amplicon v1.1.0 December 1, 2025 DRAGEN Targeted Microbial - All versions DRAGEN Microbial Amplicon v1.1.0 December 1, 2025 DRAGEN ICLR WGS v1.1, v2.0.6 - BSSH App Constellation Mapped Reads (planned 2026) December 1, 2025 DRAGEN ICLR WGS v2.0.6 - ICA Pipeline Constellation Mapped Reads (planned 2026) December 1, 2025 DRAGEN ICLR Enrichment v2.0.6 - BSSH App Constellation Mapped Reads (planned 2026) December 1, 2025 DRAGEN ICLR Enrichment v2.0.6 - ICA Pipeline Constellation Mapped Reads (planned 2026) December 1, 2025 [PreviousSupplementary Informationchevron-left](https://help.dragen.illumina.com/reference/additional-resources) [NextF2 Validationchevron-right](https://help.dragen.illumina.com/reference/f2-validation) Last updated 20 days ago Was this helpful? * [DRAGEN Secondary Analysis - End of Life Roadmap](https://help.dragen.illumina.com/reference/eol-transition#dragen-secondary-analysis-end-of-life-roadmap) * [DRAGEN BSSH/ICA Workflows - End of Life Roadmap](https://help.dragen.illumina.com/reference/eol-transition#dragen-bssh-ica-workflows-end-of-life-roadmap) * [BCL Convert BSSH Workflow End of Life Roadmap](https://help.dragen.illumina.com/reference/eol-transition#bcl-convert-bssh-workflow-end-of-life-roadmap) * [DRAGEN BSSH Run Planning Workflow End of Life Notifications](https://help.dragen.illumina.com/reference/eol-transition#dragen-bssh-run-planning-workflow-end-of-life-notifications) * [Additional DRAGEN BSSH/ICA End of Life Notifications](https://help.dragen.illumina.com/reference/eol-transition#additional-dragen-bssh-ica-end-of-life-notifications) Was this helpful? --- # DRAGEN Secondary Analysis | DRAGEN v4.3 | DRAGEN The DRAGEN secondary analysis software utilizes a highly reconfigurable Field Programmable Gate Array (FPGA) card and is available on a preconfigured DRAGEN server that can be seamlessly integrated into bioinformatics workflows. The platform can be loaded with highly optimized algorithms for many different NGS secondary analysis pipelines, including the following: * Whole genome * Exome * RNA-Seq * Methylome * Cancer All user interaction is accomplished via DRAGEN software that runs on the host server and manages all communication with the FPGA card. This user guide summarizes the technical aspects of the system and provides detailed information for all DRAGEN command line options. If you are working with DRAGEN for the first time, Illumina recommends that you first read the _Getting Started section_, which provides a short introduction to DRAGEN, including running a test of the server, generating a reference genome, and running example commands. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software/dragen-platform#dna-pipeline) DNA Pipeline ----------------------------------------------------------------------------------------------------------------------------------------------------- DRAGEN DNA Pipeline ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3660538983-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FpFn0gLe5V859SXejQNAW%252Fuploads%252Fgit-blob-2e515ef08d8202f152485297d4a5ec4d2ab1f5b0%252Fdragen-platform.DNA_Pipeline.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=22841b39&sv=2) The DRAGEN DNA Pipeline massively accelerates the secondary analysis of NGS data. For example, the time taken to process an entire human genome at 30x coverage is reduced from approximately 10 hours (using the current industry standard, BWA-MEM+GATK-HC software) to approximately 20 minutes. Time scales linearly with coverage depth. These pipelines harness the tremendous power of the DRAGEN server and include highly optimized algorithms for mapping, aligning, sorting, duplicate marking, and haplotype variant calling. They also use platform features such as hardware-accelerated compression and optimized BCL conversion, together with the full set of platform tools. Unlike all other secondary analysis methods, DRAGEN DNA Applications do not reduce accuracy to achieve speed improvements. Accuracy for both SNPs and INDELs is improved over that of BWA-MEM+GATK-HC in side-by-side comparisons. In addition to haplotype variant calling, the pipeline supports calling of copy number and structural variants as well as detection of repeat expansions. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software/dragen-platform#rna-pipeline) RNA Pipeline ----------------------------------------------------------------------------------------------------------------------------------------------------- DRAGEN secondary anaylsis includes an RNA-seq (splicing-aware) aligner, as well as RNA-specific analysis components for gene expression quantification and gene fusion detection. ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3660538983-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FpFn0gLe5V859SXejQNAW%252Fuploads%252Fgit-blob-b7f556bf8783fff0e54fd33c48677f50efe302dd%252Fdragen-platform.RNA_Pipeline.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=796f9dd8&sv=2) The DRAGEN RNA Pipeline shares many components with the DNA Pipeline. Mapping of short seed sequences from RNA-Seq reads is performed similarly to mapping DNA reads. In addition, splice junctions (the joining of noncontiguous exons in RNA transcripts) near the mapped seeds are detected and incorporated into the full read alignments. DRAGEN secondary analysis uses hardware accelerated algorithms to map and align RNA-Seq--based reads faster and more accurately than popular software tools. For instance, it can align 100 million paired-end RNA-Seq--based reads in about three minutes. With simulated benchmark RNA-Seq data sets, its splice junction sensitivity and specificity are unsurpassed. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software/dragen-platform#methylation-pipeline) Methylation Pipeline --------------------------------------------------------------------------------------------------------------------------------------------------------------------- The DRAGEN Methylation Pipeline provides support for automating the processing of bisulfite sequencing data to generate a BAM with the tags required for methylation analysis and reports detailing the locations with methylated cytosines. [PreviousDRAGEN Host Softwarechevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software) [NextDRAGEN Reference Supportchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support) Last updated 7 months ago Was this helpful? * [DNA Pipeline](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software/dragen-platform#dna-pipeline) * [RNA Pipeline](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software/dragen-platform#rna-pipeline) * [Methylation Pipeline](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software/dragen-platform#methylation-pipeline) Was this helpful? --- # Resource Files | DRAGEN v4.3 | DRAGEN [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/resource-files#overview) Overview ------------------------------------------------------------------------------------------------------- The following sub-pages contain more information about resource files used by the software. [PreviousSupportchevron-left](https://help.dragen.illumina.com/dragen-v4.3/reference/technical-assistance) [NextNoise Baselineschevron-right](https://help.dragen.illumina.com/dragen-v4.3/reference/resource-files/prebuilt-baseline-files) Last updated 7 months ago Was this helpful? Was this helpful? --- # Revision History | DRAGEN v4.3 | DRAGEN Revision history of the DRAGEN product documentation Version Date Description of Change 05 May 2025 Add v4.4 user guide 04 Feb 2025 Minor updates, errata and clarifications. 03 Dec 2024 Add v4.4 pre-release information for single cell. 02 Sept 2024 Minor updates, errata and clarifications. 01 May 2024 Initial release. [PreviousRelease Noteschevron-left](https://help.dragen.illumina.com/dragen-v4.3/reference/release-notes-readme) Last updated 7 months ago Was this helpful? Was this helpful? --- # DRAGEN Multi-Cloud | DRAGEN v4.3 | DRAGEN [DRAGEN on AWSchevron-right](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws) [DRAGEN on AWS Batchchevron-right](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws-batch) [DRAGEN on Microsoft Azurechevron-right](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-azure) [DRAGEN on Microsoft Azure Batchchevron-right](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch) [PreviousDRAGEN Serverchevron-left](https://help.dragen.illumina.com/dragen-v4.3/reference/platform-maintenance) [NextDRAGEN on AWSchevron-right](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws) Last updated 7 months ago Was this helpful? Was this helpful? --- # Troubleshooting | DRAGEN v4.3 | DRAGEN If the DRAGEN system does not seem to be responding, do the following: 1. To determine if the DRAGEN system is hanging, follow the instructions in [How to Determine if the System is Hanging](https://help.dragen.illumina.com/dragen-v4.3/reference/troubleshooting#how-to-determine-if-the-system-is-hanging) . 2. Collect diagnostic information after a hang, or a crash, as described in [Sending Diagnostic Information to Illumina Support](https://help.dragen.illumina.com/dragen-v4.3/reference/troubleshooting#sending-diagnostic-information-to-illumina-support) . 3. After all information has been collected, reset your system. if needed, as described in [Resetting Your System after a Crash or Hang](https://help.dragen.illumina.com/dragen-v4.3/reference/troubleshooting#resetting-your-system-after-a-crash-or-hang) . [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/troubleshooting#how-to-determine-if-the-system-is-hanging) How to Determine if the System is Hanging -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The DRAGEN system has a watchdog to monitor the system for hangs. If a run seems to be taking longer than it should, the watchdog may not be detecting the hang. Here are some things to try: * Run the top command to find the active DRAGEN process. If your run is healthy, you should expect to see it consuming over 100% of the CPU. If it is consuming 100% or less, then your system may be hanging. * Run the du -s command in the directory of the output BAM/SAM file. During a normal run, this directory should be growing with either intermediate output data (when sort is enabled) or BAM/SAM data. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/troubleshooting#sending-diagnostic-information-to-illumina-support) Sending Diagnostic Information to Illumina Support -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Illumina would like your feedback on your DRAGEN system, including any reports of system malfunction. In the event of a crash, hang, or watchdog fault, run the sos report command to collect diagnostic and configuration information, as follows: `sudo sos report --batch --tmp-dir /staging/tmp` This command takes several minutes to execute and reports the location where it has saved the diagnostic information in /staging/tmp. Please include the report when you submit a support ticket for Illumina Technical Support. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/troubleshooting#resetting-your-system-after-a-crash-or-hang) Resetting Your System after a Crash or Hang ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ If the DRAGEN system crashes or hangs, the dragen\_reset utility must be run to reinitialize the hardware and software. This utility is automatically executed by the host software any time it detects an unexpected condition. In this case, the host software shows the following message: `Running dragen_reset to reset DRAGEN Bio-IT processor and software` If the software is hanging, please collect diagnostic information as described in subsection \[Sending Diagnostic Information to Illumina Support\]{.underline} and then execute dragen\_reset manually, as follows: `/bin/dragen_reset` Any execution of dragen\_reset requires the reference genome to be reloaded to the DRAGEN board. The host software automatically reloads the reference on the next execution. [PreviousF2 Validationchevron-left](https://help.dragen.illumina.com/dragen-v4.3/reference/f2-validation) [NextCiting DRAGEN softwarechevron-right](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen) Last updated 4 months ago Was this helpful? * [How to Determine if the System is Hanging](https://help.dragen.illumina.com/dragen-v4.3/reference/troubleshooting#how-to-determine-if-the-system-is-hanging) * [Sending Diagnostic Information to Illumina Support](https://help.dragen.illumina.com/dragen-v4.3/reference/troubleshooting#sending-diagnostic-information-to-illumina-support) * [Resetting Your System after a Crash or Hang](https://help.dragen.illumina.com/dragen-v4.3/reference/troubleshooting#resetting-your-system-after-a-crash-or-hang) Was this helpful? --- # DRAGEN Server | DRAGEN v4.3 | DRAGEN [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/platform-maintenance#server-specifications) Server Specifications --------------------------------------------------------------------------------------------------------------------------------------- Component DRAGEN V4 server CPU Dual Intel Xeon Gold 6226R 2.9Ghz. 32 Cores, 64 Threads Memory 512GB Scratch Drive 2x 7.68TB NVMe OS Drives 2x 480 GB SSD (RAID 1) FPGA Card DRAGEN Form Factor 2U Dimensions H 8.8cm (3.5in), W 43.8cm (17.2in), D 76.4cm (29.9in) Power Supply 1968W Dual, Hotswap redundant power supply [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/platform-maintenance#system-installation) System Installation ----------------------------------------------------------------------------------------------------------------------------------- See the [Site Prep and Installation Guidearrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/documentation/software_documentation/dragen-bio-it/200015717_03_dragen-server-v4-site-prep-and-installation-guide.pdf) for more information. [![Illumina DRAGENᵀᴹ Server v4 - Unboxing, assembly and installation](https://help.dragen.illumina.com/~gitbook/image?url=http%3A%2F%2Fimg.youtube.com%2Fvi%2FSo-kWUCBvQ0%2F0.jpg&width=300&dpr=3&quality=100&sign=8889aa3&sv=2)arrow-up-right](https://www.youtube.com/watch?v=So-kWUCBvQ0) _Illumina DRAGENᵀᴹ Server v4 - Unboxing, assembly and installation_ [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/platform-maintenance#system-updates) System Updates ------------------------------------------------------------------------------------------------------------------------- Any software on the system, including the kernel, can be updated. **Note**: Please pay special attention steps which backup the License files to avoid any disruptions. See [OS Upgrade Instructionsarrow-up-right](https://knowledge.illumina.com/software/on-premises-software/software-on-premises-software-reference_material-list/000007439) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/platform-maintenance#os-image-and-kernel-patches) OS Image and Kernel patches Illumina strongly recommends the following guidelines: * Kernel packages should come from official Oracle8 updates only. * Using experimental kernels or compiling the kernel from source is not recommended. * Use OS Images provided by Illumina. * Keep up to date with security patches from Illumina. See [Linux-based OS Security Patchesarrow-up-right](https://support.illumina.com/support-content/os-patches.html) * Run the [System Checkarrow-up-right](https://github.com/illumina-swi/dragen-docs/blob/release/4.3-prod/user-guide/dragen-platform/getting-started.md#running-the-system-check) after system updates. [PreviousTools and Utilitieschevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/tools-and-utilities) [NextDRAGEN Multi-Cloudchevron-right](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud) Last updated 7 months ago Was this helpful? * [Server Specifications](https://help.dragen.illumina.com/dragen-v4.3/reference/platform-maintenance#server-specifications) * [System Installation](https://help.dragen.illumina.com/dragen-v4.3/reference/platform-maintenance#system-installation) * [System Updates](https://help.dragen.illumina.com/dragen-v4.3/reference/platform-maintenance#system-updates) * [OS Image and Kernel patches](https://help.dragen.illumina.com/dragen-v4.3/reference/platform-maintenance#os-image-and-kernel-patches) Was this helpful? --- # Getting Started | DRAGEN v4.3 | DRAGEN DRAGEN provides tests you can run to make sure that your DRAGEN system is properly installed and configured. Before running the tests, make sure that the DRAGEN server has adequate power and cooling, and is connected to a network that is fast enough to move your data to and from the machine with adequate performance. Please refer to the [Server Site Prep & Installation Guidearrow-up-right](https://support.illumina.com/downloads/illumina-dragen-server-site-prep-guide.html) when installing a new system. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started#on-premises-installation) On-premises Installation -------------------------------------------------------------------------------------------------------------------------------------------------------- The software can be installed on an on-premises server by executing the .run installer for the desired version. Installers are made available for all releases at the [DRAGEN Software Support Site pagearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/downloads.html) . Installation procedure: * Download the desired installer from the support website and unzip the package * The archive integrity can be checked using: `./ --check` * Install the appropriate release based on your Linux OS with the command: `sudo sh ` The .run file includes a script that administers un-installation of an existing software, integrity checking of the package and files, installation of the new DRAGEN software version. The DRAGEN software is installed in part by use of the Linux RPM Package Manager (rpm). Several rpm packages comprise the installation of a single DRAGEN software version. The RPM packages also configure the system for dragen, like raised user `ulimits`, and the .run script starts services needed for functionality, such as the Licensing daemon `dragen_licd`, and the hugepages daemon, `dragend_hp`. > NOTE: Root privileges are required for the installation. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started#single-version-installation) Single Version Installation Up to DRAGEN Software v4.2, only one version of the DRAGEN software can be installed at a time. Executing the .run file will remove any existing installed version and (re)install the new version. After installation, the application and associated files are available at `/opt/edico`. The single version installer will add `/opt/edico` to the Linux $PATH, so that the user can just call `dragen` without specifying the full path. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started#multi-version-installation) Multi-Version Installation Starting with DRAGEN Software v4.3 and later, multiple compatible versions of the DRAGEN software can be installed at a time. Executing the .run file will add the new version to the system. After installation, the application files are available at `/opt/dragen/{version}` and FPGA files are located at `/opt/bitstream/{bitstream version}`. The multi-version installer will NOT add `/opt/dragen/{version}` to the Linux $PATH, since multiple versions can be present at a given time. User should manage the desired paths to the specific version they want to run. When this guide provides command line examples, it will assume that the Linux $PATH is set to correct dragen version, and we will just refer to `dragen ` Notes on multi-version installation: * Installers released for DRAGEN v4.2 and earlier are single version packages * Single version packages and multi-version packages can not be mixed * Installation of a prior single version package will remove all the multi-version packages * Installation of a multi-version package will remove any installed single version package * After installing a multi-version package, see a list of installed versions at any time by running `/usr/bin/dragen_versions` * To remove any multi-version package, call `yum remove` on its Path Example: ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started#location-of-dragen-and-resource-files) Location of `dragen` and resource files DRAGEN Version on-premises server cloud instance 4.3 and later `/opt/dragen/{version}` `/opt/edico/` 4.2 and earlier `/opt/edico/` `/opt/edico/` Throughout this guide we will refer to `` which will be either of the locations above [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started#licensing) Licensing -------------------------------------------------------------------------------------------------------------------------- DRAGEN requires license(s) for most functionality, please refer to the [Licensing Reference Section](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing) for guidance on how to install and/or review your current licenses. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started#running-the-system-check) Running the System Check -------------------------------------------------------------------------------------------------------------------------------------------------------- After turning on the server, you can make sure that your DRAGEN server is functioning properly by running `/self_test/self_test.sh`, which does the following: * Automatically indexes chromosome M from the hg19 reference genome * Loads the reference genome and index * Maps and aligns a set of reads * Saves the aligned reads in a BAM file * Asserts that the alignments exactly match the expected results Each server ships with the test input FASTQ data for this script, which is located in `/self_test`. The system check takes approximately 25--30 minutes. The following example shows how to run the script and shows the output from a successful test. If the output BAM file does not match expected results, then the last line of the above text is as follows: `SELF TEST RESULT : FAIL` If you experience a FAIL result after running this test script immediately after turning on your DRAGEN server, contact Illumina Technical Support. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started#running-your-own-test) Running Your Own Test -------------------------------------------------------------------------------------------------------------------------------------------------- When you are satisfied that your DRAGEN system is performing as expected, you are ready to run some of your own data through the machine, as follows: * Load the reference table for the reference genome * Determine location of input and output files * Process input data ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started#loading-the-reference-genome) Loading the Reference Genome Before a reference genome can be used with DRAGEN, it must be converted from FASTA format into a custom binary format for use with the DRAGEN hardware. For more information, see [Prepare a Reference Genome](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome) . The reference hash table specified on the command line is automatically loaded onto the board the first time you process data with a pipeline. You can manually load the hash table for your reference genome by using the following command: `dragen -r ` Make sure that the reference hash table directory is on the fast file IO drive. The default location for the hash table for hg19 is as follows. `/staging/human/reference/hg19/hg19.fa.k_21.f_16.m_149` The command to load reference genome hg19 from the default location is as follows. `dragen -r /staging/human/reference/hg19/hg19.fa.k_21.f_16.m_149` This command loads the binary reference genome into memory on the DRAGEN board, where it is used for processing any number of input data sets. You do not need to reload the reference genome unless you restart the system or need to switch to a different reference genome. It can take up to a minute to load a reference genome. DRAGEN checks whether the specified reference genome is already resident on the board. If it is, then the upload of the reference genome is automatically skipped. You can force reloading of the same reference genome using the `force-load-reference (-l)` command line option. The command to load the reference genome prints the software and hardware versions to standard output. For example: After the reference genome has been loaded, the following message is printed to standard output: ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started#determine-input-and-output-file-locations) Determine Input and Output File Locations The DRAGEN Pipeline is very fast, which requires careful planning for the locations of the input and output files. If the input or output files are on a slow file system, then the overall performance of the system is limited by the throughput of that file system. It is recommended that inputs and outputs are streamed directly from/to a mounted external storage system. The DRAGEN system is preconfigured with at least one fast file system consisting of a set of fast SSD disks grouped with RAID-0 for performance. This file system is mounted at `/staging`. This name was chosen to emphasize the fact that this area was built to be large and fast, but is not redundant. Failure of any of the file system's constituent disks leads to the loss of all data stored there. During processing, DRAGEN generates and reads back temporary files. With DRAGEN, it is highly recommended to always direct temporary files to the fast SSD (or `/staging`) by using the `--intermediate-results-dir` option. If the `--intermediate-results-dir` option is not provided, temporary files are written to the `--output-directory`. DRAGEN recommends streaming inputs and outputs using an mounted external storage system. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started#process-your-input-data) Process Your Input Data To analyze FASTQ data, use the dragen command. For example, the following command can be used to analyze a single-ended FASTQ file: For detailed information on the command line options, see [DRAGEN Host Software](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software) . For recommended command lines in typical use cases, see [DRAGEN Recipes](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes) . [PreviousDRAGEN v4.3chevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3) [NextDRAGEN Host Softwarechevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software) Last updated 7 months ago Was this helpful? * [On-premises Installation](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started#on-premises-installation) * [Single Version Installation](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started#single-version-installation) * [Multi-Version Installation](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started#multi-version-installation) * [Location of dragen and resource files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started#location-of-dragen-and-resource-files) * [Licensing](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started#licensing) * [Running the System Check](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started#running-the-system-check) * [Running Your Own Test](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started#running-your-own-test) * [Loading the Reference Genome](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started#loading-the-reference-genome) * [Determine Input and Output File Locations](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started#determine-input-and-output-file-locations) * [Process Your Input Data](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started#process-your-input-data) Was this helpful? Copy $ dragen_versions The output format of this command may change. Use --json for machine readable output. Dragen Version Size (MB) Install Date Path 4.3.2 1378.03 2024-03-10 18:26:17 /opt/dragen/4.3.2 4.4.3 1381.41 2024-03-18 20:56:39 /opt/dragen/4.4.3 4.3.5 1379.25 2024-03-11 15:20:24 /opt/dragen/4.3.5 Bitstream Version Size (MB) Install Date Path 07.031.732 (0x18101306) 598.95 2024-03-10 18:26:03 /opt/bitstream/07.031.732 07.031.745 (0x18101306) 598.95 2024-03-18 20:56:18 /opt/bitstream/07.031.745 To remove a dragen version, call `yum remove` on its Path. Copy $ /opt/dragen/4.3.4/self_test/self_test.sh ############################################################# Logging to /var/log/dragen/self_test.1714627157_160164.0.details.log Using dragen executables in /opt/dragen/4.3.4/bin Using board(s): 0 ############################################################# Running tests for board 0 (u200) Using scratch directory /tmp/self_test.4BO0pfPST9/0 ------------------------------------------------------------- Board 0 test 1, FPGA MEMORY TEST Loading DIAG bitstream Running fpga memory test, this will take ~13 minutes Board 0 test 1, FPGA MEMORY TEST: PASS ------------------------------------------------------------- Board 0 test 2, BAR REGISTER ACCESS Board 0 test 2, BAR REGISTER ACCESS: PASS ------------------------------------------------------------- Board 0 test 3, FPGA TEMP REG ACCESS FPGA Temperature: 27C (Max Temp: 36C, Min Temp: 22C) Board 0 test 3, FPGA TEMP REG ACCESS: PASS ------------------------------------------------------------- Board 0 test 4, BOARD SERIAL # REG ACCESS Serial Number: 2130069BM05V Board 0 test 4, BOARD SERIAL # REG ACCESS: PASS ------------------------------------------------------------- Board 0 test 5, DRAGEN GENOME LICENSE Board 0 test 5, DRAGEN GENOME LICENSE: PASS ------------------------------------------------------------- Board 0 test 6, CPLD DATE TEST cpld date is n/a Board 0 test 6, CPLD DATE TEST: PASS ------------------------------------------------------------- Board 0 test 7, ENCRYPTION KEY EXISTENCE TEST Board 0 test 7, ENCRYPTION KEY EXISTENCE TEST: PASS ------------------------------------------------------------- Board 0 test 8, PARTIAL RECONFIGURATION DNA-MAPPER: ok RNA-MAPPER: ok HMM: ok ZIP: ok UNZIP: ok DIAG: ok Board 0 test 8, PARTIAL RECONFIGURATION: PASS ------------------------------------------------------------- Board 0 test 9, HASH TABLE GENERATION Board 0 test 9, HASH TABLE GENERATION: PASS ------------------------------------------------------------- Board 0 test 10, MAP AND ALIGNER running mapper aligner: ok unmapped input records percentages: ok md5sum check dbam sorted: pass Board 0 test 10, MAP AND ALIGNER: PASS ------------------------------------------------------------- Board 0 test 11, VARIANT CALLER E2E running variant caller: ok md5sum check dbam sorted: ok md5sum check VCF: ok Board 0 test 11, VARIANT CALLER E2E: PASS ############################################################# SELF TEST COMPLETED SELF TEST RESULT : PASS ############################################################# Log file at /var/log/dragen/self_test.1714627157_160164.0.details.log Copy DRAGEN Host Software Version 01.001.035.01.00.30.6682 and Bio-IT Processor Version 0x1001036 Copy DRAGEN finished normally Copy dragen \ -r /staging/human/reference/hg19/hg19.fa.k_21.f_16.m_149 \ -1 /staging/test/data/SRA056922.fastq \ --output-directory /staging/test/output \ --output-file-prefix SRA056922_dragen \ --RGID DRAGEN_RGID \ --RGSM DRAGEN_RGSM --- # Supplementary Information | DRAGEN v4.3 | DRAGEN Resource Description [Product Pagearrow-up-right](https://www.illumina.com/products/by-type/informatics-products/dragen-secondary-analysis.html) Product Overview and sales information [Software Download Sitearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/downloads.html) DRAGEN and DRAGEN On-Prem App Downloads and supporting documentation [DRAGEN Product Files Sitearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) DRAGEN Reference and Resource Files [Support Site Pagearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform.html) Documentation, FAQs, Training [Research Articlesarrow-up-right](https://www.illumina.com/science/genomics-research/articles.html) Genomics articles highlighting breakthroughs and advances in bioinformatics and clinical research from Illumina scientists and thought leaders [Resources for Population Genomicsarrow-up-right](https://developer.illumina.com/dragen/dragen-popgen) An informational page to get the latest DRAGEN pipeline versions and command lines used in large population genomics programs. [DRAGEN v4.3 Webinar Slidesarrow-up-right](https://www.illumina.com/content/dam/illumina-marketing/emailers/2025/DRAGEN%20v4.3%20webinar.pdf) An overview of changes and new features released with DRAGEN Secondary Analysis v4.3. [PreviousNoise Baselineschevron-left](https://help.dragen.illumina.com/dragen-v4.3/reference/resource-files/prebuilt-baseline-files) [NextDRAGEN Product Obsolescence Noticeschevron-right](https://help.dragen.illumina.com/dragen-v4.3/reference/eol-transition) Last updated 7 months ago Was this helpful? Was this helpful? --- # Deployment Options | DRAGEN DRAGEN analysis is available on multiple platforms. Platform Description DRAGEN on-premises server DRAGEN on-premises server offers highly accurate secondary analysis in a fraction of time compared with a traditional CPU-based system. - Analyze and store data locally - Supports varying levels of command line interface - Replace up to 30 traditional compute instances - Fully process a 34× whole human genome in ~30 minutes. _(1)_ - One unit supports two NovaSeq 6000 Systems running at full capacity DRAGEN analysis on Illumina Connected Analytics Couples the accuracy and speed of the DRAGEN with the ability to customize analysis pipeline to operationalize informatics on a secure platform. DRAGEN on BaseSpace Sequence Hub (BSSH) Push button analysis capability in an intuitive, easy-to-use interface with compliance, and storage features of BaseSpace Sequence Hub and Amazon Web Services (AWS). DRAGEN onboard NovaSeq X Series \- Flexibly runs multiple secondary analysis pipelines in parallel. - Performs up to four simultaneous applications per flow cell in a single run. - Brings up to 5x lossless data compression, and analysis with supported applications - Provides savings on analysis, which over five years can exceed the price of the sequencer DRAGEN onboard NextSeq 1000 and NextSeq 2000 Systems \- Provides access to select DRAGEN analysis informatics pipelines - Enables users to generate results in as little as two hours - Uses intuitive pipeline algorithms to reduce reliance on external informatics experts DRAGEN onboard MiSeq i100 Series Intuitive, ultra-rapid analysis including DRAGEN BCL convert, DRAGEN Library QC, DRAGEN small WGS and DRAGEN Microbial Enrichment Plus. - Rapid results with comprehensive secondary analysis generated in two hours or less _(2)_ - Highly efficient workflow with a single user touchpoint to VCF and/or html report and no intermediate file transfers - Exceptionally easy with an intuitive interface for non-expert users DRAGEN on AWS, Azure DRAGEN supports the FPGA enabled instance types of AWS, Azure. Rpm installers and the Kernel driver can be installed on images managed by the user, and DRAGEN can be run by purchasing a license. DRAGEN on AWS and Azure Marketplace Pre-configured Amazon Machine Images (AMI) and Azure Virtual Machines with DRAGEN installed can be accessed from the respective marketplace offerings in a Pay-As-You-Use model. DRAGEN on GCP DRAGEN is made available on the Google Cloud Platform. Pre-configured instances with DRAGEN installed can be accessed through the GCP application interface. Limited availability. Please reach out to your Illumina representative for access. > (1) HG002 from PrecisionFDA truth challenge V2 run with DRAGEN analysis v4.0 on DRAGEN server v4, all callers > (2) When run according to sample recommendations [PreviousDRAGEN Applicationschevron-left](https://help.dragen.illumina.com/overview/key-applications) [NextDRAGEN v4.5chevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5) Last updated 7 months ago Was this helpful? Was this helpful? --- # DRAGEN Reference Support | DRAGEN v4.3 | DRAGEN DRAGEN supports the construction of reference hash tables for both human and non-human reference genomes. The reference autodetect feature of DRAGEN is able to recognize the reference hash tables build on the four Human reference genomes: hg19 (`hg19`), GRCh37/hs37d5 (`hs37d5`), GRCh38/hs38d1(`hg38`), and T2T-CHM13v2.0 (`chm13`). DRAGEN supports pangenome reference hash tables which extend the reference genomes with alternative variant paths from a sample cohort used to construct the pangenome reference. A pangenome-based reference improves the mapping accuracy of Illumina reads in the “Difficult-to-Map Regions” of the genome and the downstream variant calling. Pre-built human references are available for download at [DRAGEN Software Support Site pagearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) . The pangenome is the recommended for Germline human analyses. The accuracy achieved with pangenome references are highlighted in the plot below. ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3660538983-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FpFn0gLe5V859SXejQNAW%252Fuploads%252Fgit-blob-daf64b84d6ea5298bdd158ccfcd5b3e661b772e9%252Fpangenome_v_linear_accuracy.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=c123bbce&sv=2) In the following tables we summarize the reference support for each DRAGEN component and the recommended reference type for each component. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support#germline) Germline Component Recommended Human Reference Type Recommended Non-Human Reference Type Human hg19 Human hs37d5 Human hg38 Human chm13 Non-Human SNV Pangenome Linear Yes Yes Yes Yes Yes CNV Pangenome Linear Yes Yes Yes Yes\* No SV Pangenome Linear Yes Yes Yes Yes\* Yes Expansion Hunter Pangenome Linear Yes Yes Yes No No Targeted Callers Pangenome Linear Yes Yes Yes No No RNA Linear Linear Yes Yes Yes Yes\* Yes De Novo Pangenome Linear Yes Yes Yes Yes\* Yes Joint Genotyping Pangenome Linear Yes Yes Yes Yes\* Yes Biomarkers (HLA) Pangenome Linear Yes Yes Yes Yes\* No gVCF genotyper Pangenome Linear Yes Yes Yes Yes\* Yes ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support#somatic) Somatic Component Recommended Human Reference Type Recommended Non-Human Reference Type Human hg19 Human hs37d5 Human hg38 Human chm13 Non-Human SNV Linear Linear Yes Yes Yes Yes\* No UMI SNV Linear Linear Yes Yes Yes Yes\* No CNV Linear Linear Yes Yes Yes Yes\* No SV Linear Linear Yes Yes Yes Yes\* No ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support#methylation) Methylation Component Recommended Human Reference Type Recommended Non-Human Reference Type Human hg19 Human hs37d5 Human hg38 Human chm13 Non-Human Methylation Linear Linear Yes Yes Yes No No ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support#annotation) Annotation Component Recommended Human Reference Type Recommended Non-Human Reference Type Human hg19 Human hs37d5 Human hg38 Human chm13 Non-Human Nirvana Pangenome Linear Yes Yes Yes No Yes \* DRAGEN supports the component execution, however the component's accuracy has not been established. See [Prepare a Reference Genome](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome) for how to build a custom reference genome. [PreviousDRAGEN Secondary Analysischevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software/dragen-platform) [NextPrepare a Reference Genomechevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome) Last updated 7 months ago Was this helpful? * [Germline](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support#germline) * [Somatic](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support#somatic) * [Methylation](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support#methylation) * [Annotation](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support#annotation) Was this helpful? --- # Release Notes | DRAGEN v4.3 | DRAGEN Release notes for prior DRAGEN versions DRAGEN v4.4 * [DRAGEN v4.4.7 Release Notesarrow-up-right](https://www.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200077195_00_DRAGEN_4_4_7_Customer_Release_Notes.pdf) * [DRAGEN v4.4.6 Release Notesarrow-up-right](https://www.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200074412_00_DRAGEN_4_4_6_Customer_Release_Notes.pdf) * [DRAGEN v4.4.4 Release Notesarrow-up-right](https://www.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200068065_00_DRAGEN-4_4_4-Customer-Release-Notes.pdf) DRAGEN v4.3 * [DRAGEN v4.3.17 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200071549_00_DRAGEN_4_3_17_Customer_Release_Notes.pdf) * [DRAGEN v4.3.16 Release Notesarrow-up-right](https://www.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200067026_00_DRAGEN-4.3.16-Customer-Release-Notes.pdf) * [DRAGEN v4.3.13 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200063347_00_DRAGEN_v4.3.13_Customer_Release_Notes.pdf) * [DRAGEN v4.3.6 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200056923_00_DRAGEN_4_3_6_Customer-Release-Notes.pdf) DRAGEN v4.2 * [DRAGEN v4.2.9 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200054960_00_DRAGEN_v4.2.9_CRN.pdf) * [DRAGEN v4.2.7 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200049372_00_DRAGEN_v4_2_7_CRN.pdf) * [DRAGEN v4.2.4 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200040845_01_DRAGEN-4.2-Customer-Release-Notes.pdf) DRAGEN v4.1 * [DRAGEN v4.1.23 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200046067_00_DRAGEN-4.1.23-Customer-Release-Notes.pdf) * [DRAGEN v4.1.7 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200040752_00_DRAGEN_v4.1.7_Customer_Release_Notes.pdf) * [DRAGEN v4.1.5 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200034017_00_DRAGEN-4.1.5-Customer-Release-Notes.pdf) DRAGEN v4.0 * [DRAGEN v4.0.5 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200037976_00_DRAGEN-4.0.5-Customer-Release-Notes.pdf) * [DRAGEN v4.0.3 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200024449_02_DRAGEN_4_0_3Customer_Release_Notes.pdf) DRAGEN v3.10 * [DRAGEN v3.10.17 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200054720_00_DRAGEN-3.10.17-Customer-Release-Notes.pdf) * [DRAGEN v3.10.16 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200045317_00_DRAGEN-3.10.16-Customer-Release-Notes.pdf) * [DRAGEN v3.10.12 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200031686_01_DRAGEN_3_10_12_Customer_Release_Notes.pdf) * [DRAGEN v3.10.11 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200031429_01_DRAGEN_3_10_11_Customer_Release_Notes.pdf) * [DRAGEN v3.10.10 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200026470_01_DRAGEN_v3_10_10_Customer_Release_Notes.pdf) * [DRAGEN v3.10.9 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200022616_01_DRAGEN_3_10_9_Customer_Release_Notes.pdf) * [DRAGEN v3.10.8 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200018949_01_DRAGEN_3_10_8_Customer_Release_Notes.pdf) * [DRAGEN v3.10.4 Release Notesarrow-up-right](https://support.illumina.com/content/dam/illumina-support/documents/downloads/software/dragen/release-notes/200016065_01_DRAGEN_3_10_4_Customer_Release_Notes.pdf) [PreviousDRAGEN Publicationschevron-left](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-publications) [NextRevision Historychevron-right](https://help.dragen.illumina.com/dragen-v4.3/reference/revision-history) Last updated 1 month ago Was this helpful? Was this helpful? --- # DRAGEN on AWS Batch | DRAGEN v4.3 | DRAGEN DRAGEN on AWS Batch deployment guide was created by Illumina in collaboration with Amazon Web Services (AWS). This helps people to deploy popular technologies on AWS according to AWS best practices. If you’re unfamiliar with AWS Batch stacks, refer to the [AWS Batch user guidearrow-up-right](https://docs.aws.amazon.com/batch/latest/userguide/what-is-batch.html) . This deployment guide provides instructions for deploying the Illumina DRAGEN in the AWS Cloud using AWS Batch. The AWS Batch is a fully managed service that simplifies the process of running and scaling batch computing workloads in AWS cloud. For those who prefer using an AWS EC2 virtual machine for DRAGEN analyses, please use the DRAGEN Complete Suite. Important: This template is provided as a starting point. Users are expected to tailor the cloudformation configuration, input data, and parameters to meet their specific workflow and requirements. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws-batch#requirements) Requirements To use DRAGEN on AWS Batch, the following are required: * Subscription to the DRAGEM AMI (marketplace or private) * s3 bucket * Quota for EC2 f1 or f2 instances ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws-batch#supported-regions) Supported Regions DRAGEN on AWS Batch is available with Marketplace AMIs in the following regions for f1 and f2 instances: * us-east-1 * us-west-2 * eu-central-1 * eu-west-1 * ap-southeast-2 For BYOL users using a private AMI, the appropriate AMI ID must be specified in the template for their region. Additionally, the SupportedRegionRule may need to be removed. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws-batch#deployment-steps) Deployment Steps 1. Clone the repository: [DRAGEN on AWS Batch deployment guidearrow-up-right](https://github.com/Illumina/dragen-aws-batch-quickstart) (include submodules as needed). 2. Modify the CloudFormation template to fit your needs (e.g., specify your private AMI). 3. Upload the modified templates to your S3 bucket, e.g., s3://your-bucket-name/DRAGEN-on-AWS-Batch/. 4. In the AWS Console, go to CloudFormation > Stacks > Create Stack, choose "With existing template" 5. provide the S3 URL * Create a new VPC and deploy DRAGEN on AWS Batch * `https://your-bucket-name.s3..amazonaws.com/DRAGEN-on-AWS-Batch/templates/dragen-main.template.yaml` * Deploy DRAGEN on AWS Batch in an existing VPC * `https://your-bucket-name.s3..amazonaws.com/DRAGEN-on-AWS-Batch/templates/dragen.template.yaml` 6. Configure stack settings: * Stack name: Provide a name for your stack * Availability Zones: select two AZs * Key pair: Use the key pair you want to use for SSH access to instances * Instance type: f2.6xlarge * Max vCPU: 24 * Genomics Data Bucket: s3://your-bucket-genomic-data/ * Quick Start S3 region: * Quick Start S3 Key Prefix: DRAGEN-on-AWS-Batch/ 7. Click Submit to launch the stack. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws-batch#simple-dragen-run-example-with-aws-batch) Simple DRAGEN Run Example with AWS Batch After successful deployment, you can initiate a sample run: Note: * Input files from the S3 bucket are automatically copied to the instance. * The job runs using local copies of the S3 input files along with the specified parameters. * Output files are first saved locally, then transferred to the designated S3 output folder. * Job status can be monitored via the AWS Batch console. * Logs are available in both CloudWatch and the S3 output folder. [PreviousDRAGEN on AWSchevron-left](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws) [NextDRAGEN on Microsoft Azurechevron-right](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-azure) Last updated 7 months ago Was this helpful? * [Requirements](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws-batch#requirements) * [Supported Regions](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws-batch#supported-regions) * [Deployment Steps](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws-batch#deployment-steps) * [Simple DRAGEN Run Example with AWS Batch](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws-batch#simple-dragen-run-example-with-aws-batch) Was this helpful? Copy cat > e2e-test.json << EOF { "jobName": "e2e-job", "jobQueue": "dragen-queue", "jobDefinition": "dragen", "containerOverrides": { "vcpus": 24, "memory": 240000, "command": [\ "-f", "-r", "s3://your-bucket-genomic-data/ref/hg38-alt_masked.cnv.graph.hla.methyl_cg.rna-11-r5.0-2.tar.gz",\ "-1", "s3://your-bucket-genomic-data/input/NA24385-AJ-Son-R1-NS_S33_L001_R1_001.fastq.gz",\ "-2", "s3://your-bucket-genomic-data/input/NA24385-AJ-Son-R1-NS_S33_L001_R2_001.fastq.gz",\ "--RGID", "1",\ "--RGSM", "HG002",\ "--enable-bam-indexing", "true",\ "--enable-map-align-output", "true",\ "--enable-sort", "true",\ "--output-file-prefix", "RGMS",\ "--enable-map-align", "true",\ "--output-format", "BAM",\ "--output-directory", "s3://your-bucket-genomic-data/output/",\ "--enable-variant-caller", "true",\ "--lic-server", https://:@license.dragen.illumina.com # requires for BYOL users\ ] }, "retryStrategy": { "attempts": 1 } } EOF aws batch submit-job --cli-input-json file://e2e-test.json --region --- # Citing DRAGEN software | DRAGEN v4.3 | DRAGEN There are two preferred methods to cite DRAGEN software. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#method-1-cite-dragen-secondary-analysis-software-in-text-or-in-reference-bibliography-list) Method 1: Cite DRAGEN secondary analysis software in-text or in reference/bibliography list --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#in-text) In-text Proper in-text citation for DRAGEN software must include the Illumina DRAGEN software product used and the version number at the time of data analysis. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#example) Example Secondary analysis was performed using Illumina DRAGEN software, v4.3. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#reference-or-bibliography-list) Reference or bibliography list Citing DRAGEN software in a bibliography or reference list should include the company name, copyright date, name of the DRAGEN software product, version number, format, and link to product website. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#example-1) Example Illumina (2024). DRAGEN secondary analysis (Version 4.3) \[Computer software\]. [https://www.illumina.com/products/by-type/informatics-products/dragen-secondary-analysis.htmlarrow-up-right](file:///Users/mdelrosar1/Library/CloudStorage/OneDrive-Illumina,Inc/Documents/22_Documentation/Gitbook/%2522) [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#method-2-cite-a-specific-algorithm-using-one-of-the-papers-listed-below) Method 2: Cite a specific algorithm using one of the papers listed below ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#dragen-germline-algorithms) DRAGEN Germline Algorithms Behera, S., Catreux, S., Rossi, M. _et al._, Comprehensive genome analysis and variant detection at scale using DRAGEN, _Nat Biotechnol_ (2024). [https://doi.org/10.1038/s41587-024-02382-1arrow-up-right](https://doi.org/10.1038/s41587-024-02382-1) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#germline-cnv-caller) Germline CNV Caller De La Vega, F.M., _et al._, Benchmarking of Germline Copy Number Variant Callers from Whole Genome Sequencing Data for Clinical Applications, _Bioinformatics Advances_ (2025). [https://doi.org/10.1093/bioadv/vbaf071arrow-up-right](https://doi.org/10.1093/bioadv/vbaf071) Gao, Y., _et al._, Whole-Genome Sequencing is a Viable Replacement for Chromosomal Microarray and Fragile X PCR Testing, _medRxiv_ (2025): 2025-05. [https://doi.org/10.1101/2025.05.24.25328260arrow-up-right](https://doi.org/10.1101/2025.05.24.25328260) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#somatic-cnv-caller) Somatic CNV Caller Masood, D., Ren, L., Nguyen, C., Brundu, F.G., Zheng, L. _et al._, Evaluation of somatic copy number variation detection by NGS technologies and bioinformatics tools on a hyper-diploid cancer genome, _Genome Biology_, **25(1)**, 163 (2024). [https://doi.org/10.1186/s13059-024-03294-8arrow-up-right](https://doi.org/10.1186/s13059-024-03294-8) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#str-expansion-detection) STR Expansion Detection Dolzhenko et al. Detection of long repeat expansions from PCR-free whole-genome sequence data. Genome Res. 2017 Nov;27(11):1895-1903. [https://doi.org/10.1101/gr.225672.117arrow-up-right](https://doi.org/10.1101/gr.225672.117) Dolzhenko, E. _et al._, ExpansionHunter: a sequence-graph-based tool to analyze variation in short tandem repeat regions, _Bioinformatics_, Volume 35, Issue 22, November 2019, Pages 4754–4756, [https://doi.org/10.1093/bioinformatics/btz431arrow-up-right](https://doi.org/10.1093/bioinformatics/btz431) Dolzhenko, E., Bennett, M.F., Richmond, P.A. et al. ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data. Genome Biol 21, 102 (2020). [https://doi.org/10.1186/s13059-020-02017-zarrow-up-right](https://doi.org/10.1186/s13059-020-02017-z) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#lpa-targeted-caller) LPA Targeted Caller Behera, S., Belyeu, J.R., Chen, X. _et al._, Identification of allele-specific KIV-2 repeats and impact on Lp(a) measurements for cardiovascular disease risk, _BMC Med Genomics_ **17**, 255 (2024). [https://doi.org/10.1186/s12920-024-02024-0arrow-up-right](https://doi.org/10.1186/s12920-024-02024-0) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#smn-targeted-caller) SMN Targeted Caller Chen, X., Sanchis-Juan, A., French, C.E. _et al._, Spinal muscular atrophy diagnosis and carrier screening from genome sequencing data, _Genet Med_ **22**, 945–953 (2020). [https://doi.org/10.1038/s41436-020-0754-0arrow-up-right](https://doi.org/10.1038/s41436-020-0754-0) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#cyp2d6-targeted-caller) CYP2D6 Targeted Caller Chen, X., Shen, F., Gonzaludo, N. _et al._, Cyrius: accurate _CYP2D6_ genotyping using whole-genome sequencing data, _Pharmacogenomics J_ **21**, 251–261 (2021). [https://doi.org/10.1038/s41397-020-00205-5arrow-up-right](https://doi.org/10.1038/s41397-020-00205-5) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#gba-targeted-caller) GBA Targeted Caller Toffoli, M., Chen, X., Sedlazeck, F.J. _et al._, Comprehensive short and long read sequencing analysis for the Gaucher and Parkinson’s disease-associated _GBA_ gene, _Commun Biol_ **5**, 670 (2022). [https://doi.org/10.1038/s42003-022-03610-7arrow-up-right](https://doi.org/10.1038/s42003-022-03610-7) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#dux4-rearrangements-caller) DUX4 Rearrangements Caller Grobecker, P., Berri, S., Peden, J.F. _et al._ A dedicated caller for DUX4 rearrangements from whole-genome sequencing data. _BMC Med Genomics_ **18**, 24 (2025). [https://doi.org/10.1186/s12920-024-02069-1arrow-up-right](https://doi.org/10.1186/s12920-024-02069-1) [PreviousTroubleshootingchevron-left](https://help.dragen.illumina.com/dragen-v4.3/reference/troubleshooting) [NextDRAGEN Publicationschevron-right](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-publications) Last updated 7 months ago Was this helpful? * [Method 1: Cite DRAGEN secondary analysis software in-text or in reference/bibliography list](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#method-1-cite-dragen-secondary-analysis-software-in-text-or-in-reference-bibliography-list) * [In-text](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#in-text) * [Reference or bibliography list](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#reference-or-bibliography-list) * [Method 2: Cite a specific algorithm using one of the papers listed below](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#method-2-cite-a-specific-algorithm-using-one-of-the-papers-listed-below) * [DRAGEN Germline Algorithms](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#dragen-germline-algorithms) * [Germline CNV Caller](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#germline-cnv-caller) * [Somatic CNV Caller](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#somatic-cnv-caller) * [STR Expansion Detection](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#str-expansion-detection) * [LPA Targeted Caller](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#lpa-targeted-caller) * [SMN Targeted Caller](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#smn-targeted-caller) * [CYP2D6 Targeted Caller](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#cyp2d6-targeted-caller) * [GBA Targeted Caller](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#gba-targeted-caller) * [DUX4 Rearrangements Caller](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#dux4-rearrangements-caller) Was this helpful? --- # Run DRAGEN VM on Azure | DRAGEN v4.3 | DRAGEN Use the following information to run the DRAGEN virtual machine (VM) on Microsoft Azure. For information on using Azure, see the Azure documentation available on the Microsoft site. You must have and use a DRAGEN Cloud license to run DRAGEN on Azure, please refer to the [Cloud Licensing Reference Section](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing) for more information. 1. Navigate to the Microsoft Azure portal, and then sign in. 2. Select **Marketplace**. 3. Select **View Private Offers**, and then select DRAGEN on Azure. 4. Select **Create**. Starting from a preset configuration option is not recommended for DRAGEN. 5. Select a subscription and resource group from the drop-down menus, or select **Create New**. 6. Enter a name for the virtual machine. 7. Select a region that is compatible with the NP-series. See the Azure documentation available on the Microsoft site for more information. 8. Select DRAGEN and the current version as the image. 9. Select a storage size from the Size drop-down list. Only NP10 and NP20 sizes are compatible. 10. Configure any additional VM settings. For your disk type, DRAGEN recommends using Premium SSD for optimal performance. For information, see the Azure documentation available on the Microsoft site. 11. When finished, select **Review + Create** 12. To launch the VM, select **Create**. 13. After deployment completes, select **Go to Resource**. After your VM deploys, you can connect to the DRAGEN VM via the Azure Cloud Shell or another client of your choice. For more information on using a VM, see the Azure documentation available on the Microsoft site. [PreviousDRAGEN on Microsoft Azurechevron-left](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-azure) [NextDRAGEN on Microsoft Azure Batchchevron-right](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch) Last updated 7 months ago Was this helpful? Was this helpful? --- # DRAGEN DNA Pipeline | DRAGEN v4.3 | DRAGEN The DRAGEN DNA Pipeline accelerates the secondary analysis of NGS data by harnessing the tremendous power available on the DRAGEN Platform. The pipeline includes highly optimized algorithms for mapping, aligning, sorting, duplicate marking, and haplotype variant calling. In addition to haplotype variant calling, the pipeline supports calling of copy number and structural variants as well as detection of repeat expansions and targeted calls. ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3660538983-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FpFn0gLe5V859SXejQNAW%252Fuploads%252Fgit-blob-8463a75eae577f8b9df5e67ce9b32a073032adf6%252Fdragen-dna-pipeline.DNA_Pipeline.whitebg.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=e5b69b52&sv=2) [PreviousPrepare a Reference Genomechevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome) [NextDNA Mappingchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align) Last updated 7 months ago Was this helpful? Was this helpful? --- # DRAGEN on AWS | DRAGEN v4.3 | DRAGEN You can run DRAGEN analysis on Amazon Web Services (AWS). For information on using DRAGEN, see the [DRAGEN User Guide Section](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3) . For information on using AWS, see the AMI documentation available on the Amazon Web Services site. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws#getting-access-to-the-dragen-ami-amazon-machine-image) Getting Access to the DRAGEN AMI (Amazon Machine Image) --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- There are two options available for using the AMI: a Marketplace AMI (Pay-As-You-Go) and a Private AMI (Bring-Your-Own-License). To use the Marketplace AMI, visit the AWS Marketplace and subscribe to the DRAGEN Complete Suite. For access to the Private AMI, please contact our [sales teamarrow-up-right](https://www.illumina.com/company/contact-us.html) to obtain licensing and deployment instructions. Also, please refer to the [Cloud Licensing Reference Section](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing) for more information. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws#launch-an-instance) Launch an Instance --------------------------------------------------------------------------------------------------------------------------------------------- To launch an EC2 instance, refer to the [AWS EC2 Launch Guide documentationarrow-up-right](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/LaunchingAndUsingInstances.html) - making sure you select the desired DRAGEN AMI and instance type. * For instance type, an [EC2 FPGA-powered instancearrow-up-right](https://aws.amazon.com/ec2/instance-types/f2/) is required. f2.6xlarge instance type is recommended for DRAGEN if regionally available, otherwise f1.4xlarge. * For configure storage, we recommend attaching 2TB of EBS storage using 4x 500GB GP3 volumes configured as RAID0, if local instance storage is insufficient [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws#connect-to-and-configure-your-instance) Connect to and Configure Your Instance ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 1. Navigate to the Instances page and select the instance to connect to. 2. Click **Connect**, then choose **SSH client**. 3. instanceUse appropriate username: ec2-user for EL8 images, or centos for EL7 images. 4. Mount the disks to `/staging` on the instance using the following commands as needed. Copy sudo yum -y install mdadm sudo mdadm --create --verbose /dev/md0 --level=0 --name=MY_RAID0 --raid-devices= sudo mkfs.ext4 -L MY_RAID0 /dev/md0 sudo mkdir -p /staging sudo sh -c "echo 'LABEL=MY_RAID0 /staging ext4 defaults,noatime 0 0' >> /etc/fstab" sudo mount -a For example, the following command mounts four volumes attached at `/dev/nvme1n1`, `/dev/nvme2n1`, `/dev/nvme3n1`, and `/dev/nvme4n1` to a RAID 0 on `/staging`. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws#data-flow) Data Flow --------------------------------------------------------------------------------------------------------------------------- Input files, including the [hash tablearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) , must be downloaded to the instance. DRAGEN can also process large input files (e.g., FASTQ, BAM) directly from s3 or a pre-signed URL. Ensure the instance has access to the s3 bucket via local credentials or an IAM role. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws#simple-run-example) Simple Run Example --------------------------------------------------------------------------------------------------------------------------------------------- You can use DRAGEN command-line options. For more information on DRAGEN analysis and command line options, see the [Command Line Options Section](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/command-line-options) of the user guide. For information on using AWS, see the AMI documentation available on the Amazon Web Services site. Note: As stated in the [Cloud Licensing Reference Section](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing) , if running DRAGEN in a container with IDMSv2 - make sure to increase your hop count to at least 2. [PreviousDRAGEN Multi-Cloudchevron-left](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud) [NextDRAGEN on AWS Batchchevron-right](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws-batch) Last updated 4 months ago Was this helpful? * [Getting Access to the DRAGEN AMI (Amazon Machine Image)](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws#getting-access-to-the-dragen-ami-amazon-machine-image) * [Launch an Instance](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws#launch-an-instance) * [Connect to and Configure Your Instance](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws#connect-to-and-configure-your-instance) * [Data Flow](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws#data-flow) * [Simple Run Example](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws#simple-run-example) Was this helpful? Copy sudo yum -y install mdadm sudo mdadm --create --verbose /dev/md0 --level=0 --name=MY_RAID0 --raid-devices=4 /dev/nvme1n1 /dev/nvme2n1 /dev/nvme3n1 /dev/nvme4n1 sudo mkfs.ext4 -L MY_RAID0 /dev/md0 sudo mkdir -p /staging sudo sh -c "echo 'LABEL=MY_RAID0 /staging ext4 defaults,noatime 0 0' >> /etc/fstab" sudo mount -a Copy cd /ephemeral/ wget https://webdata.illumina.com/downloads/software/dragen/resource-files/hg38-alt_masked.cnv.graph.hla.rna-10-r4.0-1.tar.gz mkdir /ephemeral/hg38-alt_masked.cnv.graph.hla.rna-10-r4.0-1 tar xvfz hg38-alt_masked.cnv.graph.hla.rna-10-r4.0-1.tar.gz -C hg38-alt_masked.cnv.graph.hla.rna-10-r4.0-1 wget https://ilmn-dragen-giab-samples.s3.amazonaws.com/WES/HG002/NA24385-AJ-Son-R1-NS_S33_L001_R2_001.fastq.gz wget https://ilmn-dragen-giab-samples.s3.amazonaws.com/WES/HG002/NA24385-AJ-Son-R1-NS_S33_L001_R1_001.fastq.gz dragen \ -r /ephemeral/hg38-alt_masked.cnv.graph.hla.rna-10-r4.0-1 \ --fastq-file1 /ephemeral/NA24385-AJ-Son-R1-NS_S33_L001_R2_001.fastq.gz \ --fastq-file2 /ephemeral/NA24385-AJ-Son-R1-NS_S33_L001_R2_001.fastq.gz \ --RGID NA24385_RGID \ --RGSM NA24385 \ --enable-map-align true \ --enable-map-align-output true \ --enable-duplicate-marking true \ --enable-variant-caller true \ --intermediate-results-dir /ephemeral/ \ --output-file-prefix NA24385 \ --output-directory /ephemeral/ --lic-server # needed for private AMI users --- # DRAGEN on Microsoft Azure | DRAGEN v4.3 | DRAGEN You can run DRAGEN analysis on Microsoft Azure. For information on using DRAGEN, see the [DRAGEN User Guide Section](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3) . For information on using Azure, see the Azure documentation available on the Microsoft site. A QuickStart for DRAGEN on Azure is avaialble here [DRAGEN on Azure QuickStartarrow-up-right](https://illumina.github.io/dragen-azure-quickstart/) If you are using an Azure Batch account, see [DRAGEN on Azure Batch](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch) for more information. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-azure#requirements) Requirements ----------------------------------------------------------------------------------------------------------------------------------- To use the DRAGEN Software for genomic data analysis on Azure, the following are required: * DRAGEN BYOL license. For more information, refer to the [Cloud Licensing Reference Section](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing) . * Azure cloud subscription. * Quota for NP-series virtual machines (VM). * Azure command-line interface (CLI). For instructions on installing the Azure CLI, see the Azure documentation available on the Microsoft site. * Genomic data uploaded to your Azure Blob storage account. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-azure#supported-regions) Supported Regions --------------------------------------------------------------------------------------------------------------------------------------------- DRAGEN on Azure is available in the following regions with field programmable gate array (FPGA)-enabled, standard NP-series virtual machines. * West US 2 * East US * South Central US * West Europe (Amsterdam) * Southeast Asia (Singapore) [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-azure#request-a-np-series-quota) Request a NP-series Quota ------------------------------------------------------------------------------------------------------------------------------------------------------------- DRAGEN requires FPGA hardware. You must request access to the FPGA-enabled, NP-series VMs. The vCPU requirements for NP-series SKUs are in increments of 10. When requesting an updated quota, DRAGEN recommends requesting vCPUs in batches of 10. The instructions for running DRAGEN on Azure require a minimum increase of 10 vCPU quotas for NP-series machines. To make sure you have quota for NP-series VMs or to increase a quota, navigate to your Microsoft Azure portal, and then do as follows. 1. Select **Subscriptions**, then choose your subscription. 2. Under Settings, select **Usage + quotas**. 3. Enter `NP` into the search bar to filter for the NP-series. The quota list displays Standard NPS Family vCPUs. 4. If you do not see any results, select **Request Increase**. For more information on requesting quota for NP-series VMs, see the Azure documentation available on the Microsoft site. [PreviousDRAGEN on AWS Batchchevron-left](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-aws-batch) [NextRun DRAGEN VM on Azurechevron-right](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-azure/run-vm-on-azure) Last updated 4 months ago Was this helpful? * [Requirements](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-azure#requirements) * [Supported Regions](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-azure#supported-regions) * [Request a NP-series Quota](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-azure#request-a-np-series-quota) Was this helpful? --- # DRAGEN on Microsoft Azure Batch | DRAGEN v4.3 | DRAGEN You can run DRAGEN analysis on Microsoft Azure Batch. For information on using Azure, see the Azure documentation available on the Microsoft site. If you are not using an Azure Batch account, see DRAGEN on Microsoft Azure for information on using DRAGEN without Azure Batch. You must have and use a DRAGEN Cloud license to run DRAGEN on Azure Batch, please refer to the [Cloud Licensing Reference Section](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing) for more information. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch#requirements) Requirements To use DRAGEN on Azure, the following are required: * DRAGEN Multi-Cloud license. For more information, contact Illumina Technical Support. * Azure cloud subscription. * Quota for NP-series virtual machines (VM). * Azure command-line interface (CLI). For instructions on installing the Azure CLI, see the Azure documentation available on the Microsoft site. * Genomic data uploaded to your Azure Blob storage account. * The Azure directory principal must be assigned the Contributor role for the Azure subscription. If you would like to restrict access, you can run deployment pipelines as a managed service principal with the Contributor role. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch#supported-regions) Supported Regions DRAGEN on Azure is available in the following regions with field programmable gate array (FPGA)-enabled, standard NP-series virtual machines. * West US 2 * East US * South Central US * West Europe (Amsterdam) * Southeast Asia (Singapore) [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch#quota-requirements) Quota Requirements ------------------------------------------------------------------------------------------------------------------------------------------- ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch#request-a-np-series-quota) Request a NP-series Quota DRAGEN requires FPGA hardware. You must request access to the FPGA-enabled, NP-series VMs. The vCPU requirements for NP-series SKUs are in increments of 10. When requesting an updated quota, DRAGEN recommends requesting vCPUs in batches of 10. The instructions for running DRAGEN on Azure require a minimum increase of 10 vCPU quotas for NP-series machines. To make sure you have sufficient quota for NP-series VMs or to increase your quota, navigate to your Microsoft Azure portal, and then do as follows: 1. Select **Subscriptions**, then choose your subscription. 2. Under Settings, select **Usage + quotas**. 3. Enter `NP` into the search bar to filter for the NP-series. The quota list displays Standard NPS Family vCPUs. 4. If you do not see any results, select **Request Increase**. For more information on requesting quota for NP-series VMs, see the Azure documentation available on the Microsoft site. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch#pool-allocation-modes) Pool Allocation Modes By default, Azure Batch allocates pools using the Batch service mode. In Batch service mode, compute nodes are separated into different quotas. To run DRAGEN in Batch service mode, you need to request additional quota for your Azure Batch account. For information on the current default quota for Batch accounts and on increasing your Azure Batch account quota, see the Azure Batch documentation available on the Microsoft Azure site. If using user subscription mode for your Batch account, you must add the Azure Batch service as the Contributor role. For more information, see the Microsoft Azure Batch documentation available on the Microsoft site. [PreviousRun DRAGEN VM on Azurechevron-left](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/dragen-on-azure/run-vm-on-azure) [NextAzure Batch Run Modeschevron-right](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch/run-azure-modes) Last updated 7 months ago Was this helpful? * [Requirements](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch#requirements) * [Supported Regions](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch#supported-regions) * [Quota Requirements](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch#quota-requirements) * [Request a NP-series Quota](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch#request-a-np-series-quota) * [Pool Allocation Modes](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch#pool-allocation-modes) Was this helpful? --- # DRAGEN Cloud Licensing | DRAGEN v4.3 | DRAGEN Credential based authentication is required for users that run DRAGEN on the cloud with the Bring-Your-Own-License (BYOL) model. DRAGEN must have access to the DRAGEN License server at runtime, license.dragen.illumina.com. To verify connectivity to the license server, you can query the healthcheck endpoint which will return a 200 status code and a small JSON body if successful. Copy curl https://license.dragen.illumina.com/healthcheck/version --header 'Content-Type: application/json' {"version":""} [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing#running-dragen-cloud-byol-with-credential-licensing) Running DRAGEN Cloud BYOL with Credential Licensing -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- BYOL users must provide credentials to DRAGEN at runtime, using one of two options. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing#providing-credentials-via-license-credentials-file) Providing Credentials via License Credentials File The suggested method for specifying your credentials is through a file passed in to DRAGEN at runtime. The file can be pointed to using the option `--lic-credentials `. This method provides a more secure way to pass credentials, which avoids accidental credentials leaks from command line console logs. The License Credentials File should be formatted as follows; Copy credentials-1= credentials-2= Note: when using the License Credentials file, a default license server domain will be used based off the version of DRAGEN being used; * DRAGEN 4.4 and above: https://license.dragen.illumina.com * DRAGEN 4.3 and earlier: https://license.edicogenome.com * If you wish to override this behavior, you can add the credentials-3 option to the credentials file. Note, do not specify the protocol (i.e. https). Copy credentials-3=license.dragen.illumina.com ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing#providing-credentials-via-command-line) Providing Credentials via Command Line Alternatively, you can simply provide your credentials over the command line using the option `--lic-server `. The license server URL should be formatted as follows; [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing#instance-identity) Instance Identity ------------------------------------------------------------------------------------------------------------------------------------ DRAGEN Cloud runs access the local instance identity document via the Instance Metadata Service to be used with credential authentication. It uses the IPv4 local address. If access to the local address is not allowed, authentication will fail. Alternately, the user may save the instance identity document(s) and point DRAGEN to use them instead, if the user does not want to allow applications to access this service. The instance identity document(s) only need to be saved once per account and region, and those files can be re-used subsequently. This is achieved using the command line option `--lic-instance-id-location `. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing#aws-instance-metadata-service-imdsv1-imdsv2) AWS Instance Metadata Service (IMDSv1/IMDSv2) [AWS Instance Metadata Service Informationarrow-up-right](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/configuring-instance-metadata-service.html) DRAGEN supports both AWS IMDSv1, and the more secure AWS IMDSv2. AWS IMDSv2 must be enabled on the AWS instance, otherwise, IMDSv1 is used by default. DRAGEN software will automatically detect the IMDS version in use and adapt its behavior accordingly. **Notes** * Currently, input streaming from an S3 bucket is supported only with IMDSv1. * IDMSv2 is only supported in DRAGEN versions 4.3 and above. * If using IDMSv2 and running DRAGEN in a container, you may need to [increase the defaultarrow-up-right](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/configuring-IMDS-existing-instances.html#modify-PUT-response-hop-limit) hop count to be at least 2. Saving the IMDSv1 document: Saving the IDMSv2 document: The instance identity folder must contain three files, respectively named `pkcs7`, `signature`, and `document`. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing#azure-instance-metadata-service-imds) Azure Instance Metadata Service (IMDS) [Azure Instance Metadata Service Informationarrow-up-right](https://learn.microsoft.com/en-us/azure/virtual-machines/instance-metadata-service) Saving the IDMS document: The instance identity folder must contain two files, respectively named `instance` and `document`. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing#retrieving-license-information-and-usage-using-dragen_lic) Retrieving License Information and usage using dragen\_lic --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- There are two options for retrieving information and usage for your licenses using the packaged dragen\_lic tool. Examples for each one are below. * Basic Output (i.e. no additional arguments). This is the recommended method to view license information by a human user as the output is more readable. * JSON Output (i.e. using the -j flag). This is the recommended method to view license information by a machine user as the output is already in a machine readable JSON format. **Note**: Just like running DRAGEN as noted above, you must specify your credentials using the `--lic-credentials` or `--lic-server` command line options. Note: Retrieving license information using the dragen\_lic tool is only supported on DRAGEN 4.4 and above. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing#retrieving-license-information-and-usage-using-api-endpoint) Retrieving License Information and usage using API endpoint ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Alternatively, you can retrieve license information and usage using our License Server endpoint specified below without the use of DRAGEN. License information is returned in a JSON format. GET request to https://license.dragen.illumina.com/api/v2/query\_quota. Your user credentials must be provided as a Basic Authorization header. An example of this using the curl tool is shown below. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing#run-information-collected) Run information collected ---------------------------------------------------------------------------------------------------------------------------------------------------- Run information is a key component of the DRAGEN Licensing infrastructure. For each individual run, the following information is collected by Illumina; * run date * run duration * licensing quota consumed (number of bases) in that run * run status * software version used for the run. * instance identity document [PreviousDRAGEN Server Licensingchevron-left](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/onprem_licensing) [NextSupportchevron-right](https://help.dragen.illumina.com/dragen-v4.3/reference/technical-assistance) Last updated 1 month ago Was this helpful? * [Running DRAGEN Cloud BYOL with Credential Licensing](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing#running-dragen-cloud-byol-with-credential-licensing) * [Providing Credentials via License Credentials File](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing#providing-credentials-via-license-credentials-file) * [Providing Credentials via Command Line](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing#providing-credentials-via-command-line) * [Instance Identity](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing#instance-identity) * [AWS Instance Metadata Service (IMDSv1/IMDSv2)](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing#aws-instance-metadata-service-imdsv1-imdsv2) * [Azure Instance Metadata Service (IMDS)](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing#azure-instance-metadata-service-imds) * [Retrieving License Information and usage using dragen\_lic](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing#retrieving-license-information-and-usage-using-dragen_lic) * [Retrieving License Information and usage using API endpoint](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing#retrieving-license-information-and-usage-using-api-endpoint) * [Run information collected](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing#run-information-collected) Was this helpful? Copy https://:@license.dragen.illumina.com Copy curl -v -H Metadata:true --noproxy "*" "http://169.254.169.254/latest/dynamic/instance-identity/pkcs7" -o /opt/instance-identity/pkcs7 curl -v -H Metadata:true --noproxy "*" "http://169.254.169.254/latest/dynamic/instance-identity/document" -o /opt/instance-identity/document cp /opt/instance-identity/pkcs7 /opt/instance-identity/signature Copy curl -X PUT -H "X-aws-ec2-metadata-token-ttl-seconds: 300" -H "X-aws-ec2-metadata-token: required" --noproxy "*" "http://169.254.169.254/latest/api/token" curl -H "X-aws-ec2-metadata-token: " --noproxy "*" "http://169.254.169.254/latest/dynamic/instance-identity/document" curl -H "X-aws-ec2-metadata-token: " --noproxy "*" "http://169.254.169.254/latest/dynamic/instance-identity/signature" curl -H "X-aws-ec2-metadata-token: " --noproxy "*" "http://169.254.169.254/latest/dynamic/instance-identity/pkcs7" Copy curl -v -H Metadata:true --noproxy "*" "http://169.254.169.254/metadata/instance?api-version=2020-09-01" -o /opt/instance-identity/instance curl -v -H Metadata:true --noproxy "*" "http://169.254.169.254/metadata/attested/document?api-version=2020-09-01" -o /opt/instance-identity/document Copy $ dragen_lic --lic-credentials my_credentials.cfg Copy User: DRAGEN Version: Time: --scatac-barcode-sequence-list ` The files must contain one possible cell barcode sequence per line. You can compress the file with gzip (`*.txt.gz`). During cell-barcode error correction any observed barcodes that do not match a sequence specified in the file are considered errors. If possible, the barcodes are corrected to a similar allowed sequence. If the barcodes cannot be corrected, they are filtered out. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#cell-filtering) Cell Filtering For each individual modality (either scRNA or scATAC), DRAGEN uses a threshold on the total count of unique UMIs (or reads) per cell barcode, to determine which barcodes are likely to correspond to single-cells in the original sample, instead of background noise. The threshold is determined based on the distribution of counts along barcodes and on the expected number of true cells in the sample. For more information, see the corresponding section in scATAC/scRNA documentation. After count thresholds in each individual modality is computed, DRAGEN performs a joint cell filtering step. Each cell barcode is represented in a 2-D space with coordinates computed as the total UMI count across genes and the total fragment count across peaks. Initially, a cell-barcode is considered as passing the joint filter if it is passing the filter in each individual modality. DRAGEN then groups all cell barcodes in two categories: those passing both individual modality filters and the rest of cell barcodes. A k-means algorithm with 2 clusters is run and the filtering status of each cell barcode is refined. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#command-line-example) Command-line Example ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ The following is an example command line to run the DRAGEN Single Cell Multiomics Pipeline. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#outputs) Outputs ---------------------------------------------------------------------------------------------------------------------------------------------------- Single-cell Multiomics outputs are found in the standard DRAGEN output location using the prefix ``. in case of a single library and the prefix `.`. in case of multiple libraries. All single-cell Multiomics output files contain word `multiomics` in their names. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#counts) Counts The following three files provide information per-cell feature count level in matrix market (\*.mtx) format: * `.multiomics.matrix.mtx.gz` * Count of unique UMIs or fragments for each cell/feature pair in sparse matrix format. * `.multiomics.barcodes.tsv.gz` * Cell-barcode sequence for each cell from the matrix. This includes all cell-barcodes. * `.multiomics.features.tsv.gz` * Feature name and ID for each feature in the matrix. The subset of barcodes corresponding to passing cells can be found under the Filter column in `.multiomics.barcodeSummary.tsv` indicated by values `PASS` and `FAIL`. The output includes filtered matrix files which only include the per-cell feature count level for the filtered cells in matrix market (`*.mtx`) format. The `multiomics.features.tsv.gz` file is common for the unfiltered and filtered matrices: * `.multiomics.filtered.matrix.mtx.gz` * Count of unique UMIs for each **filtered** cell/feature pair in sparse matrix format. * `.multiomics.filtered.barcodes.tsv.gz` * Cell-barcode sequence for each **filtered** cell from the matrix. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#loading-output-in-a-dense-matrix) Loading output in a dense matrix Some users might want to explore the output matrix in a human-readable format. To do so, a possible way would be to load the matrix in a "dense" dataframe in python (similar methodologies can be used in alternative programming languages). It is important to remember, however, that when possible a "sparse" representation of the matrix is preferable, due to the significant usage of memory and disk space of "dense" matrices. Several tools are available to work efficiently with "sparse" representations of single cell matrices (e.g., scanpy in python). The matrix can be converted into a "dense" representation through two python modules: `scanpy` and `pandas`. This has been tested with python 3.10.0, scanpy 1.9.3, pandas 1.5.3. First, it is necessary to install the required libraries: Within python, the matrix can be loaded in "dense" representation using the following commands: The matrix can be saved through different output formats (e.g., CSV), although this is not recommended due to high disk usage. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#alignments) Alignments DRAGEN Single-Cell Multiomics outputs two BAM files sorted by coordinate - one with suffix `scRNA.bam` and one with suffix `scATAC.bam`. For more details, please consult the corresponding section from scATAC/scRNA user guide. When running in multiomics mode, since the DRAGEN aligner processes both RNA and ATAC reads, there will be separate mapper metrics summary for each modality. This is identified via the RGID field as `rna` or `atac`. There is no common map/align metrics summary for all input reads since it would merge the two modalities. The `.mapping_metrics.csv` file will also reflect this. Below is an example snippet of the alignment metrics printed at the end of a DRAGEN Single-Cell Multiomics run. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#overall-metrics) Overall Metrics The `.multiomics_metrics.csv` file contains per sample scATAC and scRNA metrics. For more details, please consult with the corresponding section from scATAC/scRNA user guide. Here is an example of how a `.multiomics_metrics.csv` file can look like: ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#per-cell-metrics) Per-cell Metrics The `.multiomics.barcodeSummary.tsv` contains summary statistics for each unique cell-barcode per cell after error correction. Here is an example of how a `.multiomics.barcodeSummary.tsv` file can look like: [PreviousscATACchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac) [NextDRAGEN Methylation Pipelinechevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-methylation-pipeline) Last updated 7 months ago Was this helpful? * [Input Files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#input-files) * [Alignment Reference](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#alignment-reference) * [Read Input](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#read-input) * [DRAGEN Single-cell Settings](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#dragen-single-cell-settings) * [Barcode Position](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#barcode-position) * [Known Barcode Lists](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#known-barcode-lists) * [Cell Filtering](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#cell-filtering) * [Command-line Example](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#command-line-example) * [Outputs](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#outputs) * [Counts](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#counts) * [Alignments](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#alignments) * [Overall Metrics](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#overall-metrics) * [Per-cell Metrics](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics#per-cell-metrics) Was this helpful? Copy Lane,RGID,RGSM,RGLB,Read1File,Read2File,UmiFile,InputGroup 1,rna,sample1,illumina,scRNA.R1.fastq.gz,,scRNA.R2.fastq.gz,SCRNA 2,atac,sample1,illumina,scATAC.R1.fastq.gz,scATAC.R2.fastq.gz,scATAC.R3.fastq.gz,SCATAC Copy --scrna-barcode-position [++...][(:-)|(:+)] --scrna-umi-position --scatac-barcode-position [++...][(:-)|(:+)] Copy dragen \ --enable-rna=true \ --enable-single-cell-atac=true \ -r hg19.fa.default \ --ht-alt-aware-validate=false \ --fastq-list=multiomics_fastq_list.csv \ --umi-source=umifile \ --output-dir=multiomics_output \ --output-file-prefix=sample1 \ --scatac-barcode-position=0_15 \ --scrna-barcode-position=0_15 \ --scrna-umi-position=16_25 \ --single-cell-threshold=ratio \ --enable-single-cell-rna=true \ -a gencode.v32.primary_assembly.annotation.filtered.gtf \ --scrna-barcode-sequence-whitelist=737K-arc-v1-gex.gz \ --scatac-barcode-sequence-whitelist=737K-arc-v1-atac.gz Copy > pip install -U scanpy pandas Copy # import libraries import pandas as pd import scanpy as sc # define path to input files matrix_path = "path/to/matrix.mtx.gz" features_path = "path/to/features.tsv.gz" barcodes_path = "path/to/barcodes.tsv.gz" # load matrix through scanpy adata = sc.read_mtx(matrix_path).T adata.var_names = pd.read_csv(features_path, sep="\t", header=None, compression="gzip")[1] adata.obs_names = pd.read_csv(barcodes_path, sep="\t", header=None, compression="gzip")[0] # convert scanpy internal format (AnnData) to dense pandas DataFrame df = pd.DataFrame(adata.X.todense(), index=adata.obs_names, columns=adata.var_names) # save it as CSV file df.to_csv("output_matrix.csv") Copy MAPPING/ALIGNING PER RG rna Total reads in RG 52319680 100.00 MAPPING/ALIGNING PER RG rna Number of duplicate marked reads 0 0.00 MAPPING/ALIGNING PER RG rna Number of duplicate marked and mate reads removed NA MAPPING/ALIGNING PER RG rna Number of unique reads (excl. duplicate marked reads) 52319680 100.00 ... MAPPING/ALIGNING PER RG atac Total reads in RG 203484254 100.00 MAPPING/ALIGNING PER RG atac Number of duplicate marked reads 0 0.00 MAPPING/ALIGNING PER RG atac Number of duplicate marked and mate reads removed NA MAPPING/ALIGNING PER RG atac Number of unique reads (excl. duplicate marked reads) 203484254 100.00 ... Copy SINGLE-CELL ATAC METRICS,lib1,Invalid barcode fragments,0 SINGLE-CELL ATAC METRICS,lib1,Error free cell-barcode,308656 SINGLE-CELL ATAC METRICS,lib1,Error corrected cell-barcode,251364 ... SINGLE-CELL RNA METRICS,lib1,Median genes per cell,1456 SINGLE-CELL RNA METRICS,lib1,Total genes detected,14128 SINGLE-CELL METRICS,lib1,Passing cells,1920 Copy ID Barcode BarcodeScAtac BarcodeScRna UniqueFragments TotalFragments PeakFragments NonPrimaryContigFragments ChimericFragments LowMapqFragments MitochondrialFragments Peaks TotalReads GeneReads UMIs Genes MitochondrialReads Filter 1 AAACAAGCAAACAAAG AAACAAGCAAACAAAG GGCTAGTGTTCGGTAA 22 48 13 17 0 2 7 9 59 0 0 0 0 LOW 2 AAACAAGCAAACCAGC AAACAAGCAAACCAGC GGCTAGTGTACTGAAT 7 7 3 0 0 0 0 3 8 0 0 0 0 LOW --- # Kmer Classifier | DRAGEN v4.3 | DRAGEN [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#description) Description --------------------------------------------------------------------------------------------------------------------------------------- The metagenomics classifier uses a k-mer based classification algorithm to classify each query sequence (usually a read) against a collection of reference sequences. There are two logical steps to this process: 1) reference sequences are indexed into a searchable database 2) reference sequence database is searched using query sequences and query sequences are classified to taxid(s) associated with the reference sequences. This guide explains how to run query sequences against a pre-existing reference sequence database (As of DRAGEN 4.3+, users can build their own custom reference sequence database). [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#command-line-settings) Command Line Settings ----------------------------------------------------------------------------------------------------------------------------------------------------------- Option Description Required Inputs `--enable-kmer-classifier` Enables the Kmer Classifier. (Default=false). `--output-file-prefix` Prefix for all output files. `--output-directory` Directory for all output files. `--kmer-classifier-input-read-file` Input sequence file (zipped or unzipped) to the Kmer Classifier. `--kmer-classifier-db-file` Database of sequences to classify against. Optional Inputs `--intermediate-results-dir` Area for temporary files. Size must be greater than size of all FASTQ files multiplied by 2. `--kmer-classifier-load-db-ram` Load the database onto RAM. Do not use if database is on ramdisk. (Default=false). `--kmer-classifier-multiple-inputs` Set to true to run with multiple inputs. The input read file is now a .tsv file that has three columns: Sample ID, Read1 file, (optional) Read 2 file. (Default=false). `--kmer-classifier-min-window` The minimum number of consecutive kmers to classify assignment at taxid. (Default=1). `--kmer-classifier-output-read-seq` Option to enable read sequence column in the output file. (Default=false). `--kmer-classifier-output-taxid-seq` Option to enable a taxid string column in the output file. (Default=false). `--kmer-classifier-db-to-taxid-json` Path to JSON file that maps database IDs to external taxids, names, and ranks. `--kmer-classifier-no-read-output` Option to not create individual read output. (Default=false). `--kmer-classifier-no-taxid-counts` Option to not write taxid count output file. (Default=false). `--kmer-classifier-protein-input` Option to indicate protein query sequences. To use this option, the reference sequence database MUST be of protein sequences. (Default=false). `--kmer-classifier-ncpus` Option to set the number of CPUs available for processing. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#example-command-line) Example Command Line [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#input-details) Input Details ------------------------------------------------------------------------------------------------------------------------------------------- ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#input-reads) Input Reads Applies to: `--kmer-classifier-input-read-file`, `--kmer-classifier-multiple-inputs` If the analysis is for a single FASTA/FASTQ read file, then that filename is input to `--kmer-classifier-input-read-file` and `--kmer-classifier-multiple-inputs=false`. However, many read files can be submitted to the Kmer Classifier at one time, minimizing the load time for a large reference sequence database. In this case, the input file must be a `.tsv` (tab-separated) file with two columns (optionally 3 columns). The first column is a unique ID, the second column is the path to the read file, and the optional third column is the path to the second read file in the case of paired-end reads. The ID is used to distinguish the output files. There is no header line. This `.tsv` file is the input file to `--kmer-classifier-input-read-file` and `--kmer-classifier-multiple-inputs=true`. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#reference-sequences) Reference Sequences Applies to: `--kmer-classifier-db-file`, `--kmer-classifier-db-to-taxid-json`, `--kmer-classifier-load-db-ram` A file of reference sequences (the "database") can be quite large. If the database file is stored on a normal file system, it is recommended that you set `--kmer-classifier-load-db-ram=true`. This will tell the Kmer Classifier to load the database file into memory for faster analysis. It is also allowable to store the database file on a RAM disk, which reduces load time over many Kmer Classifier runs. In this case, it is recommended to set `--kmer-classifier-load-db-ram=false`. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#db-taxid-json-mapping-file) DB TaxID JSON Mapping File Applies to: `--kmer-classifier-db-to-taxid-json` This input file is downloaded alongside the reference sequence database. It associates a taxid internal to the classifier database to an external source, like the NCBI taxonomy. This JSON file is a dictionary where the keys are internal taxids, and is mapped to an external taxid, name, and rank. Example: The internal taxids are used in the output files. This JSON file can be used to map the results to taxids from the NCBI taxonomy. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#downloading-reference-sequence-databases-and-mapping-files) Downloading Reference Sequence Databases and Mapping Files #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#genome-database) Genome Database The genome database includes NCBI RefSeq genomes for human, bacteria, archaea, viruses, and fungi. The December 3 2023 NCBI taxonomy was used to build the database, and the sequences were collected in December 2023. To download the reference index file and the taxid mapping JSON: #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#genome-and-nt-database) Genome and NT Database This database includes the contents of the Genome database and all of the NCBI nucleotide (nt) database. The sequences from the NCBI nucleotide database were collected in July 2023, and the December 3 2023 NCBI taxonomy was used to build the database. Two versions of this database are available for download: One that requires a machine with >= 550GB RAM, and a compressed version that trades approximately 5-10% accuracy for a smaller RAM footprint and requires a machine with >= 225GB RAM. To download the reference index file and the taxid mapping JSON: To download the compressed reference index file and the taxid mapping JSON: #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#uniref90-database) UniRef90 Database This database includes all protein sequences of the UniRef90 database. The sequences were collected in March 2024 and the March 28 2024 NCBI taxonomy was used to build the database. To download the reference index file and the taxid mapping JSON: #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#id-16s-database) 16S database This database includes full length bacterial 16S sequences from the NCBI. The sequences were collected in April 2024 and the March 28 2024 NCBI taxonomy was used to build the database. To download the reference index file and the taxid mapping JSON: [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#ouput-details) Ouput Details ------------------------------------------------------------------------------------------------------------------------------------------- There are two output files, one organized around the reads, and the other organized around the taxids. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#read-level-output) Read-level Output Applies to: `--kmer-classifier-output-taxid-seq`, `--kmer-classifier-output-read-seq` The main output file is a `.tsv` file with the extension `.read_classifications.tsv`. It has no header line, has tab-separated columns, and can vary in the number of columns depending on command line options. It details the results for each read. Column Description Data Type 1 Read index integer 2 Read name string 3 Taxid the read classified to integer 4 Maximum number of contiguous kmers that classified to this taxid integer 5 Score assigned to the classification integer 6 Number of kmers that classified to this taxid integer 7 Read duplication count integer 8 Name associated with taxid, if given with `--kmer-classifier-db-to-taxid-json` string 9 Taxonomic rank associated with taxid, if given with `--kmer-classifier-db-to-taxid-json` string 10 Taxid that each kmer classified to (is output when the `--kmer-classifier-output-taxid-seq` flag is set) list of integers separated by commas 11 Read sequence (is output when the the `--kmer-classifier-output-read-seq` flag is set) string ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#taxid-level-output) TaxID-level Output The second output file is a `.tsv` file with the extension `.classifier.taxid_kmer_counts.tsv`. It has a header line and has tab-separated columns. It summarizes the results for each taxid. Header Description Data Type db\_taxid Identifier for this taxid used internally in the database integer duplicity Ratio of total number of kmers from reads assigned to this taxid compared to the number of distinct kmers from reads assigned to this taxid float distinct\_coverage Percent of kmers in the database assigned to this taxid that are covered by kmers in the reads assigned to this taxid integer read\_count Number of reads that classified to this taxid integer total\_kmer\_count Number of kmers that classified to this taxid integer distinct\_kmer\_count Number of distinct kmers that classified to this taxid integer cumulative\_read\_count Cumulative number of reads assigned to this taxid and its taxonomic descendants integer taxid Taxid integer name Name associated with the taxid, if given with `--kmer-classifier-db-to-taxid-json` string rank Taxonomic rank of the taxid, if given with `--kmer-classifier-db-to-taxid-json` string taxid\_distinct\_kmer\_count Number of distinct kmers assigned to this taxid from the reference sequences string probability\_present Not in use float [PreviousExplify Analysis Pipelinechevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline) [NextKmer Classifier Database Builderchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-class-db-builder) Last updated 7 months ago Was this helpful? * [Description](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#description) * [Command Line Settings](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#command-line-settings) * [Example Command Line](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#example-command-line) * [Input Details](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#input-details) * [Input Reads](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#input-reads) * [Reference Sequences](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#reference-sequences) * [DB TaxID JSON Mapping File](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#db-taxid-json-mapping-file) * [Downloading Reference Sequence Databases and Mapping Files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#downloading-reference-sequence-databases-and-mapping-files) * [Ouput Details](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#ouput-details) * [Read-level Output](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#read-level-output) * [TaxID-level Output](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-classifier#taxid-level-output) Was this helpful? Copy dragen \ --enable-kmer-classifier=true \ --output-file-prefix \ --output-directory \ --kmer-classifier-input-read-file /path/to/fastq.gz \ --kmer-classifier-db-file /path/to/database \ --kmer-classifier-min-window 1 \ --kmer-classifier-ncpus=2 \ --kmer-classifier-output-read-seq=false \ --kmer-classifier-output-taxid-seq=false Copy { "2": {"taxid": 2, "name": "bacteria", "rank": "kingdom"}, "3": {"taxid": 2697049, "name": "SARS-CoV-2", "rank": "subspecies"}, "4": {"taxid": 5052, "name": "Aspergillus", "rank": "genus"} } Copy wget https://illumina-explify-databases.s3.us-east-1.amazonaws.com/kmer-classifier/dragen-kmer-classifier.refseq_genomes.v6dh.t6db wget https://illumina-explify-databases.s3.us-east-1.amazonaws.com/kmer-classifier/dragen-kmer-classifier.refseq_genomes.name_map.json Copy wget https://illumina-explify-databases.s3.us-east-1.amazonaws.com/kmer-classifier/dragen-kmer-classifier.genomes_plus_nt.v6dh.t6db wget https://illumina-explify-databases.s3.us-east-1.amazonaws.com/kmer-classifier/dragen-kmer-classifier.genomes_plus_nt.name_map.json Copy wget https://illumina-explify-databases.s3.us-east-1.amazonaws.com/kmer-classifier/dragen-kmer-classifier.genomes_plus_nt.compressed.v6dh.t6db wget https://illumina-explify-databases.s3.us-east-1.amazonaws.com/kmer-classifier/dragen-kmer-classifier.genomes_plus_nt.name_map.json Copy wget https://illumina-explify-databases.s3.us-east-1.amazonaws.com/kmer-classifier/dragen-kmer-classifier.u90_all.v6dh.t6db wget https://illumina-explify-databases.s3.us-east-1.amazonaws.com/kmer-classifier/dragen-kmer-classifier.u90_all.name_map.json Copy wget https://illumina-explify-databases.s3.us-east-1.amazonaws.com/kmer-classifier/dragen-kmer-classifier.16S.v6dh.t6db wget https://illumina-explify-databases.s3.us-east-1.amazonaws.com/kmer-classifier/dragen-kmer-classifier.16S.name_map.json --- # Prepare a Reference Genome | DRAGEN v4.3 | DRAGEN Before a reference genome can be used with DRAGEN, it must be converted from FASTA format into a custom binary format for use with the DRAGEN hardware. The options used in this preprocessing step offer tradeoffs between performance and mapping quality. Pre-built DRAGEN reference genomes are available for download in the Illumina customer portal. If you find that performance and mapping quality with these are adequate, there is a good chance that you can simply work with these supplied reference genomes. Depending on your read lengths and other particular aspects of your application, you may be able to improve mapping quality and/or performance by tuning the reference preprocessing options. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#hash-table-background) Hash Table Background -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The DRAGEN mapper extracts many overlapping seeds (subsequences or K-mers) from each read, and looks up those seeds in a hash table residing in memory on its PCIe card, to identify locations in the reference genome where the seeds match. Hash tables are ideal for extremely fast lookups of exact matches. The DRAGEN hash table must be constructed from a chosen reference genome using the `--build-hash-table option`, which extracts many overlapping seeds from the reference genome, populates them into records in the hash table, and saves the hash table as a binary file. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#automatic-reference-detection) Automatic Reference Detection DRAGEN will attempt to detect the provided reference in order to automatically apply recommended resources and settings. There are four human references that DRAGEN can detect: hg38, hg19, hs37d5, and chm13v2. DRAGEN is able to detect references that contain a subset of the primary contigs from one of these references, as long as the names and lengths of the detected contigs are consistent with the names and lengths from the standarad assemblies of these references. In detail, automatic reference detection operates as follows: We define a primary contig of a human genome to be an autosome (1-22) or sex chromosome (X,Y). Let F be the input fasta. For each reference genome R in hg38, hg19, hs37d5, and chm13v2, DRAGEN checks if there are any contigs in F that have the same name and length as a primary contig in R, and that there are no contigs in F that have the same name as a contig in R, but with different length. If these conditions hold for exactly one of hg38, hg19, hs37d5, and chm13v2, then that reference is detected and resources may be applied automatically. The DRAGEN hash table builder will automatically apply decoy contigs and mask bed files to detected reference. Other pipelines may also apply automatic resources. For example variant callers may apply machine learning models and target bed files. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#naming-conventions) Naming Conventions In order for DRAGEN to correctly detect the provided reference, it is important to use the standard naming conventions for each of the four human assemblies that DRAGEN detects: Assembly Autosome and Sex Chromosome Names hg38, hg19, chm13v2 chr1-chr22, chrX, chrY hs37d5 1-22, X, Y ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#reference-seed-interval) Reference Seed Interval The size of the DRAGEN hash table is proportionate to the number of seeds populated from the reference genome. The default is to populate a seed starting at every position in the reference genome, ie, roughly 3 billion seeds from a human genome. This default requires at least 32 GB of memory on the DRAGEN PCIe board. To operate on larger, nonhuman genomes or to reduce hash table congestion, it is possible to populate less than all reference seeds using the `--ht-ref-seed-interval` option to specify an average reference interval. The default interval for 100% population is `--ht-ref-seed-interval 1`, and 50% population is specified with `--ht-ref-seed-interval 2`. The population interval does not need to be an integer. For example, `--ht-ref-seed-interval 1.2` indicates 83.3% population, with mostly 1-base and some 2-base intervals to achieve a 1.2 base interval on average. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#hash-table-occupancy) Hash Table Occupancy It is characteristic of hash tables that they are allocated a certain size, but always retain some empty records, so they are less than 100% occupied. A healthy amount of empty space is important for quick access to the DRAGEN hash table. Approximately 90% occupancy is a good upper bound. Empty space is important because records are pseudo-randomly placed in the hash table, resulting in an abnormally high number of records in some places. These congested regions can get quite large as the percentage of empty space approaches zero, and queries by the DRAGEN mapper for some seeds can become increasingly slow. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#hash-table-seed-length) Hash Table / Seed Length The hash table is populated with reference seeds of a single common length. This primary seed length is controlled with the `--ht-seed-len` option, which defaults to 21. The longest primary seed supported is 27 bases when the table is 8 GB to 31.5 GB in size. Generally, longer seeds are better for run time performance, and shorter seeds are better for mapping quality (success rate and accuracy). A longer seed is more likely to be unique in the reference genome, facilitating fast mapping without needing to check many alternative locations. But a longer seed is also more likely to overlap a deviation from the reference (variant or sequencing error), which prevents successful mapping by an exact match of that seed (although another seed from the read may still map), and there are fewer long seed positions available in each read. Longer seeds are more appropriate for longer reads, because there are more seed positions available to avoid deviations. Seed Length Recommendations Value for `--ht-seed-len` Read Length 21 100 bp to 150 bp 17 to 19 shorter reads (36 bp) 27 250+ bp ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#hash-table-seed-extensions) Hash Table / Seed Extensions Due to repetitive sequences, some seeds of any given length match many locations in the reference genome. DRAGEN uses a unique mechanism called seed extension to successfully map such high-frequency seeds. When the software determines that a primary seed occurs at many reference locations, it extends the seed by some number of bases at both ends, to some greater length that is more unique in the reference. For example, a 21-base primary seed may be extended by 7 bases at each end to a 35-base extended seed. A 21-base primary seed may match 100 places in the reference. But 35-base extensions of these 100 seed positions may divide into 40 groups of 1-3 identical 35-base seeds. Iterative seed extensions are also supported, and are automatically generated when a large set of identical primary seeds contains various subsets that are best resolved by different extension lengths. The maximum extended seed length, by default equal to the primary seed length plus 128, can be controlled with the `--ht-max-ext-seed-len` option. For example, for short reads, it is advisable to set the maximum extended seed shorter than the read length, because extensions longer than the whole read can never match. It is also possible to tune how aggressively seeds are extended using the following options (advanced usage): `--ht-cost-coeff-seed-len` `--ht-cost-coeff-seed-freq` `--ht-cost-penalty` `--ht-cost-penalty-incr` There is a tradeoff between extension length and hit frequency. Faster mapping can be achieved using longer seed extensions to reduce seed hit frequencies, or more accurate mapping can be achieved by avoiding seed extensions or keeping extensions short, while tolerating the higher hit frequencies that result. Shorter extensions can benefit mapping quality both by fitting seeds better between SNPs, and by finding more candidate mapping locations at which to score alignments. The default extension settings along with default seed frequency settings, lean aggressively toward mapping accuracy, with relatively short seed extensions and high hit frequencies. The defaults for the seed frequency options are as follows: Option Default `--ht-cost-coeff-seed-len` 1 `--ht-cost-coeff-seed-freq` 0.5 `--ht-cost-penalty` 0 `--ht-cost-penalty-incr` 0.7 `--ht-max-seed-freq` 16 `--ht-target-seed-freq` 4 ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#seed-frequency-limit-and-target) Seed Frequency Limit and Target One primary or extended seed can match multiple places in the reference genome. All such matches are populated into the hash table, and retrieved when the DRAGEN mapper looks up a corresponding seed extracted from a read. The multiple reference positions are then considered and compared to generate aligned mapper output. However, the DRAGEN software enforces a limit on the number of matches, or frequency, of each seed, which is controlled with the `--ht-max-seed-freq option`. By default, the frequency limit is 16. In practice, when the software encounters a seed with higher frequency, it extends it to a sufficiently long secondary seed that the frequency of any particular extended seed pattern falls within the limit. However, if a maximum seed extension would still exceed the limit, the seed is rejected, and not populated into the hash table. Instead, a single High Frequency record is populated. This seed frequency limit does not tend to impact DRAGEN mapping quality notably, for two reasons. First, because seeds are rejected only when extension fails, only extremely high-frequency primary seeds, typically with many thousands of matches are rejected. Such seeds are not very useful for mapping. Second, there are other seed positions to check in a given read. If another seed position is unique enough to return one or more matches, the read can still be properly mapped. However, if all seed positions were rejected as high frequency, often this means that the entire read matches similarly well in many reference positions, so even if the read were mapped it would be an arbitrary choice, with very low or zero MAPQ. Thus, the default frequency limit of 16 for `--ht-max-seed-freq` works well. However, it may be decreased or increased, up to a maximum of 256. A higher frequency limit tends to marginally increase the number of reads mapped (especially for short reads), but commonly the additional mapped reads have very low or zero MAPQ. This also tends to slow down DRAGEN mapping, because correspondingly large numbers of possible mappings are occasionally considered. In addition to a frequency limit, a target seed frequency can be specified with `--ht-target-seed-freq` option. This target frequency is used when extensions are generated for high frequency primary seeds. Extension lengths are chosen with a preference toward extended seed frequencies near the target. The default of 4 for `--ht-target-seed-freq` means that the software is biased toward generating shorter seed extensions than necessary to map seeds uniquely. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#references-with-alt-contigs) References with ALT contigs When building a reference hash table from a fasta with ALT contigs, it may be desired to mask certain regions of high similarity, or to establish a liftover realtionships between primary and alternate contigs. The recommended approach is masking, as described in the Map-Align section. When hg19 or hg38 alt contigs are detected, the hash table builder will require a liftover file or a bed file to mask the alt contigs. If non are provided, a mask bed file from `/fasta_mask/` will be used automaticaly. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#masked-references) Masked References DRAGEN has adopted a masked approach to handle native reference ALT contigs, where strategic regions are masked to increased accuracy. The hash table builder will build the mapper hash table as if the regions that were specified in the argument for `ht-mask-bed` were masked with N's. The hash table builder will only allow setting one of `ht-mask-bed` or `ht-alt-liftover`. Each line in the bed file is expected to contain a contig name, start position (0-based), and end position (1-based), seperated by a single tab or space. Lines that start with # are ignored by the hash table builder to allow commenting. Any line with a contig name that is not found in the input fasta is skipped and logged to the DRAGEN log file. Likewise, lines that describe empty intervals are skipped. If all lines are skipped this way, the hash table builder will issue an error and abort, unless the mask bed file was automatically applied (see Automatic masking). The hash table builder will always issue an error and abort if an interval described in the BED file is outside of the range of the corresponding contig in the fasta. Lines that are not skipped are written to a file called mask.bed that will be present in the hash table output directory, and whose digest will appear in hash\_table.cfg. This file is used when a reference is loaded to the FPGA card to dynamically mask reference.bin. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#automatic-masking) Automatic masking When running from a fasta for which hg38 or hg19 is detected (See Automatic Reference Detection), and no argument for `ht-mask-bed` or `ht-alt-liftover` was provided, the hash table builder will automatically apply the corresponding bed file for the detected reference from `/fasta_mask/`. Note that the hash table builder will identify alt contigs by name. So when running from an input fasta that contains alt contig with standard names but modified base content, it is recommended to suppress automatic masking by setting `ht-suppress-mask=true` or by passing a custom mask bed file to `ht-mask-bed`. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#handling-decoy-contigs) Handling Decoy Contigs The behavior of DRAGEN with respect to the handling of decoy contigs in the reference has changed since version 2.6. Starting with DRAGEN 3.x, DRAGEN's hash table builder automatically detects the absence of the decoy contigs from the reference and adds it to the FASTA file, prior to building the hash table. The decoys file is found at `/liftover/hs_decoys.fa`. If the reference is missing the decoy contigs, then the reads which map to the decoy contigs are artificially marked as unmapped in the output BAM (because the original reference does not have the decoy contig). This results in an artificially lower mapping rate, however, the accuracy of variant calling is improved thanks to removing false positive caused by decoy reads. Illumina recommends using this feature by default. However, you can to set the `--ht-suppress-decoys` option to true to suppress adding these decoys to the hash table. The table below describes the difference in behavior between older DRAGEN versions (2.6 and earlier) and DRAGEN 3.x versions with respect to the handling of decoy contigs in the hash table builder: DRAGEN Behavior DRAGEN 2.6 and earlier versions DRAGEN 3.0 and later versions Reference does not include the decoy contigs (eg, hg19) Decoy reads mismap elsewhere in the genome due to the lack of contigs in the reference. Artificially higher mapping rate. False positive calls in noisy regions to which the decoy contigs are mismapped. DRAGEN automatically detects the absence of the decoy contig from the reference and adds it to the FASTA file. Artificially lower mapping rate because decoy reads which map to the decoy contigs are artificially marked as unmapped in the output BAM (because the original reference does not have the decoy contig). False positive calls are avoided thanks to adding the decoy contigs under the hood. Therefore this helps variant calling. Reference includes the decoy contigs (eg, hs37d5) Decoy reads map to the decoy contigs. High mapping rate. No false positive calls caused by decoy reads because decoy reads map to the right place Decoy reads map to the decoy contigs. High mapping rate. No false positive calls caused by decoy reads because decoy reads map to the right place [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#prepare-a-pangenome-reference) Prepare a Pangenome Reference ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ DRAGEN analysis is capable of mapping on a pangenome hash table. The pangenome hash table introduces alternate graph paths to the linear reference hash table to represent more broadly the allelic diversity of the population over the whole genome or in specific regions defined in a bed file. Gain on accuracy from this methodology has been described in scientific blogs available on the [Illumina Genomics Research Hub sitearrow-up-right](https://www.illumina.com/science/genomics-research.html) . Mutigenome hash tables for CHM13\_v2, hg38, hg19 and hs37d5 assemblies are available on the [DRAGEN Software Support Site pagearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) . See [DRAGEN Multigenome Mapper](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align#dragen-multigenome-mapper) for information on the multigenome mapping method. It is possible to build a custom pangenome reference in order to: * customize the released pangenome hash table with custom bed files or hash table builder options. A set of bed files are available in the resource files on the [DRAGEN Software Support Site pagearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) . * generate a population-specific-pangenome hash table from pangenome msVCF generated from the BSSH app. * generate a human or non-human pangenome hash table from customer-provided msVCF. The input files required are a single multi-sample VCF file containing the set of population variants, and optionally bed files restricting graph to some region. The generated files, including hash\_table.cmp and associated files in the specified output directory, can then be used as the reference hash table for the DRAGEN mapper. DRAGEN software supports the tool on human reference with files available on the [DRAGEN Software Support Site pagearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) . For non-human, the user provides the required resource files. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#usage) Usage To enable the pangenome hash table builder, example command usage is : `dragen --build-hash-table true (required) --ht-graph-msvcf-file (required) --ht-graph-extra-kmer-bed < graph.bed> (optional) --ht-mask-bed (optional) --ht-graph-exclusion-bed (optional) --output-directory (required) [options]` ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#inputs) Inputs #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#set-of-population-variants-in-a-multi-sample-vcf-msvcf) Set of population variants, in a multi-sample VCF (msVCF) The custom pangenome hash table builder tool uses a set of population variants provided by the user to generate a pangenome hash table. The variants must be specified in VCF format, in a single multi-sample VCF (msVCF) file containing the variants for a set of individuals. This multi-sample VCF file must have specific formatting described below. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#specific-msvcf-input-formatting) Specific msVCF input formatting The custom pangenome hash table builder tool only supports msVCF file input respecting the format described below: * msVCF compliant with 4.2 VCF format specification * with variants positionally sorted in the same contig order as the main FASTA reference genome provided in --ht-reference * records shall include diploid or haploid GT calls * supports multi-allelic variants merged in multi-line or separated in multiple lines * with the following FILTER codes, non-PASS records are ignored: * ##FILTER= * with the following FORMAT field : * ##FORMAT= * for better results, we recommend variants to be left-aligned. * maximum number of recommended samples in the msVCF is 256. Higher number may lead to very high memory usage at hash table creation. > Note: INFO/FORMAT subfields must be defined in the header. Events with undefined subfields are ignored. To build a high-performance custom genome it is highly recommended to use long read sequencing data. We recommend using external tools such as Whatshap (https://github.com/whatshap/whatshap) to generate phased input. DRAGEN analysis leverages the phasing information to reconstruct population haplotypes. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#reference-genome) Reference genome A reference genome in FASTA format must be provided. Reference genomes are available to download from the [DRAGEN Software Support Site pagearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) . > Note: the reference genome provided as input must be the same as the one used to generate the input phased msVCF. If the msVCF contains variants from regions not present in the fasta file, the pangenome reference builder will stop with an error. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#exclusion-bed-file-optional) Exclusion bed file (optional) This bed file is used to filter out regions of the msVCF file. Variants that fall within intervals defined in the "Graph exclusion bed" file will be ignored and not used in any part of the pangenome reference builder. The result will be the same as if the input msVCF did not contain any variants in the regions defined in the exclusion bed. The file is optional, by default every variants in the msVCF file will be used. Exclusion bed files are available to download from [DRAGEN Software Support Site pagearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) . A custom exclusion bed file can also be provided given the following format: tab delimited with first three columns being: contig name, start position, end position. Any line with a contig name that is not found in the input FASTA is skipped. Any lines that describe empty intervals are skipped. > Note: records of the exclusion bed file provided must be from the same build as the reference genome used to build the pangenome reference. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#extra-kmer-bed-file-optional) Extra kmer bed file (optional) This file is used to define regions in the genome where extra seeds will be indexed in the hash table. By default, only seed extracted from the primary reference will be extracted and saved in the reference hash table for mapping. This option will additionally generate seeds from population variants in the defined regions. It is recommended to include the expected difficult regions in this bed file. Extra-kmer-bed files are available to download from [DRAGEN Software Support Site pagearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) for the human hg38, hg19, hs37d5, and chm13 references. An Extra-kmer-bed bed file can also be provided given the following format: tab delimited with first three columns being: contig name, start position, end position. Any line with a contig name that is not found in the input FASTA is skipped. Any lines that describe empty intervals are skipped. > Note: records of the Extra-kmer-bed file provided must be from the same build as the reference genome used to build the graph reference. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#mask-bed-file-recommended) Mask bed file (recommended) A mask bed file must be provided in order to mask certain regions of high similarity between primary and alternate contigs present in the main genome FASTA. Mask bed files are available to download from the [DRAGEN Software Support Site pagearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) . A custom mask bed file can also be provided given the following format: tab delimited with first three columns being: contig name, start position, end position. Any line with a contig name that is not found in the input FASTA is skipped. Any lines that describe empty intervals are skipped. > Note: records of the mask bed file provided must be from the same build as the reference genome used to build the graph reference. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#command-line-options) Command line options Option Required Description \--build-hash-table Yes Set to true \--ht-graph-msvcf-file Yes Path to the multi-sample VCF file containing population variants \--ht-reference Yes Path to the reference genome FASTA file. \--ht-graph-extra-kmer-bed No Path to the extra kmer bed file \--ht-mask-bed No (but recommended) Path to the mask bed file \--ht-graph-exclusion-bed No Path to the exclusion bed file \--output-directory Yes Specify the directory where all related hash table files will be written > Note: The custom graph reference hash table end to end pipeline will return an error if options --ht-alt-liftover or --ht-allow-mask-and-liftover are specified. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#output) Output The hash table builder generates the following outputs: File Description reference.bin The reference sequences, encoded in 4 bits per base. Four-bit codes are used, so the size in bytes is roughly half the reference genome size. In between reference sequences, N are trimmed and padding is automatically inserted. For example, hg19 has 3,137,161,264 bases in 93 sequences. This is encoded in 1,526,285,312 bytes = 1.46 GB, where 1 GB means 1 GiB or 2^30^ bytes. hash\_table.cmp Compressed hash table. The hash table is decompressed and used by the DRAGEN mapper to look up primary seeds with length specified by the `--ht-seed-len` option and extended seeds of various lengths. hash\_table.cfg A list of parameters and attributes for the generated hash table, in a text format. This file provides key information about the reference genome and hash table. hash\_table.cfg.bin A binary version of hash\_table.cfg used to configure the DRAGEN hardware. hash\_table\_stats.txt A text file listing extensive internal statistics on the constructed hash including the hash table occupancy percentages. This table is for information purposes. It is not used by other tools. mask.bed Present only for masked hash tables. A tab delimeted bed file that describes the masked regions. Contains all lines from the input bed file that are not comment lines, lines that describe empty intervals, or lines with contig names that were not found in the input fasta. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#prepare-a-linear-reference) Prepare a linear Reference ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#usage-1) Usage Use the `--build-hash-table` option to transform a reference FASTA into the hash table for DRAGEN mapping. It takes as input a FASTA file (multiple reference sequences being concatenated) and a preexisting output directory. Build command usage is as follows: ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#input) Input The `--ht-reference` and `--output-directory` options are required for building a hash table. The `--ht‑reference` option specifies the path to the reference FASTA file, while `--output-directory` specifies a preexisting directory where the hash table output files are written. Illumina recommends organizing various hash table builds into different folders. As a best practice, folder names should include any nondefault parameter settings used to generate the contained hash table. The sequence names in the reference FASTA file must be unique. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#command-line-options-1) Command line options Option Required Description \--build-hash-table Yes Set to true \--ht-reference Yes Path to the reference genome FASTA file. \--ht-mask-bed No (but recommended) Path to the mask bed file. If not provided, the DRAGEN software automatically applies BED files for hg38 and hg19 from /opt/edico/fasta\_mask. \--output-directory Yes Specify the directory where all related hash table files will be written #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#liftover-based-alt-aware-hash-tables) Liftover Based ALT-Aware Hash Tables While masking is the recommended approach to dealing with ALT contigs, DRAGEN also supports a liftover based method. To enable liftover based ALT-aware mapping in DRAGEN, build the hash table with a liftover file by using the `--ht-alt-liftover` option. The hash table builder classifies each reference sequence as primary or alternate based on the liftover file, and packs primaries before alternates in reference.bin. SAM liftover files for hg38DH and hg19 are in the `/liftover` folder. **Custom Liftover Files** Custom liftover files can be used in place of those provided with DRAGEN. Liftover files must be SAM format, but no SAM header is required. SEQ and QUAL fields can be omitted ('\*'). Each alignment record should have an alternate haplotype reference sequence name as QNAME, indicating the RNAME and POS of its liftover alignment in a destination (normally primary assembly) reference sequence. Reverse-complemented alignments are indicated by bit 0x10 in FLAG. Records flagged unmapped (0x4) or secondary (0x100) are ignored. The CIGAR may include hard or soft clipping, leaving parts of the ALT contig unaligned. A single reference sequence cannot serve as both an ALT contig (appearing in QNAME) and a liftover destination (appearing in RNAME). Multiple ALT contigs can align to the same primary assembly location. Multiple alignments can also be provided for a single ALT contig (extras optionally be flagged 0x800 supplementary), such as to align one portion forward and another portion reverse-complemented. However, each base of the ALT contig only receives one liftover image, according to the first alignment record with an M CIGAR operation covering that base. SAM records with QNAME missing from the reference genome are ignored, so that the same liftover file may be used for various reference subsets, but an error occurs if any alignment has its QNAME present but its RNAME absent. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#options-for-advanced-users) Options for advanced users ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#primary-seed-length) Primary Seed Length The `--ht-seed-len` option specifies the initial length in nucleotides of seeds from the reference genome to populate into the hash table. At run time, the mapper extracts seeds of this same length from each read, and looks for exact matches (unless seed editing is enabled) in the hash table. The maximum primary seed length is a function of hash table size. The limit is k=27 for table sizes from 16 GB to 64 GB, covering typical sizes for whole human genome, or k=26 for sizes from 4 GB to 16 GB. The minimum primary seed length depends mainly on the reference genome size and complexity. It needs to be long enough to resolve most reference positions uniquely. For whole human genome references, hash table construction typically fails with k < 16. The lower bound may be smaller for shorter genomes, or higher for less complex (more repetitive) genomes. The uniqueness threshold of `--ht-seed-len 16` for the 3.1Gbp human genome can be understood intuitively because log4(3.1 G) ≈ 16, so it requires at least 16 choices from 4 nucleotides to distinguish 3.1 G reference positions. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#accuracy-considerations) Accuracy Considerations For read mapping to succeed, at least one primary seed must match exactly (or with a single SNP when edited seeds are used). Shorter seeds are more likely to map successfully to the reference, because they are less likely to overlap variants or sequencing errors, and because more of them fit in each read. So for mapping accuracy, shorter seeds are mainly better. However, very short seeds can sometimes reduce mapping accuracy. Very short seeds often map to multiple reference positions, and lead the mapper to consider more false mapping locations. Due to imperfect modeling of mutations and errors by Smith-Waterman alignment scoring and other heuristics, occasionally these noise matches may be reported. Run time quality filters such as `--Aligner.aln_min_score` can control the accuracy issues with very short seeds. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#speed-considerations) Speed Considerations Shorter seeds tend to slow down mapping, because they map to more reference locations, resulting in more work such as Smith-Waterman alignments to determine the best result. This effect is most pronounced when primary seed length approaches the reference genome's uniqueness threshold, eg, K=16 for whole human genome. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#application-considerations) Application Considerations **Read Length**\---Generally, shorter seeds are appropriate for shorter reads, and longer seeds for longer reads. Within a short read, a few mismatch positions (variants or sequencing errors) can chop the read into only short segments matching the reference, so that only a short seed can fit between the differences and match the reference exactly. For example, in a 36 bp read, just one SNP in the middle can block seeds longer than 18 bp from matching the reference. By contrast, in a 250 bp read, it takes 15 SNPs to exceed a 0.01% chance of blocking even 27 bp seeds. **Paired Ends**\---The use of paired end reads can make longer seeds yield good mapping accuracy. DRAGEN uses paired end information to improve mapping accuracy, including with rescue scans that search the expected reference window when only one mate has seeds mapping to a given reference region. Thus, paired end reads have essentially twice the opportunity for an exact matching seed to find their correct alignments. **Variant or Error Rate**\---When read differences from the reference are more frequent, shorter seeds may be required to fit between the difference positions in a given read and match the reference exactly. **Mapping Percentage Requirement**\---If the application requires a high percentage of reads to be mapped somewhere (even at low MAPQ), short seeds may be helpful. Some reads that do not match the reference well anywhere are more likely to map using short seeds to find partial matches to the reference. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#maximum-seed-length) Maximum Seed Length The `--ht-max-ext-seed-len` option limits the length of extended seeds populated into the hash table. Primary seeds (length specified by `--ht-seed-len`) that match many reference positions can be extended to achieve more unique matching, which may be required to map seeds within the maximum hit frequency (`--ht-max-seed-freq`). Given a primary seed length k, the maximum seed length can be configured between k and k+128. The default is the upper bound, k+128. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#when-to-limit-seed-extension) When to Limit Seed Extension The `--ht-max-ext-seed-len` option is recommended for short reads, eg, less than 50 bp. In such cases, it is helpful to limit seed extension to the read length minus a small margin, such as 1-4 bp. For example, with 36 bp reads, setting `--ht-max-ext-seed-len` to 35 might be appropriate. This ensures that the hash table builder does not plan a seed extension longer than the read causing seed extension and mapping to fail at run time, for seeds that could have fit within the read with shorter extensions. While seed extension can be similarly limited for longer reads, eg, setting `--ht-max-ext-seed-len` to 99 for 100 bp reads, there is little utility in this because seeds are extended conservatively in any event. Even with the default k+128 limit, individual seeds are only extended to the lengths required to fit under the maximum hit frequency (`--ht-max-seed-freq`), and at most a few bases longer to approach the target hit frequency (`‑‑ht‑target-seed-freq`), or to avoid taking too many incremental extension steps. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#maximum-hit-frequency) Maximum Hit Frequency The `--ht-max-seed-freq` option sets a firm limit on the number of seed hits (reference genome locations) that can be populated for any primary or extended seed. If a given primary seed maps to more reference positions than this limit, it must be extended long enough that the extended seeds subdivide into smaller groups of identical seeds under the limit. If, even at the maximum extended seed length (`--ht-max-ext-seed-len`), a group of identical reference seeds is larger than this limit, their reference positions are not populated into the hash table. Instead, a single High Frequency record is populated. The maximum hit frequency can be configured from 1 to 256. However, if this value is too low, hash table construction can fail because too many seed extensions are needed. The practical minimum for a whole human genome reference, other options being default, is 8. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#accuracy-considerations-1) Accuracy Considerations Generally, a higher maximum hit frequency leads to more successful mapping. There are two reasons for this. First, a higher limit rejects fewer reference positions that cannot map under it. Second, a higher limit allows seed extensions to be shorter, improving the odds of exact seed matching without overlapping variants or sequencing errors. However, as with very short seeds, allowing high hit counts can sometimes hurt mapping accuracy. Most of the seed hits in a large group are not to the true mapping location, and occasionally one of these noise hits may be reported due to imperfect scoring models. Also, the mapper limits the total number of reference positions it considers, and allowing very high hit counts can potentially crowd out the actual best match from consideration. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#speed-considerations-1) Speed Considerations Higher maximum hit frequencies slow down read mapping, because seed mapping finds more reference locations, resulting in more work, such as Smith-Waterman alignments, to determine the best result. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#pangenome-reference) Pangenome Reference The DRAGEN Software enables the user to build a custom pangenome hash table from a set of population variants. The population variants are specified in a single multi-sample VCF file. * `--ht-graph-msvcf-file: Input file containing list of population variants, in multi-sample VCF format.` This replaces the previous options that were previously used to build a graph Reference that are now deprecated. List of deprecated options : * `--ht-pop-alt-contigs: Population based alternate contigs FASTA.` * `--ht-pop-alt-liftover: Liftover SAM file of population alternate contigs.` * `--ht-pop-snps: Population based SNPs VCF` ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#alt-contigs) ALT-Contigs The following options control building hash tables from references with ALT-contigs. See References with ALT contigs for more information. * `--ht-mask-bed`: Set a custom BED file that defines which regions to mask. If not provided, the DRAGEN software automatically applies BED files for hg38 and hg19 from `/fasta\_mask`. * `--ht-alt-liftover`: Set a liftover file to build a liftover based ALT-aware hash table. SAM liftover files for hg38DH and hg19 are provided in `/liftover`. * `--ht-allow-mask-and-liftover`: Allow the use of both `--ht-mask-bed` and `--ht-alt-liftover` together. * `--ht-suppress-mask`: Suppress automatic detection of the default mask bed files when building the hash table. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#decoy-contigs) Decoy Contigs * `--ht-decoys` The DRAGEN software automatically detects the use of hg19 and hg38 references and adds decoys to the hash table when they are not found in the FASTA file. Use the `--ht-decoys` option to specify the path to a decoys file. The default is `/liftover/hs\_decoys.fa`. * `--ht-suppress-decoys`: Suppress automatic detection of the default decoys file when building the hash table. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#processing-options) Processing Options * `--ht-num-threads` The `--ht-num-threads` option determines the maximum number of worker CPU threads that are used to speed up hash table construction. The default for this option is 8, with a maximum of 32 threads allowed. If your server supports execution of more threads, it is recommended that you use the maximum. For example, the DRAGEN servers contain 24 cores that have hyperthreading enabled, so a value of 32 should be used. When using a higher value, adjust `--ht-max-table-chunks` needs to be adjusted as well. The servers have 128 GB of memory available. * `--ht-max-table-chunks` The `--ht-max-table-chunks` option controls the memory footprint during hash table construction by limiting the number of ~1 GB hash table chunks that reside in memory simultaneously. Each additional chunk consumes roughly twice its size (~2 GB) in system memory during construction. The hash table is divided into power-of-two independent chunks, of a fixed chunk size, X, which depends on the hash table size, in the range 0.5 GB < X ≤ 1 GB. For example, a 24 GB hash table contains 32 independent 0.75 GB chunks that can be constructed by parallel threads with enough memory and a 16 GB hash table contains 16 independent 1 GB chunks. The default is `--ht-max-table-chunks` equal to `--ht-num-threads`, but with a minimum default `--ht-max-table-chunks` of 8. It makes sense to have these two options match, because building one hash table chunk requires one chunk space in memory and one thread to work on it. Nevertheless, there are build-speed advantages to raising `--ht-max-table-chunks` higher than `--ht-num-threads`, or to raising `--ht-num-threads` higher than `--ht-max-table-chunks`. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#size-options) Size Options * `--ht-mem-limit` Memory Limit. The `--ht-mem-limit` option controls the generated hash table size by specifying the DRAGEN card memory available for both the hash table and the encoded reference genome. The `‑‑ht‑mem-limit` option defaults to 32 GB when the reference genome approaches WHG size, or to a generous size for smaller references. Normally there is little reason to override these defaults. * `--ht-size` Hash Table Size. This option specifies the hash table size to generate, rather than calculating an appropriate table size from the reference genome size and the available memory (option `--ht-mem-limit`). Using default table sizing is recommended and using `--ht-mem-limit` is the next best choice. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#seed-population-options) Seed Population Options * `--ht-ref-seed-interval` Seed Interval. The `--ht-ref-seed-interval` option defines the step size between positions of seeds in the reference genome populated into the hash table. An interval of 1 (default) means that every seed position is populated, 2 means 50% of positions are populated, etc. Noninteger values are supported, eg, 2.5 yields 40% populated. Seeds from a whole human reference are easily 100% populated with 32 GB memory on DRAGEN boards. If a substantially larger reference genome is used, change this option. * `--ht-soft-seed-freq-cap` and `--ht-max-dec-factor` Soft Frequency Cap and Maximum Decimation Factor for Seed Thinning. Seed thinning is an experimental technique to improve mapping performance in high-frequency regions. When primary seeds have higher frequency than the cap indicated by the `--ht-soft-seed-freq-cap option`, only a fraction of seed positions are populated to stay under the cap. The `--ht-max-dec-factor` option specifies a maximum factor by which seeds can be thinned. For example, `--ht-max-dec-factor 3` retains at least 1/3 of the original seeds. `--ht-max-dec-factor 1` disables any thinning. Seeds are decimated in careful patterns to prevent leaving any long gaps unpopulated. The idea is that seed thinning can achieve mapped seed coverage in high frequency reference regions where the maximum hit frequency would otherwise have been exceeded. Seed thinning can also keep seed extensions shorter, which is also good for successful mapping. Based on testing to date, seed thinning has not proven to be superior to other accuracy optimization methods. * `--ht-rand-hit-hifreq` and `--ht-rand-hit-extend` Random Sample Hit with HIFREQ Record and EXTEND Record. Whenever a HIFREQ or EXTEND record is populated into the hash table, it stands in place of a large set of reference hits for a certain seed. Optionally, the hash table builder can choose a random representative of that set, and populate that HIT record alongside the HIFREQ or EXTEND record. Random sample hits provide alternative alignments that are very useful in estimating MAPQ accurately for the alignments that are reported. They are never used outside of this context for reporting alignment positions, because that would result in biased coverage of locations that happened to be selected during hash table construction. To include a sample hit, set `--ht-rand-hit-hifreq` to 1. The `--ht-rand-hit-extend` option is a minimum pre-extension hit count to include a sample hit, or zero to disable. Modifying these options is not recommended. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#seed-extension-control) Seed Extension Control DRAGEN seed extension is dynamic, applied as needed for particular K-mers that map to too many reference locations. Seeds are incrementally extended in steps of 2--14 bases (always even) from a primary seed length to a fully extended length. The bases are appended symmetrically in each extension step, determining the next extension increment if any. There is a potentially complex seed extension tree associated with each high frequency primary seed. Each full tree is generated during hash table construction and a path from the root is traced by iterative extension steps during seed mapping. The hash table builder employs a dynamic programming algorithm to search the space of all possible seed extension trees for an optimal one, using a cost function that balances mapping accuracy and speed. The following options define that cost function: * `--ht-target-seed-freq` Target Hit Frequency. The `--ht-target-seed-freq` option defines the ideal number of hits per seed for which seed extension should aim. Higher values lead to fewer and shorter final seed extensions, because shorter seeds tend to match more reference positions. * `--ht-cost-coeff-seed-len` Cost Coefficient for Seed Length The `--ht-cost-coeff-seed-len` option assigns the cost component for each base by which a seed is extended. Additional bases are considered a cost because longer seeds risk overlapping variants or sequencing errors and losing their correct mappings. Higher values lead to shorter final seed extensions. * `--ht-cost-coeff-seed-freq` Cost Coefficient for Hit Frequency. The `--ht-cost-coeff-seed-freq` option assigns the cost component for the difference between the target hit frequency and the number of hits populated for a single seed. Higher values result primarily in high-frequency seeds being extended further to bring their frequencies down toward the target. * `--ht-cost-penalty` Cost Penalty for Seed Extension. The `--ht-cost-penalty` option assigns a flat cost for extending beyond the primary seed length. A higher value results in fewer seeds being extended at all. Current testing shows that zero (0) is appropriate for this parameter. * `--ht-cost-penalty-incr` Cost Increment for Extension Step. The `--ht-cost-penalty-incr` option assigns a recurring cost for each incremental seed extension step taken from primary to final extended seed length. More steps are considered a higher cost because extending in many small steps requires more hash table space for intermediate EXTEND records, and takes substantially more run time to execute the extensions. A higher value results in seed extension trees with fewer nodes, reaching from the root primary seed length to leaf extended seed lengths in fewer, larger steps. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#pipeline-specific-hash-tables) Pipeline Specific Hash Tables ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#rna-seq) RNA-Seq When building a hash table, DRAGEN configures the options for DNA analysis by default. To run RNA-Seq data, you must build an RNA-Seq hash table by setting `--ht-build-rna-hashtable` to true. If running RNA-Seq alignment, use the original `--output-directory` instead of the automatically generated subdirectory. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#cnv) CNV If using the CNV pipeline, set `--ht-build-cnv-hashtable` to true. The command generates an additional Kmer hash map that is used in the CNV algorithm. Illumina recommends to always use the `--ht-build-cnv-hashtable` option, so you can perform CNV calling with the same hash table used for mapping and aligning. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#methylation) Methylation To run the methylation pipeline, you must build a methylation-specific hash table. DRAGEN can build a single-pass or legacy multi-pass methylation hash table. Methylation runs using a single-pass hash table are completed faster than the legacy multipass hash tables. Single-pass hash tables are recommended for building methylation tables and running analyses. Hash Table Type Hash Table Commands single-pass `--ht-methylated-combined=true` `--ht-seed-len 27` multi-pass `--ht-methylated=true` `--ht-seed-len 27` `--ht-max-seed-freq 16` #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#single-pass) Single-pass The following is an example of a single-pass hash table build. The example generates a combined hash table in your reference index folder under the methyl\_converted subdirectory. `dragen --build-hash-table true \ --output-directory $REFDIR \ --ht-reference $FASTA \ --ht-num-threads 40 \ --ht-methylated-combined=true \ --ht-seed-len 27` #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#multipass) Multipass Multi-pass methylation mapping requires building two special hash tables with reference bases converted from C to T in one table and G to A in the other table. The conversions are performed automatically when using the `--ht-methylated` command line option. The converted hash tables are generated in two subdirectories under the folder specified using the `--output-directory` command line option. The subdirectories are named CT\_converted and GA\_converted, corresponding with the base conversions. When using the hash tables for methylated alignment runs, make sure to refer to the `--output-directory` folder, not the subdirectories. The base conversions remove a significant amount of information from the hash tables. You might need to use different hash table parameters than in a conventional hash table build. The following options are recommended for building hash tables for mammalian species. `dragen --build-hash-table=true --output-directory $REFDIR --ht-reference $FASTA --ht-max-seed-freq 16 --ht-seed-len 27 --ht-num-threads 40 --ht-methylated=true` [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#hla) HLA -------------------------------------------------------------------------------------------------------------------------------------------------- To run the HLA caller, an HLA-specific anchored reference hash table must be built. Set `--ht-build-hla-hashtable` to true. The command will create a `anchored_hla` subdirectory inside the `--output-directory`. The HLA-specific reference subdirectory can be built at the same time as the primary reference construction. An HLA resource file is packaged with DRAGEN and located at the following path after installation: `/resources/hla/HLA_resource.v1.fasta.gz`. This file is used by default when building the HLA-specific anchored hash table. A custom file can be specified with `--ht-hla-reference`. See the HLA section for more information [Using Custom HLA Reference Files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#using-custom-hla-reference-files) [PreviousDRAGEN Reference Supportchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support) [NextDRAGEN DNA Pipelinechevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline) Last updated 7 months ago Was this helpful? * [Hash Table Background](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#hash-table-background) * [Automatic Reference Detection](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#automatic-reference-detection) * [Reference Seed Interval](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#reference-seed-interval) * [Hash Table Occupancy](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#hash-table-occupancy) * [Hash Table / Seed Length](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#hash-table-seed-length) * [Hash Table / Seed Extensions](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#hash-table-seed-extensions) * [Seed Frequency Limit and Target](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#seed-frequency-limit-and-target) * [References with ALT contigs](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#references-with-alt-contigs) * [Masked References](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#masked-references) * [Automatic masking](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#automatic-masking) * [Handling Decoy Contigs](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#handling-decoy-contigs) * [Prepare a Pangenome Reference](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#prepare-a-pangenome-reference) * [Usage](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#usage) * [Inputs](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#inputs) * [Command line options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#command-line-options) * [Output](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#output) * [Prepare a linear Reference](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#prepare-a-linear-reference) * [Usage](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#usage-1) * [Input](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#input) * [Command line options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#command-line-options-1) * [Options for advanced users](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#options-for-advanced-users) * [Primary Seed Length](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#primary-seed-length) * [Maximum Seed Length](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#maximum-seed-length) * [Maximum Hit Frequency](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#maximum-hit-frequency) * [Pangenome Reference](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#pangenome-reference) * [ALT-Contigs](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#alt-contigs) * [Decoy Contigs](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#decoy-contigs) * [Processing Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#processing-options) * [Size Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#size-options) * [Seed Population Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#seed-population-options) * [Seed Extension Control](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#seed-extension-control) * [Pipeline Specific Hash Tables](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#pipeline-specific-hash-tables) * [RNA-Seq](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#rna-seq) * [CNV](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#cnv) * [Methylation](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#methylation) * [HLA](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome#hla) Was this helpful? Copy dragen --build-hash-table true [options] --ht-reference --output-directory --- # Splice Variant Caller | DRAGEN v4.3 | DRAGEN The identification of alternatively spliced isoforms (using their constitutive splice variants) and their functional effects is of high importance in the study of genetic variation and diseases, including cancer and neurological disorders. The main types of alternative splicing events resulting in splice variants are: * Exon skipping * Intron retention * Mutually exclusive exons * Alternative 5' splice site * Alternative 3' splice site [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/splice-variant-caller#running-splice-variant-caller) Running Splice Variant Caller -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- When enabled with the `--enable-rna-splice-variant=true` option added to an RNA Map/Align job, DRAGEN runs a Splice Variant caller by taking advantage of its fast and highly accurate splice-aware read mapper/aligner that aligns to the whole genome to identify novel alternative Splice Junction (SJ) candidates. These candidates can be filtered by additional information provided such as a "normals list" and a "target regions list", or whitelisted with a "knowns" list. Next during the read sorting phase, evidence for these alternative splices candidates (_alts_) vs. reference splicing are accumulated. Finally, each of the candidates are scored based on the accumulated read evidence and the results are written to TSV and VCF files for downstream tertiary analysis. Following is an example command line. Copy dragen \ -r \ -1 \ -2 \ -a \ --output-dir \ --output-file-prefix \ --RGID \ --RGSM \ --enable-rna true \ --enable-rna-splice-variant true [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/splice-variant-caller#splice-variant-optional-input-files) Splice Variant Optional Input Files -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- In addition to the required inputs listed in the above example (i.e. paired fastq reads, reference hashtable, and annotation), the following 3 optional input resource files can be provided to help provide better precision by reducing FP count. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/splice-variant-caller#normals-list) Normals List A list of Normal splice variants that will be filtered out of the final output (i.e. operating as a blacklist), as long as they are not in the "knowns" list, using the **"--rna-splice-variant-normals"** option. The format of this file should be a tab separated file in the same format as the SJ.out.tab, except only the first 4 columns are used, i.e. 1. contig name 2. first base of the splice junction (1-based) 3. last base of the splice junction (1-based) 4. strand (0: undefined, 1: +, 2: -) To create a Normals list file, a collection of DRAGEN RNA mapper output **SJ.out.tab** files for at least 30 samples can be used along with a simple script to process all the SJs in these files. The pseudo code block below describes the function of this script: ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/splice-variant-caller#knowns-list) Knowns List A list of known splice variants that are exempt from being filtered out of the final output (i.e. operating as a whitelist), using the **"--rna-splice-variant-knowns"** option. The format of the file should be a tab separated file in the same format as the SJ.out.tab with 9 columns present, except only the first 4 columns are evaluated, i.e. 1. contig name 2. first base of the splice junction (1-based) 3. last base of the splice junction (1-based) 4. strand (0: undefined, 1: +, 2: -) By default, the caller will not consider any splice variant candidates that are found in the input annotation file since it is looking for denovo variants, unless it is included in the _knowns_ list which directs it not to discard the specified candidate. Note that some newer gene annotation models have added alt transcripts that contain clinically relevant splice variants, which causes the DRAGEN to skip reporting them. To ensure these are reported, the user may want to pass these in with a _knowns_ file containing these common variants if they are found in the annotation that is used. An example is shown below using hg38 coordinates specifying the _MET exon 14 skip_, _EGFRv3_, and _ARv7_ alt splicing events, respectively. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/splice-variant-caller#target-regions-bed) Target Regions BED A list of regions that called splice variants must fall within using the **"--rna-splice-variant-regions"** option. Any splice variant candidates will be excluded if they are not within these regions. This file should be in BED file format with the following info, except that the regions are 1-based 1. chromosome id 2. start position (1-based) 3. end position (1-based) 4. region (i.e. gene) name [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/splice-variant-caller#splice-variant-output-files) Splice Variant Output Files ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The detected splice variants are output as two separate TSV files for the intragenic and intergenic candidates, and as a VCF for the intragenic candidates. The following categories are used when accumulating read counts for each alt SJ candidate: * _DedupUniqueSupportingReads_ - Non-duplicate marked reads that are unique and precisely align with the SJ * _DupUniqueSupportingReads_ - Duplicate marked reads that are unique and precisely align with the SJ * _DedupUniqueNonsupportingReads_ - Non-duplicate marked reads that are unique but don't support the splice variant * _DupUniqueNonsupportingReads_ - Duplicate marked reads that are unique but don't support the splice variant To be counted, a paired read alignment: 1. must be primary and properly paired 2. must contain a splice junction (i.e. an alignment gap in the CIGAR containing skip ops) 3. must have overhangs on either side of the skip that are at least 6 bases 4. considered to be _"unique"_ only if NH=1 and the MAPQ > 35 ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/splice-variant-caller#splice-variant-tsv-files) Splice Variant TSV Files These two output files are named: * **.splice\_variants.tsv** which contains the _intragenic_ alt splice junctions that result in transcript variants * **.splice\_variant\_fusions.tsv** which contains the _intergenic_ alt splice junctions that result fusions across genes Each detected splice junction contains the following columns: 1. **gene\_start** - Gene name(s) at the start of the SJ. Multiple genes are separated by a semicolon 2. **gene\_end** - Gene name(s) at the end of the SJ. Multiple genes are separated by a semicolon 3. **chromosome** - Chromosome containing the SJ 4. **start** - SJ's start position (1-based genomic coordinate) 5. **end** - SJ's start position (1-based genomic coordinate) 6. **strand** - Detected strand for the SJ (1: +, 2: -) 7. **motif** - intron motif 8. **annotated** - True if annotated, otherwise False 9. **split\_unique\_reads\_ref** - DedupUniqueNonsupportingReads count that support reference 10. **split\_total\_reads\_ref** - DupUniqueNonsupportingReads + DedupUniqueNonsupportingReads count that support reference 11. **split\_unique\_reads\_alt** DedupUniqueSupportingReads count that support variant 12. **split\_total\_reads\_alt** - DupUniqueSupportingReads + DedupUniqueSupportingReads count that support variant 13. **max\_spliced\_alignment\_overhang** - maximum spliced alignment overhang from all supporting reads 14. **score** - The splice junction variant score (ranging from 0.0 to 1.0). Currently, this is just a linear function of the number of _split\_unique\_reads_ divided by 10, i.e. equals _MIN(1.0, split\_unique\_reads\_alt/10)_ Note: * In the _intragenic_ output file containing transcript variant splice junctions, the **gene\_start** and **gene\_end** columns must match. * In the _intergenic_ output file containing fusions from splice junctions, the **gene\_start** and **gene\_end** columns must be different. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/splice-variant-caller#splice-variant-vcf-file) Splice Variant VCF File This file contains the detected intra-genic splice junction variants that are not filtered out, and are written into a zipped VCF file titled **.splice\_variants.vcf.gz**, where each splice variant candidate is written as a one-line VCF record containing the fields below: * CHROM - Chromosome of the splice * POS - SJ start position (1-based) i.e. first base of intron * ID - "." (unused) * REF - Base from the reference genome FASTA at the SJ start position * ALT - "" * QUAL - The junction score from 0.0 - 1.0 * FILTER - Semicolon separated list of filters: LowQ and LowUniqueAlignment * INFO - See the possible "Info fields below" * FORMAT - AD:DP * SAMPLE - Counts for {DedupUniqueSupportingReads}:{DedupUniqueNonsupportingReads} The following lines of the VCF header describe columns 5 to 10 (last 6 columns) **Note on Filter Thresholds** The passing thresholds for the LowQ and LowUniqueAlignments filters are fixed to the settings below. **Filter** **Description** **Value** LowQ Below splice variant score threshold less than 1.0 LowUniqueAlignments Below unique supporting read count threshold less than 2 ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/splice-variant-caller#output-merged-with-fusion-caller) Output Merged with Fusion Caller When the splice variant caller and gene fusion caller are both enabled, the passing and failed intergenic fusion SJ's will also be merged into the relevant fusion output TSV files. The **passing** calls get added to the fusion caller's _.fusion\_candidates.final_ file. The tab separated fields are described below. **Field Names** **Description** FusionGene Left and Right gene names (separated by '--') Score Value between 0 and 1 LeftBreakpoint, RightBreakpoint The location for left and right sides of the splice with three colon separated fields: chromosome:coordinate:strand(+/-) Gene1Location, Gene2Location Splice Variant caller always outputs "**SpliceVar**" here instead of Exon/Intron location Gene1Sense, Gene2Sense Always TRUE for by design Gene1Id, Gene2Id Long form ID (i.e. for Gencode it is usually "ENSG.version") NumSplitReads Taken from the _dedupUniqueSupportingReads_ count (i.e. _split\_unique\_reads\_alt_ column value) NumSoftClippedReads, NumPairedReads These values are not used by RSV caller and are set to '0' ReadNames Not provided by this caller and set to 'N/A' The **failing** calls get added into the fusion caller's _.fusion\_candidates.filter\_info_ output file. The output fields are the same as described above for the "final" output file, with the addition of the FILTER\_INFO field in the first column. The value in this field will be "**RSV\_FILTER:**" followed by the specific filters that are not passing, as described in the table below. **Filter Names** **Description** LOW\_QUAL Below the "low quality score" threshold LOW\_UNIQUE\_ALIGNS Number of unique anchors either on left or right side are below the MIN\_UNIQUE\_ALIGNS=2 threshold LOW\_EVIDENCE\_OR\_OVERHANG Not meeting the SJ.out read count vs. splice length and overhang requirements READTHROUGH Gene partner is the next downstream annotated gene [PreviousRNA Variant Callingchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-variant-calling) [NextDRAGEN Single-Cell Pipelinechevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline) Last updated 7 months ago Was this helpful? * [Running Splice Variant Caller](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/splice-variant-caller#running-splice-variant-caller) * [Splice Variant Optional Input Files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/splice-variant-caller#splice-variant-optional-input-files) * [Normals List](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/splice-variant-caller#normals-list) * [Knowns List](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/splice-variant-caller#knowns-list) * [Target Regions BED](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/splice-variant-caller#target-regions-bed) * [Splice Variant Output Files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/splice-variant-caller#splice-variant-output-files) * [Splice Variant TSV Files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/splice-variant-caller#splice-variant-tsv-files) * [Splice Variant VCF File](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/splice-variant-caller#splice-variant-vcf-file) * [Output Merged with Fusion Caller](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/splice-variant-caller#output-merged-with-fusion-caller) Was this helpful? Copy Generate_Normals(SJ_out_tab_files) { Typedef tuple(int,int,int,int) = SJ_key // for contig #, start, end, strand Typedef dict(SJ_key, int) = SJ_count Const MIN_UNIQUE_READS = 3 Const MIN_OCCURRENCE = 2 SJ_count All_SJ = {} list Normal_SJ = [] // create list of all candidate SJ for sj_file in SJ_out_tab_files open(sj_file,'r') for each sj in sj_file if sj.unique_reads >= MIN_UNIQUE_READS if exists sj in All_SJ All_SJ[sj] += 1 else All_SJ[sj] = 1 close(sj_file) // save any SJ that occur in enough samples for each (sj, count) in All_SJ if count >= MIN_OCCURRENCE Normal_SJ.append(sj) // Write out the Normals.txt Normal_SJ.sort(); normals_file = open("Normals.txt",'w') for each sj in Normal_SJ write(normals_file,sj[0..3],0,0,0,0,0) // pad sj tuple's 4 vals with 5 unused field 0's close(normals_file) } Copy chr7 116771655 116774880 1 0 0 0 0 0 chr7 55019366 55155829 1 0 0 0 0 0 chrX 67686127 67694672 1 0 0 0 0 0 Copy ##ALT= ##QUAL= ##FILTER= ##FILTER= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##INFO= ##FORMAT= ##FORMAT= --- # RNA Alignment | DRAGEN v4.3 | DRAGEN ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#rna-alignment) RNA Alignment The DRAGEN RNA pipeline uses the DRAGEN RNA-Seq spliced aligner. Mapping of short seed sequences from RNA-Seq reads is performed similarly to mapping DNA reads. In addition, splice junctions (the joining of noncontiguous exons in RNA transcripts) near the mapped seeds are detected and incorporated into the full read alignments. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#alignment-output) Alignment Output The output files generated when running DRAGEN in RNA mode are similar to those generated in DNA mode. RNA mode also produces extra information related to spliced alignments. Details regarding the splice junctions are present both in the SAM alignment record and an additional file, the SJ.out.tab file. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#bam) BAM The output BAM file meets the SAM specification and is compatible with downstream RNA-Seq analysis tools. **RNA-Seq BAM Tags** The following BAM tags are emitted alongside spliced alignments. Tag Description `XS:A` The XS tag denotes the strand orientation of an intron. See \[Compatibility with Cufflinks\]{.underline}. `NH:i` A standard SAM tag indicating the number of reported alignments that contains the query in the current record. This tag may be used for downstream tools such as featureCounts. `HI:i` A standard SAM tag denoting the query hit index, with its value indicating that this alignment is the i-th one stored in the SAM. Its value ranges from 1 ... NH. This tag may be used for downstream tools such as featureCounts. `jM:B` The jM tag lists the intron motifs for all junctions in the alignments. It has the following definitions `jM:B` Definition 0 non-canonical 1 GT/AG 2 CT/AC 3 GC/AG 4 CT/GC 5 AT/AC 6 GT/AT If a gene annotations file is used during the map/align stage, and the splice junction is detected as an annotated junction, then 20 is added to its motif value. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#compatibility-with-cufflinks) Compatibility with Cufflinks Cufflinks might require spliced alignments to emit the `XS:A` strand tag. This tag is present in the SAM record if the alignment contains a splice junction. The possible values for `XS:A` strand tag are as follows: '.' (undefined), '+' (forward strand), '-' (reverse strand), or '\*' (ambiguous). By default, if the spliced alignment has an undefined strand or an ambiguous (conflicting) strand, then the alignment output is suppressed. These alignments can be output into the output alignment file by setting the `--no-ambig-strand` option to 1. Cufflinks also expects that the MAPQ for a uniquely mapped read is a single value. This value is specified by the `--rna‑mapq-unique` option. To force all uniquely mapped reads to have a MAPQ equal to this value, set `‑‑rna‑mapq‑unique` to a nonzero value. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#sj.out.tab) SJ.out.tab Along with the alignments emitted in the SAM/BAM file, an additional SJ.out.tab file summarizes the high confidence splice junctions in a tab-delimited file. The columns for this file are as follows: 1. contig name 2. first base of the splice junction (1-based) 3. last base of the splice junction (1-based)strand (0: undefined, 1: +, 2:-) 4. strand (0: undefined, 1: +, 2: -, 3: ambiguous) 5. intron motif: 0: noncanonical, 1: GT/AG, 2: CT/AC, 3: GC/AG, 4: CT/GC, 5: AT/AC, 6: GT/AT 6. 0: unannotated, 1: annotated, only if an input gene annotations file was used 7. number of uniquely mapping reads spanning the splice junction 8. number of multimapping reads spanning the splice junction 9. maximum spliced alignment overhang The maximum spliced alignment overhang (column 9) field in the SJ.out.tab file is the anchoring alignment overhang. For example, if a read is spliced as `ACGTACGT------------ACGT`, then the overhang is 4. For the same splice junction, across all reads that span this junction, the maximum overhang is reported. The maximum overhang is a confidence indicator that the splice junction is correct based on anchoring alignments. There are two SJ.out.tab files generated by the DRAGEN host software, an unfiltered version and a filtered version. The records in the unfiltered file are a consolidation of all spliced alignment records from the output SAM/BAM. However, the filtered version has a much higher confidence for being correct due to the use of the following filters. A splice junction entry in the SJ.out.tab file is filtered out if any of these conditions are met: * SJ is a noncanonical motif and is only supported by < 3 unique mappings. * SJ of length > 50000 and is only supported by < 2 unique mappings. * SJ of length > 100000 and is only supported by < 3 unique mappings. * SJ of length > 200000 and is only supported by < 4 unique mappings. * SJ is a noncanonical motif and the maximum spliced alignment overhang is < 30. * SJ is a canonical motif and the maximum spliced alignment overhang is < 12. The filtered SJ.out.tab is recommended for use with any downstream analysis or post processing tools. Alternatively, you can use the unfiltered SJ.out.tab and apply your own filters (for example, with basic awk commands). Note that the filter does not apply to the alignments present in the BAM or SAM file. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#chimeric.out.junction-file) Chimeric.out.junction File If there are chimeric alignments present in the sample, then a supplementary Chimeric.out.junction file is also output. This file contains information about split-reads that can be used to perform downstream gene fusion detection. Each line contains one chimerically aligned read. The columns of the file are as follows: 1. Chromosome of the donor. 2. First base of the intron of the donor (1-based). 3. Strand of the donor. 4. Chromosome of the acceptor. 5. First base of the intron of the acceptor (1-based). 6. Strand of the acceptor. 7. N/A---not used, but is present to be compatible with other tools. It will always be `1`. 8. N/A---not used, but is present to be compatible with other tools. It will always be `*`. 9. N/A---not used, but is present to be compatible with other tools. It will always be `*`. 10. Read name. 11. First base of the first segment, on the + strand. 12. CIGAR of the first segment. 13. First base of the second segment. 14. CIGAR of the second segment. CIGARs in this file follow the standard CIGAR operations as found in the SAM specification, with the addition of a gap length L that is encoded with the operation p. For paired end reads, the sequence of the second mate is always reverse complemented before determining strandedness. The following is an example entry that shows two chimerically aligned read pairs, in which one of the mates is split, mapping segments of chr19 to chr12. Also shown are the corresponding SAM records associated with these entries. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#mapping-metrics) Mapping Metrics The RNA Pipeline reports summary and per read group statistics pertaining to read mapping in the `mapping_metrics.csv` file. The majority of the matrics are as described in the [DNA mapping metrics](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/qc-metrics-reporting#mapping-and-aligning-metrics) section, but the metrics that are specific to RNA-seq are listed below. * **Filtered rRNA reads**\---Total number of ribosomal RNA reads that are filtered out with the `--rrna-filter-enable` option. * **Mitochondrial reads excluded**\---Total number of reads detected to be in ChrM if the `--rna-mapping-metrics-exclude-chrm` option is enabled. * **Mapped reads adjusted for filtered mapping**\---Adjusted count of mapped reads by adding in the filtered rRNA reads. * **Mapped reads adjusted for excluded mapping**\---Adjusted count of mapped reads by adding in the excluded mitocondrial reads. * **Mapped reads adjusted for filtered and excluded mapping**\---Adjusted count of mapped reads by adding in both the filtered rRNA and excluded mitocondrial reads. * **Unmapped reads adjusted for filtered mapping**\---Adjusted count of unmapped reads by subtracting out the filtered rRNA reads. * **Unmapped reads adjusted for excluded mapping**\---Adjusted count of unmapped reads by subtracting out the excluded mitocondrial reads. * **Unmapped reads adjusted for filtered and excluded mapping**\---Adjusted count of unmapped reads by subtracting out both the filtered rRNA and the excluded mitocondrial reads. * **Reads with splice junction**\---Total number of reads that included a spliced alignment that crosses an intron ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#rna-alignment-options) RNA Alignment Options The aligner stage of the RNA spliced aligner uses Smith-Waterman Alignment Scoring options and Splicing Scoring Options. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#smith-waterman-alignment-scoring-options) Smith-Waterman Alignment Scoring Options Refer to \[Smith-Waterman Alignment Scoring Settings\]{.underline} for more details about the alignment algorithm used within DRAGEN. The following scoring options are specific to the processing of canonical and noncanonical motifs within introns. * `--Aligner.intron-motif12-pen` The `--Aligner.intron-motif12-pen` option controls the penalty for canonical motifs 1/2 (GT/AG, CT/AC). The default value calculated by the host software is `1 * (match-score + mismatch-pen)`. * `--Aligner.intron-motif34-pen` The `--Aligner.intron-motif34-pen` option controls the penalty for canonical motifs 3/4 (GC/AG, CT/GC). The default value calculated by the host software is `3 * (match-score + mismatch-pen)`. * `--Aligner.intron-motif56-pen` The `--Aligner.intron-motif56-pen` option controls the penalty for canonical motifs 5/6 (AT/AC, GT/AT). The default value calculated by the host software is `4 * (match-score + mismatch-pen)`. * `--Aligner.intron-motif0-pen` The `--Aligner.intron-motif0-pen` option controls the penalty for noncanonical motifs. The default value calculated by the host software is `6 * (match-score + mismatch-pen)`. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#splicing-scoring-options) Splicing Scoring Options * `--Mapper.min-intron-bases` For RNA-Seq mapping, a reference alignment gap can be interpreted as a deletion or an intron. In the absence of an annotated splice junction, the min-intron-bases option is a threshold gap length separating this distinction. Reference gaps at least this long are interpreted and scored as introns, and shorter reference gaps are interpreted and scored as deletions. However, alignments can be returned with annotated splice junctions shorter than this threshold. * `--Mapper.max-intron-bases` The max-intron-bases option controls the largest possible intron that is reported, which useful for preventing false splice junctions that would otherwise be reported. Set this option to a value that is suitable to the species you are mapping against. * `--Mapper.ann-sj-max-indel` For RNA-seq, seed mapping can discover a reference gap in the position of an annotated intron, but with slightly different length. If the length difference does not exceed this option, the mapper investigates the possibility that the intron is present exactly as annotated, but an indel on one side or the other near the splice junction explains the length difference. Indels longer than this option and very near annotated splice junctions are not likely to be detected. Higher values may increase mapping time and false detections. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#duplicate-marking) Duplicate Marking DRAGEN RNA can detect duplicate reads, which are defined as fragments that have both ends mapping to the same (clipping-adjusted) position during alignment. In RNA-Seq data, the reads can represent PCR duplicates during library prep or as a result from deep coverage of highly expressed regions. If `--enable-duplicate-marking` is set to true, duplicate fragments are marked in the BAM file and the total number of duplicate reads is reported as a mapping metric. Marking of duplicates does not affect gene expression quantification and gene fusion calling. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#downsampling) Downsampling DRAGEN RNA also supports internal downsampling, which is a process by which a random sub-sample of reads is selected from the dataset after trimming and alignment for downstream analysis. In RNA-Seq, this can be useful in two ways - it can speed up analysis of samples with excessively high coverage, and it can allow for more accurate cross-comparisons between different samples. If `--enable-down-sampler` is set to true and a value specified for `--down-sampler-reads`, DRAGEN will use only that many RNA fragments (including both Read 1 and Read 2) for quantification, fusion, variant calling and output to BAM. Please note the the entire input dataset is still used for the generation of trimming, fastqc, and mapping metrics. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#ribosomal-rna-filtering) Ribosomal RNA filtering Ribosomal RNA (rRNA) sequences can contribute a large fraction of reads in some RNA-Seq datasets, depending on the sample type and library prep method. You can use the DRAGEN RNA pipeline to filter rRNA reads during alignment, because the reads are not relevant for downstream analysis. By filtering rRNA, you can reduce run time and file size and avoid deep read alignment pile ups at rRNA repeat loci on the genome to make downstream analysis of RNA BAM files easier. rRNA filtering relies on a decoy contig with the rRNA sequence included in the reference hash table. Any read that maps to the decoy contig, including multimappers, is tagged with rRNA and is not mapped in the output. NOTE: The rrna filter option only accepts a single contig by default. In the event multiple contigs need to be provided, they can be concatenated using a 1kb N mask between them, and added to the reference FASTA while creating the hash table. NOTE: rRNA filtering is not supported with `chm13`\-based references and it will be automatically disabled. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#command-line-options) Command-Line Options The following are the required command-line options for rRNA filtering. * `--rrna-filter-enable=true`\--Enables rRNA filtering. Set to `true` to enable rRNA filtering. The default value is `false`. * `--rrna-filter-contig`\--Specify the name of the rRNA sequences to use for filtering. If you do not specify a value, the default `gl000220` is provided for human genome alignments by using the reference autodetect feature. `gl000220` is an unplaced contig included in hg19 and hg38 genomes, which include a full copy of the rRNA repeat. For other genomes, you must include a rRNA decoy contig when creating a hash table. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#output-files) Output Files All rRNA filtered reads are left unaligned in the BAM files and tagged `ZS:Z:FLT`. The number and percentage of filtered rRNA reads is reported as a mapper metric `Adjustment of reads matching filter contigs`. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#polya-trimming) PolyA trimming PolyA tails may be trimmed by including the settings `--read-trimmers polya` or `--soft-read-trimmers polyg,polya` (Note: polyg soft trimming is enabled by default). The minimum number of poly-A/poly-T bases required for trimming may be set using `--trim-polya-min-trim`. The default threshold is 20 poly-A/poly-T bases. Refer to [DNA pipeline read trimmers](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#read-trimming) section for usage of read trimmers options. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#polya-trimming-by-read-orientation) PolyA trimming by read orientation The PolyA trimmer determines which end of the reads to trim for poly-A and poly-T sequences based on the library type. For example, for Illumina forward stranded paired reads the trimmer will trim poly-A sequences at 3' end of read 1 and poly-T sequences at 5' end of read 2. If `--rna-library-type` is not provided or set to autodetect (`A`), the trimmer assumes the library is unstranded and trims poly-A sequences from 3' end of each read and poly-T sequences from 5' end of each read. The option `--rna-library-type` is described in the [Gene Expression](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/gene-expression-quantification###QuantificationOptions) section. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#mapq-scoring) MAPQ Scoring By default, the MAPQ calculation for RNA-Seq is identical to DNA-Seq. The primary contributor to MAPQ calculation is the difference between the best and second-best alignment scores. Therefore, adjusting the alignment scoring parameters impacts the MAPQ estimate. These adjustments are outlined in [Smith-Waterman Alignment Scoring Settings](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#smith-waterman-alignment-scoring-options) . The `--mapq-strict-sjs` option is specific to RNA, and applies where at least one exon segment is aligned confidently, but there is ambiguity regarding possible splice junctions. When this option is set to 0, a higher MAPQ value is returned, expressing confidence that the alignment is at least partially correct. When this option is set to 1, a lower MAPQ value is returned, reflecting the splice junction ambiguity. Some downstream tools, such as Cufflinks, expect the MAPQ value to be a unique value for all uniquely mapped reads. This value is specified with the `--rna-mapq-unique` option. Setting this option to a nonzero value overrides all MAPQ estimates based on alignment score. Instead, all uniquely mapped reads have a MAPQ set to the value of `--rna-mapq-unique`. All multimapped reads have a MAPQ value of `int(-10*log10(1 ‑ 1/NH)`, where the NH value is the number of hits (primary and secondary alignments) for that read. [PreviousDRAGEN RNA Pipelinechevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline) [NextGene Fusion Detectionchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/gene-fusion-detection) Last updated 7 months ago Was this helpful? * [RNA Alignment](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#rna-alignment) * [Alignment Output](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#alignment-output) * [RNA Alignment Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#rna-alignment-options) * [Duplicate Marking](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#duplicate-marking) * [Downsampling](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#downsampling) * [Ribosomal RNA filtering](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#ribosomal-rna-filtering) * [PolyA trimming](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#polya-trimming) * [MAPQ Scoring](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#mapq-scoring) Was this helpful? Copy chr19 580462 + chr12 120876182 + 1 * * R_15448 571532 49M8799N26M8p49M26S 120876183 49H26M chr19 580462 + chr12 120876182 + 1 * * R_15459 571552 29M8799N46M8p29M46S 120876183 29H46M R_15448:1   99    chr19   571531      60  49M8799N26M  =       580413 R_15448:2   147   chr19   580413      60  49M26S       =       571531 R_15448:2   2193  chr12   120876182   15  49H26M       chr19   571531 R_15459:1   99    chr19   571551      60  29M8799N46M  =       580433 R_15459:2   147   chr19   580433      4   29M46S       =       571551 R_15459:2   2193  chr12   120876182   15  29H46M       chr19   571551 --- # Ploidy Calling | DRAGEN v4.3 | DRAGEN [Ploidy Estimatorchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/ploidy-calling/ploidy-estimator) [Ploidy Callerchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/ploidy-calling/ploidy-caller) [PreviousFilter Duplicate Variantschevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/variant-deduplication) [NextPloidy Estimatorchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/ploidy-calling/ploidy-estimator) Last updated 7 months ago Was this helpful? Was this helpful? --- # Azure Batch Run Modes | DRAGEN v4.3 | DRAGEN ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch/run-azure-modes#run-dragen-vm-on-azure-batch) Run DRAGEN VM on Azure Batch Use the following information to run the DRAGEN virtual machine (VM) on Microsoft Azure Batch. For information on using DRAGEN, see the see the [DRAGEN User Guide Section](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3) . For information on using Azure, see the Azure documentation available on the Microsoft site. 1. Navigate to the Microsoft Azure portal, and then sign in. 2. Select **Marketplace**. 3. Select **View Private Offers**, and then select **DRAGEN on Azure**. 4. Select **Create**. Starting from a preset configuration option is not recommended for DRAGEN. 5. Select a subscription and resource group from the drop-down menus, or select **Create New**. 6. Enter a name for the virtual machine. 7. Select a region that is compatible with the NP-series. See the Azure documentation available on the Microsoft site for more information. 8. Select DRAGEN and the current version as the image. 9. Select a storage size from the Size drop-down list. Only NP10 and NP20 sizes are compatible. 10. Configure any additional VM settings. For your disk type, DRAGEN recommends using Premium SSD for optimal performance. For information, see the Azure documentation available on the Microsoft site. 11. When finished, select **Review + Create** 12. To launch the VM, select **Create**. 13. After deployment completes, select **Go to Resource**. After your VM deploys, you can connect to the DRAGEN VM via the Azure Cloud Shell or another client of your choice. For more information on using a VM, see the Azure documentation available on the Microsoft site. For more information on DRAGEN analysis and command line options, ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch/run-azure-modes#run-dragen-with-arm) Run DRAGEN With ARM You can also run DRAGEN on Azure Batch using an Azure Resource Manager (ARM) template available on the DRAGEN Multi-Cloud support site. The ARM template only includes the parameters required to run DRAGEN on Azure Batch. See the Microsoft Azure documentation available on the Microsoft site for information on configuring additional parameters. Running DRAGEN using an ARM template enables the following advanced options. * Incorporating DRAGEN into an existing infrastructure. * Automating deployments with CI/CD pipelines. * Customizing the DRAGEN deployment. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch/run-azure-modes#storage-account-parameters) Storage Account Parameters The ARM template available on the DRAGEN Multi-Cloud support site creates a storage account and container. To use an existing Azure Blob storage account, specify the following input parameters in the ARM template. * `storageNewOrExisting: existing` * `storageAccountName: ` #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch/run-azure-modes#run-dragen-using-arm-template) Run DRAGEN Using ARM Template Use the following instructions to run DRAGEN on Azure Batch using the ARM template available on the DRAGEN Multi-Cloud support site. 1. Download the ARM template available on the DRAGEN Multi-Cloud support site. 2. Enter the following commands. You can enter additional command line options to further customize the run, including maximum Batch job and task run time. See the Azure CLI documentation available on the Microsoft site for more information. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch/run-azure-modes#use-dragen-with-the-azure-batch-cli) Use DRAGEN With the Azure Batch CLI After creating and authenticating your Azure Batch account, use the following instructions to run DRAGEN with the Azure Batch CLI. To run a DRAGEN Batch task, create a `task.json` file. The `task.json` file contains information on the Batch task, resource files, and output files. See Create the JSON file for information. You can then use the JSON file in the create Batch task command. For more information on creating Azure Batch accounts and using the Azure Batch CLI, see the Azure Batch documentation available on the Microsoft site. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch/run-azure-modes#create-the-json-file) Create the JSON file To set up the `task.json` to use in the Batch task create command, use the following structure. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch/run-azure-modes#batch-task) Batch Task Use the following instructions to configure the Batch task. For more information using the Azure Batch CLI, see the Azure Batch documentation available on the Microsoft site. 1. Create the batch job using the following command. 1. Enter the following command line to create the batch task. To use files located in a private Azure Blob storage account, see the Azure Blob CLI documentation available on the Microsoft site. The following is an example Batch task configuration. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch/run-azure-modes#resource-files) Resource Files To add resource files to the Batch node, use the `resourceFiles` configuration in the `task.json`. The following example specifies the genome and FASTQ files. To use files located in a private Azure Blob storage account, see the Azure Blob CLI documentation available on the Microsoft site. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch/run-azure-modes#output-files) Output Files To specify the location to write output files, use the `outputFiles` configuration in the `task.json`. The following command-line example places output logs and DRAGEN files in the specified storage container. To generate a storage container URL, use a SAS token. For more information on accessing a Blob storage container using a SAS token, see the Azure Blob CLI documentation available on the Microsoft site. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch/run-azure-modes#example-json-file) Example JSON File The following is an example `task.json` file. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch/run-azure-modes#stream-files) Stream Files DRAGEN can stream input FASTQ and BAM files from private Azure Blob containers. The genome file must be located locally on the node. DRAGEN does not support streaming from public Blob containers. **Stream From Azure Blob Storage** Use the following command as the Batch task command. If using the following command, you do not need to specify `resourceFiles` in `task.json`. **Stream From FASTQ List** You can use a FASTQ list file to reference and stream FASTQ files. The FASTQ list file must be local to the node. The FASTQ files referenced in the FASTQ list can be URLs to files on a Blob storage account. To configure `resourceFiles` to stream from a FASTQ file list file, use the following command. The FASTQ files in the following command are located on Blob storage account. The `task.json` file is structured as follows. Use the following command as the Batch task command. If using the following command, you do not need to specify `resourceFiles` in `task.json`. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch/run-azure-modes#create-the-batch-task) Create the Batch Task After you have set up the `task.json` file, you can use this file and Batch job ID to create the Azure Batch task with the following command. For more information on creating Azure Batch tasks, see the Azure Batch documentation available on the Microsoft site. [PreviousDRAGEN on Microsoft Azure Batchchevron-left](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch) [NextDRAGEN Licensingchevron-right](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing) Last updated 4 months ago Was this helpful? * [Run DRAGEN VM on Azure Batch](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch/run-azure-modes#run-dragen-vm-on-azure-batch) * [Run DRAGEN With ARM](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch/run-azure-modes#run-dragen-with-arm) * [Use DRAGEN With the Azure Batch CLI](https://help.dragen.illumina.com/dragen-v4.3/reference/dragen-multi-cloud/azure-batch/run-azure-modes#use-dragen-with-the-azure-batch-cli) Was this helpful? Copy RESOURCE_GROUP="dragen" az group create -n "$RESOURCE_GROUP" -l "EastUS" az deployment group create \ -g "$RESOURCE_GROUP" \ -p prefix=fpgaci \ -p azureBatchServiceOid=795cc567-16b1-4904-9344-afc876387199 \ -f mainTemplate.json \ --query "properties.outputs" Copy { "id": "", "commandLine": "", "resourcesFiles": [], "outputFiles": [] } Copy az batch job create --id pool-id Copy /bin/bash -c \ "mkdir ; \ tar xzvf dragen.tar -C ; \ /opt/edico/bin/dragen --partial-reconfig HMM --ignore-version-check true; \ /opt/edico/bin/dragen -f -r \ -1 \ -2 \ --RGID \ --RGSM \ --enable-bam-indexing true \ --enable-map-align-output true \ --enable-sort true \ --output-file-prefix dragen-batch \ --enable-map-align true \ --output-format BAM \ --output-directory \ --enable-variant-caller true \ --lic-server " Copy /bin/bash -c \ "mkdir dragen output; \ tar xzvf dragen.tar -C dragen; \ /opt/edico/bin/dragen --partial-reconfig HMM --ignore-version-check true; \ /opt/edico/bin/dragen -f -r dragen \ -1 1.fq.gz \ -2 2.fq.gz \ --RGID NA24385-AJ-Son-R1-NS_S33 \ --RGSM NA24385-AJ-Son-R1-NS_S33 \ --enable-bam-indexing true \ --enable-map-align-output true \ --enable-sort true \ --output-file-prefix dragen-batch \ --enable-map-align true \ --output-format BAM \ --output-directory output \ --enable-variant-caller true \ --lic-server " Copy "resourceFiles": [{\ "filePath": "dragen.tar",\ "httpUrl": ""\ }, {\ "filePath": ".gz",\ "httpUrl": ""\ }, {\ "filePath": ".gz",\ "httpUrl": ""\ }] Copy "outputFiles": [{\ "filePattern": "../stdout.txt",\ "destination": {\ "container": {\ "containerUrl": "",\ "path": "/stdout.txt"\ }\ },\ "uploadOptions": {\ "uploadCondition": "taskcompletion"\ }\ }, {\ "filePattern": "../stderr.txt",\ "destination": {\ "container": {\ "containerUrl": "",\ "path": "/stderr.txt"\ }\ },\ "uploadOptions": {\ "uploadCondition": "taskcompletion"\ }\ }, {\ "filePattern": "/**/*",\ "destination": {\ "container": {\ "containerUrl": "",\ "path": "/"\ }\ },\ "uploadOptions": {\ "uploadCondition": "taskcompletion"\ }\ }, {\ "filePattern": "/var/log/dragen.log",\ "destination": {\ "container": {\ "containerUrl": "",\ "path": "/log/dragen.log"\ }\ },\ "uploadOptions": {\ "uploadCondition": "taskcompletion"\ }\ }, {\ "filePattern": "/var/log/dragen/**/*",\ "destination": {\ "container": {\ "containerUrl": "",\ "path": "/log/dragen"\ }\ },\ "uploadOptions": {\ "uploadCondition": "taskcompletion"\ }\ }] Copy { "id": "task1", "commandLine": "$COMMAND", "resourceFiles": [{\ "filePath": "dragen.tar",\ "httpUrl": "https://dragentestdata.blob.core.windows.net/reference-genomes/Hsapiens/hash-tables/hg38_altaware_nohla-cnv-anchored.v8.tar"\ }, {\ "filePath": "1.fq.gz",\ "httpUrl": "https://dragentestdata.blob.core.windows.net/samples/wes/NA24385-AJ-Son-R1-NS_S33/NA24385-AJ-Son-R1-NS_S33_L001_R1_001.fastq.gz"\ }, {\ "filePath": "2.fq.gz",\ "httpUrl": "https://dragentestdata.blob.core.windows.net/samples/wes/NA24385-AJ-Son-R1-NS_S33/NA24385-AJ-Son-R1-NS_S33_L001_R2_001.fastq.gz"\ }], "outputFiles": [{\ "filePattern": "../stdout.txt",\ "destination": {\ "container": {\ "containerUrl": "$CONTAINER_URL",\ "path": "task1/stdout.txt"\ }\ },\ "uploadOptions": {\ "uploadCondition": "taskcompletion"\ }\ }, {\ "filePattern": "../stderr.txt",\ "destination": {\ "container": {\ "containerUrl": "$CONTAINER_URL",\ "path": "task1/stderr.txt"\ }\ },\ "uploadOptions": {\ "uploadCondition": "taskcompletion"\ }\ }, {\ "filePattern": "output/**/*",\ "destination": {\ "container": {\ "containerUrl": "$CONTAINER_URL",\ "path": "task1/output"\ }\ },\ "uploadOptions": {\ "uploadCondition": "taskcompletion"\ }\ }, {\ "filePattern": "/var/log/dragen.log",\ "destination": {\ "container": {\ "containerUrl": "$CONTAINER_URL",\ "path": "task1/log/dragen.log"\ }\ },\ "uploadOptions": {\ "uploadCondition": "taskcompletion"\ }\ }, {\ "filePattern": "/var/log/dragen/**/*",\ "destination": {\ "container": {\ "containerUrl": "",\ "path": "task1/log/dragen"\ }\ },\ "uploadOptions": {\ "uploadCondition": "taskcompletion"\ }\ }] } Copy /bin/bash -c \ "echo DefaultEndpointsProtocol=https >> ~/.azure-credentials; \ echo AccountName= >> ~/.azure-credentials; \ echo AccountKey= >> ~/.azure-credentials; \ echo EndpointSuffix=core.windows.net >> ~/.azure-credentials; \ mkdir dragen output; \ tar xzvf dragen.tar -C dragen; \ /opt/edico/bin/dragen --partial-reconfig HMM --ignore-version-check true; \ /opt/edico/bin/dragen -f -r dragen \ -1 \ -2 \ --RGID \ --RGSM \ --enable-bam-indexing true \ --enable-map-align-output true \ --enable-sort true \ --output-file-prefix dragen-batch \ --enable-map-align true \ --output-format BAM \ --output-directory output \ --enable-variant-caller true \ --lic-server " Copy LIST_URL= az storage blob generate-sas \ --name \ --account-name \ --account-key \ --container-name \ --expiry \ --permissions r \ --https \ --full-uri \ --output tsv Copy "resourceFiles": [{\ "filePath": "dragen.tar",\ "httpUrl": "$GENOME_URL"\ }, {\ "filePath": "fastq_list.csv",\ "httpUrl": "$LIST_URL"\ }] Copy /bin/bash -c \ "echo DefaultEndpointsProtocol=https >> ~/.azure-credentials; \ echo AccountName= >> ~/.azure-credentials; \ echo AccountKey= >> ~/.azure-credentials; \ echo EndpointSuffix=core.windows.net >> ~/.azure-credentials; \ mkdir dragen output; \ tar xvf dragen.tar -C dragen; \ /opt/edico/bin/dragen --partial-reconfig HMM --ignore-version-check true; \ /opt/edico/bin/dragen -f -r dragen \ --fastq-list fastq_list.csv \ --fastq-list-sample-id \ --enable-bam-indexing true \ --enable-map-align-output true \ --enable-sort true \ --output-file-prefix dragen-batch \ --enable-map-align true \ --output-format BAM \ --output-directory output \ --enable-variant-caller true \ --lic-server " Copy az batch task create \ --job-id \ --json-file task.json --- # Gene Expression Quantification | DRAGEN v4.3 | DRAGEN The DRAGEN RNA pipeline contains a gene expression quantification module that estimates the expression of each transcript and gene in an RNA-seq data set. The module first internally translates the genomic mapping of each read (read pair) to the corresponding transcript mappings. Then uses an Expectation-Maximization (EM) algorithm to infer the transcript expression values that best match all the observed reads. The EM algorithm can also model and correct for GC-bias in the reported quantification results. To enable the quantification module, set the `--enable-rna-quantification` option to `true` in your current RNA-seq command-line scripts. Additionally, you must provide a gene annotation file (GTF/GFF) that contains the genomic position of all transcripts to quantify. You can specify the GTF/GFF file using the `-a` or `--annotation-file` option. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/gene-expression-quantification#quantification-options) Quantification Options --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Option Description \--enable-rna-quantification If set to true, enables RNA quantification. Requires `--enable-rna` to be set to true. \--rna-library-type Specifies the type of RNA-seq library. The following are the available values: * `IU`—Paired-end unstranded library. * `ISR`—Paired-end stranded library in which read2 matches the transcript strand (eg, Illumina Stranded Total RNA Prep). * `ISF`—Paired-end stranded library in which read1 matches the transcript strand. * `U`—Single-end unstranded library. * `SR`—Single-end stranded library in which reads are in reverse orientation to the transcript strand (eg, Illumina Stranded Total RNA Prep). * `SF`—Single-end stranded library in which reads match the transcript strand. * `A`— DRAGEN examines the first reads pairs in the data set to automatically detect the correct library type. For polya tail trimming, the library type is assumed to be unstranded. Autodetect is the default value. \--rna-quantification-gc-bias GC bias correction estimates the effect of transcript %GC on sequencing coverage and accounts for the effect when estimating expression. To disable GC bias correction, set to false. \--rna-quantification-fld-max --rna-quantification-fld-mean --rna-quantification-fld-sd Use these options to specify the insert size distribution of the RNA-seq library for single-end runs. These options are relevant for GC bias correction. The defaults are 250 +- 25. The maximum allowed value is 1000. To improve accuracy, modify the values to match your library. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/gene-expression-quantification#quantification-outputs) Quantification Outputs --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Transcript quantification results are reported in the `.quant.sf` text file. The file lists results for each transcript. You can use the output file as input for differential gene expression using tools such as tximport and DESeq2. The following is an example of the file contents: Copy Name Length EffectiveLength TPM NumReads ENST00000364415.1 116 12.3238 5.2328 1 ENST00000564138.1 2775 2105.58 1.28293 41.8885 Field Description Name The ID of the transcript. Length The length of the (spliced) transcript in base pairs. EffectiveLength The length as accessible to RNA-seq, accounting for insert-size and edge effects. TPM Transcripts per Million (TPM) represents the expression of the transcript when normalized for transcript length and sequencing depth. NumReads The estimated number of reads from the transcript. The values are not normalized. The gene expression quantification module also outputs the files below. For information on the metrics included, see the section `Quantification and RNA QC Metrics`. * `.quant.genes.sf`—Contains quantification results at the gene level. The results are produced by summing together all transcripts with the same geneID in the annotation file (GTF). Length and EffectiveLength are the expression-weighted means of the individual transcripts in the gene. * `.quant.metrics.csv`—Summary statistics relevant to RNA transcripts and quantification. See `Quantification and RNA QC Metrics`. * `.quant.transcript_fragment_lengths.txt` —Full fragment length distribution of reads mapped to transcripts, output in length- probability pairs of length minimum through >999 bases. Summing the products of the two columns will yield the average fragment length. * `.quant.transcript_coverage.txt`—Measures coverage uniformity with a normalized average of 5' to 3' coverage pattern along transcripts in increments of 1%. A summation of the 100 coverage bins should yield 100%. * `.SJ.saturation.txt`—Measures sequencing saturation of the library, including the number of unique splice junctions observed as a function of reads processed. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/gene-expression-quantification#quantification-and-rna-qc-metrics) Quantification and RNA QC metrics The RNA Quantification module outputs metrics related to the gene expression results and more general RNA QC metrics that rely on the transcript-level analysis. A summary of the metrics is output to the `.quant_metrics.csv` file. Metric Description `Library orientation` Library orientation of the RNA-seq reads relative to the original transcripts. The library orientation can be automatically detected, or can be explicitly provided. See `Quantification Options` for more information. `Total Genes` Total number of genes from the gene annotation (GTF/GFF) input used for analysis. `Coding Genes` Number of coding genes from the gene annotation (GTF/GFF) excluding pseudo-genes and biotypes which are non-coding. `Total Transcripts` Number of transcripts from the gene annotation (GTF/GFF) input used for analysis. `Median transcript CV coverage` Median Coefficient of Variation (CV), which is standard deviation divided by mean coverage, of the 1000 most highly expressed transcripts. This metric measures uniformity of RNA-seq read coverage. `Median 5' coverage bias` Median 5 prime bias of the 1000 most highly expressed transcripts, calculated per transcript as mean coverage of the 5'-most 100 bases divided by the mean coverage of the whole transcript. `Median 3' coverage bias` Median 3 prime bias of the 1000 most highly expressed transcripts, calculated per transcript as mean coverage of the 3'-most 100 bases divided by the mean coverage of the whole transcript. `Forward transcript fragments` The number of read pairs that match transcripts on the forward strand. Only reads that align fully within exons are counted. `Reverse transcript fragments` The number of read pairs that match transcripts on the reverse strand. Only reads that align fully within exons are counted. `Strand mismatched fragments` In the case of _stranded_ library orientation, number of read pairs that do not match the expected strand of the transcript. Only reads that align fully within exons are counted. `Ambiguous strand fragments` Read pairs that match transcripts in both forward and reverse orientation. Only reads that align fully within exons are counted. `Intron fragments` Read pairs that overlap with a gene, but do not overlap with any exons. `Intergenic fragments` Read pairs that do not overlap with any gene. `Unknown transcript fragments` Read pairs that partially align with an exon but overlap non-exonic regions (usually due to alternative splicing). `Number of genes with coverage > 1x,10x,30x,100x` The count of the number of genes where the most highly expressed transcript has average coverage greater than 1x, 10x, 20x, and 100x . `Fold coverage of all exons` The average sequencing coverage across all annotated exons, determined using the most highly expressed transcript for each gene. `Fold coverage of coding exons` The average sequencing coverage across only exons within coding genes, determined using the most highly expressed transcript for each gene. `Fold coverage of introns` The average sequencing coverage across detected introns. `Fold coverage of intergenic regions` The average sequencing coverage across areas detected outside annotated genes. Only unfiltered and properly paired reads (for paired-end sequencing) are counted in the above metrics. The seven fragments types that are listed (_Forward transcript_, _Reverse transcript_, _Strand mismatched_, _Ambiguous strand_, _Intron_, _Intergenic_, _Unknown transcript_) add up to 100% of the counted fragments, and the percentage of this total is provided next to each fragment metric count. [PreviousGene Fusion Detectionchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/gene-fusion-detection) [NextRNA Variant Callingchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-variant-calling) Last updated 7 months ago Was this helpful? * [Quantification Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/gene-expression-quantification#quantification-options) * [Quantification Outputs](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/gene-expression-quantification#quantification-outputs) * [Quantification and RNA QC metrics](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/gene-expression-quantification#quantification-and-rna-qc-metrics) Was this helpful? --- # Gene Fusion Detection | DRAGEN v4.3 | DRAGEN The DRAGEN Gene Fusion module uses the DRAGEN RNA splice-aware aligner to detect gene fusion events. The supplementary (chimeric) alignments are used to find potential breakpoints and read evidence is accumulated for the resulting fusion event candidates. Then, an ML model is applied to score the putative fusion events to filter potential false positives. The ML scoring model is currently available on human samples and does not support non-human reference genomes. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/gene-fusion-detection#running-dragen-gene-fusion) Running DRAGEN Gene Fusion -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- You can run the DRAGEN Gene Fusion module together with a regular RNA-Seq map/align job. To enable the DRAGEN Gene Fusion module, set `--enable-rna-gene-fusion` to true in your current RNA-Seq command-line scripts. The DRAGEN Gene Fusion module requires a gene annotations file in GTF or GFF format. The following is an example command line for running an end-to-end RNA-Seq experiment. Copy dragen \ -r \ -1 \ -2 \ -a \ --output-dir \ --output-file-prefix \ --RGID \ --RGSM \ --enable-rna true \ --enable-rna-gene-fusion true At the end of a run, a summary of detected gene fusion events is output, which is similar to the following example. Copy ================================================================== Completed DRAGEN Gene Fusion Detection ================================================================== Chimeric alignments: 3072 Total fusion candidates: 259 Final fusion candidates: 223 [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/gene-fusion-detection#gene-fusion-output-and-filters) Gene Fusion Output and Filters ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The `.fusion_candidates.features.csv` file lists the detected gene fusion events. The output CSV file includes the following columns. * `**#FusionGene**`: Parent gene names (in 5' to 3' order of transcript) participating in the fusion; hereafter refer red to as Gene 1 and Gene 2. If a fusion breakpoint overlaps multiple genes, the genes are listed by default as separate candidates (rows). To show them as a semi-colon separated gene list on the same row, the option `--rna-gf-merge-calls` can be set to `true` as described in the Gene Fusion Options and Filters section. * `**Score**`: Fusion call confidence score predicted by the ML model. If the ML model is used, the score can be 0 (low confidence) to 1 (high-confidence call). Currently the ML model only supports human references. In the case an ML model is not available, the number of supporting reads will be reported as the score. * `**LeftBreakpoint**`: Gene 1 breakpoint formatted as `::`. * `**RightBreakpoint**`: Gene 2 breakpoint formatted as `::`. * `**Filter**`: Semicolon separated list of filter flags. The `LOW_SCORE` filter is used to filter low confidence fusion candidates. If `--rna-gf-enable-post-filtering=true`, other confidence filters will also be applied. Informative filters, on the other hand, do not fail the fusion. In the absence of the ML model scoring (i.e. a non-human reference is used), a more aggressive post-filtering will take place and all confidence and informative filters will be applied. The following are the available filters. Filter Type Description Option to set threshold `LOW_SCORE` Confidence (always applied) The fusion candidate has low probabilistic score (< 0.5) as determined by the features of the candidate. `--rna-gf-min-score` `MIN_SUPPORT` Confidence (optional) The fusion candidate has at least one fusion supporting read pairs. `--rna-gf-min-split-support` `LOW_UNIQUE_ALIGNMENTS` Confidence (optional) All fusion supporting read alignments near at least one of the breakpoints have the same start and end position. `--rna-gf-min-unique-alignments` `LOW_MAPQ` Confidence (optional) All fusion supporting read alignments at either breakpoint have MAPQ < 20. `--rna-gf-min-breakpoint-mapq` `DOUBLE_BROKEN_EXON` Confidence (optional) If both breakpoints are 50 bp from annotated exon boundaries, then the number of supporting reads do not satisfy a high threshold requirement (≥10 supporting reads). `--rna-gf-exon-snap` `--rna-gf-min-support-be` `UNENRICHED_GENES` Confidence (optional) If enrichment list provided, then neither parent genes is enriched. If amplicon mode is enabled, then at least one parents gene is not enriched (See DRAGEN amplicon pipeline for further information). `--rna-gf-enriched-only` `MITOCHONDRIAL_GENES` Confidence (optional) The fusion candidate involves mitochondrial genes. Set `--rna-gf-filter-chrm=false` to disable this filter. `--rna-gf-filter-chrm` `READ_THROUGH` Confidence (optional) The breakpoints are cis neighbors (< 200,000 bp) on the reference genome. `--rna-gf-min-cis-distance` `ANCHOR_SUPPORT` Information only Read alignments of fusion supporting reads are not long enough (less than 12 bp) at either breakpoint. `--rna-gf-min-anchor` `HOMOLOGOUS` Information only The candidate is likely to be a false candidate generated because the two genes involved have high gene homology. `--rna-gf-min-blast-pairs-eval` `LOW_ALT_TO_REF` Information only The number of reads supporting the fusion is < 1% of the number of reads supporting the reference transcript at either breakpoint. `--rna-gf-min-alt-to-ref` `LOW_GENE_COVERAGE` Information only Either breakpoint has less than 125 bp with nonzero read coverage. `--rna-gf-min-covered-bases` **Note that the specific features and column values are subject to change in future DRAGEN versions as more RNA data is analyzed.** * `**#SplitScore**`: Combined count of fusion supporting read pairs reported as split reads and soft-clipped reads * `**#NumSplitReads**`: Number of fusion supporting read pairs with at least one split read alignment. * `**#NumSoftClippedReads**`: Number of fusion supporting read pairs with no split read alignment, but at least one soft clipped alignment. Included in `SplitScore` and includes soft-clipped reads for both Gene1 and Gene2 * `**#NumSoftClippedReadsGene1**`: Number of fusion supporting read pairs with no split read alignment, but at least one soft clipped alignment to Gene 1 * `**#NumSoftClippedReadsGene2**`: See above (`NumSoftClippedReadsGene1`) for Gene 2 * `**#NumPairedReads**`: Number of fusion supporting read pairs such that one of the reads maps to Gene1 and the other maps to Gene2, without any breakpoint overlap * `**#NumRefSplitReadsGene1**`: Number of read pairs that map fully within Gene 1 such that at least one of the reads aligns across the breakpoint. These reads support the reference transcript and do not support the fusion. * `**#NumRefSplitReadsGene2**`: See above (`NumRefSplitReadsGene1`) for Gene 2 * `**#NumRefPairedReadsGene1**`: Number of read pairs such that one of the reads maps on the left side of the Gene1 breakpoint and the other maps on the right side of the Gene1 breakpoint, without overlapping the break. These reads support the reference transcript and do not support the fusion. * `**#NumRefPairedReadsGene2**`: See above (`NumRefPairedReadsGene1`) for Gene 2 * `**#AltToRef**`\-- Ratio of (fusion split + soft clipped reads) / max(NumRefSplitReadsGene1, NumRefSplitReadsGene2); used for the `LOW_ALT_TO_REF` filter * `**#UniqueAlignmentsGene1**`: Unique (start-end) positions of fusion supporting read alignments to Gene 1 (after dedup); used for the `LOW_UNIQUE_ALIGNMENTS` filter * `**#UniqueAlignmentsGene2**`: Unique (start-end) positions of fusion supporting read alignments to Gene 2 (after dedup); used for the `LOW_UNIQUE_ALIGNMENTS` filter * `**#MaxMapqGene1**`: Maximum MAPQ for fusion supporting reads in Gene 1 * `**#AvgMapqGene1**`: Average MAPQ for fusion supporting reads in Gene 1 * `**#MaxMapqGene2**`: Maximum MAPQ for fusion supporting reads in Gene 2 * `**#AvgMapqGene2**`: Average MAPQ for fusion supporting reads in Gene 2 * `**#CoverageBasesGene1**`: Bases in Gene 1 with read coverage within a certain distance (default 1000 bp) of the breakpoint in the direction of the breakpoint strand which is part of the fusion transcript * `**#CoverageBasesGene2**`: See above (`CoverageBasesGene1`) for Gene 2 * `**#DeltaExonBoundaryGene1**`: Distance from the Gene 1 breakpoint for the closest fusion supporting alignment (higher distance to boundary lowers score) * `**#DeltaExonBoundaryGene2**`: See above (`DeltaExonBoundaryGene1`) for Gene 2 * `**#IsRestrictedGene1**`: Indicator variable of whether the Gene 1 is tagged as protein coding in the annotation file * `**#IsRestrictedGene2**`: Indicator variable of whether the Gene 2 is tagged as protein coding in the annotation file * `**#IsEnrichedGene1**`: If enrichment or amplicon assay, then indicates whether Gene 1 is enriched. If whole transcriptome sequencing, then set to 1 * `**#IsEnrichedGene2**`: See above (`IsEnrichedGene1`) for Gene 2 * `**#CisDistance**`: Distance between breakpoints if they are adjacent to each other and on the same strand. Large value (100M) if not a CIS break; used for the `READ_THROUGH` filter. * `**#BreakpointDistance**`: Distance between breakpoints if they are adjacent. Large value (100M) if not within same chromosome * `**#GenePairHomologyEval**`: E-value of pairwise BLAST alignment of the parent genes * `**#AnchorLength1**`: Longest alignment of a fusion supporting read to Gene 1 * `**#AnchorLength2**`: Longest alignment of a fusion supporting read to Gene 2 * `**#FusionLengthGene1**`: Distance from breakpoint to the end of Gene 1 * `**#FusionLengthGene2**`: Distance from breakpoint to the end of Gene 2 * `**#NonFusionLengthGene1**`: Breakpoint distance to the end of transcript not part of the fusion for Gene 1 * `**#NonFusionLengthGene2**`: Breakpoint distance to the end of transcript not part of the fusion for Gene 2 * `**#Gene1Id**`: Gene ID reported in the annotation file for Gene 1 * `**#Gene2Id**`: Gene ID reported in the annotation file for Gene 2 * `**#Gene1Location**`: * _IntactExon_: Breakpoint matches exon boundary, * _BrokenExon_: Breakpoint is within an exon but does not match the exon boundary, * _Intron_: Breakpoint is within an intron, * _Intergenic_: Breakpoint does not overlap any gene * `**#Gene2Location**`: See above (`Gene1Location`) for Gene 2 * `**#Gene1Sense**`: `True` if the Gene 1 5' to 3' direction matches the breakpoint order, indicating that the gene is the upstream gene in the fusion transcript * `**#Gene2Sense**`: See above (`Gene1Sense`) for Gene 2 In addition, if `--rna-gf-merge-calls` is enabled, DRAGEN merges the fusion candidates that overlap the same breakpoint into a single row reporting the feature values for the highest scoring passing candidate (or highest scoring failing candidate if no passing candidate is reported). For each breakpoint, in the column `#FusionGene`, it reports a semi-colon separated list of names of all overlapping genes with a passing candidate. The following two columns are added to the `features.csv` output file: * `**#AdditionalGenes1**`: If a mix of passing and failing candidates are reported for the same breakpoint of Gene 1, genes with only failing candidates are listed. If no passing candidate exists, then all overlapping genes are reported in the `#FusionGene` column. * `**#AdditionalGenes2**`: See above (`AdditionalGenes1`) for Gene 2 The `.fusion_candidates.final` output file lists each passing fusion along with the read names that support the fusion, including Split Reads, Soft-clipped reads, and Paired (discordant) Reads and the passing scores. These reads can be extracted from the output BAM file and then used to visualize the fusions (i.e. in IGV). The same information for the non-passing fusions is provided in the `.filter_info` output file. The `.fusion_candidates.vcf.gz` output file provides the VCF representation for all of the breakpoints for the candidate fusions using structural variant-style BND notation. The VCF header is annotated with `##source=DRAGEN_RNA_GF` to indicate the file is generated by the DRAGEN RNA Gene Fusion pipeline. All fusion candidates (passing and failing) are represented in the VCF output with one entry for each side of the fusion breakpoint (Gene 1 and Gene 2). The `.fusion_metrics.csv` output file provides a simple count of the total number of fusion candidates, those passing the scoring filter, and the number of unique left-right gene combinations that are found. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/gene-fusion-detection#gene-fusion-options-and-filters) Gene Fusion Options and Filters ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ The following thresholds and options may be used to configure the fusion caller: * `--rna-gf-blast-pairs` A tab separated file listing gene pairs that have a high level of similarity. The first and second column are the gene names, and the third column is the e-score. This list of gene pairs is used as a homology filter to reduce false positives. For runs on human genome assemblies GRCH38 and hg19, DRAGEN automatically applies a default file generated using [Gencode Human Release 32arrow-up-right](https://www.gencodegenes.org/human/release_32.html) annotations for primary chromosomes if no other file is specified using the command-line. * `--rna-gf-enriched-genes` For RNA enrichment assays, a list of targeted genes specified as one gene-name per line. Only fusion calls involving at least one gene on the list are reported. The enriched genes list should only contain genes listed in the input annotation file. This option cannot be provided together with `--rna-gf-enriched-regions`. If RNA amplicon mode is enabled and the amplicon bed file already includes the gene name, then you do not need to set this option; DRAGEN will read the enriched genes names from the amplicon BED file (fifth column). See DRAGEN Amplicon Pipeline for further information. * `--rna-gf-enriched-regions` Alternative to `--rna-gf-enriched-genes`, but input is provided as a bed-file with regions coordinates instead of a gene list. All the genes in the provided annotation file that overlap such regions are included. Genes that are extracted in this way are summarized in output in the `*.fusion.enriched_genes.txt` file. This option cannot be provided together with `--rna-gf-enriched-genes`. * `--rna-repeat-genes` Text file that contains the names or IDs (from the annotation file) of targeted repetitive genes for sensitive fusion detection. Exclusive from `--rna-repeat-intervals`. This option overrides the default BED file. The repeat genes list should only contain genes listed in the input annotation file. * `--rna-repeat-intervals` BED file that contains a target list of repeat intervals for sensitive fusion detection. Exclusive from `--rna-repeat-genes`. This option overrides the default files, which contain the genes CIC, DUX4, PSPH, and SEPTIN14 for GRCh38 and hg19 reference genomes. * `--enable-variant-annotation=true`, `--variant-annotation-assembly`, and `--variant-annotation-data` Enable Illumina Annotation Engine (IAE) to report fusion annotations in JSON format. `--enable-variant-annotation` must be set to true. For more information, see Illumina Annotation Engine. * `--rna-gf-restrict-genes` When parsing the gene annotations file for use in the DRAGEN Gene Fusion module, you can use this option to restrict the entries of interest to only protein-coding regions. Restricting the annotation to only the protein-coding genes reduces false positive rates in currently studied fusion events. To report non-coding gene fusions such as pseudo genes and lincRNAs, turn off this option. The default value is `true`. * `--rna-gf-merge-calls` If multiple genes overlap a fusion breakpoint, DRAGEN generates and scores a separate fusion candidate for each gene pair overlapping the breakpoint. The default value is `false` so that each reported fusion event only has one left and right gene in the fusion, and overlapping genes are output as separate events. * `--rna-gf-allow-overlapping-genes` Allows for fusion calls between overlapping genes. The default value is "false". * `--rna-gf-enable-post-filters` Enable post-filtering of RNA gene fusion candidates by confidence flags. The filter flags are listed in the table above. The default value is "false". * `--enable-rna-amplicon` A separate fusion filtering model is trained for RNA amplicon mode. Duplicate removal for fusion supporting reads is disabled for RNA amplicon mode and both genes are required to be in the list of enriched genes. By default, the DRAGEN fusion caller filters candidates if a breakpoint overlaps both transcripts (e.g. fusions such as FIP1L1--PDGFRA and GOPC--ROS1). In RNA amplicon mode, such candidates are not filtered. See DRAGEN Amplicon Pipeline for further information. The default is "false". * `--rna-gf-sv-vcf` Structural Variant VCF file output from DRAGEN DNA structural variant caller run in somatic mode. See the next section for more information. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/gene-fusion-detection#running-rna-fusion-detection-with-a-somatic-sv-evidence) Running RNA fusion detection with a somatic SV evidence ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ You can run the DRAGEN Gene Fusion module with a VCF file containing somatic Structural Variant (SV) calls. DRAGEN will report SV events matching each fusion candidate in the `*.features.csv` output file for informational purposes, but will _not_ use this data in the scoring or filtering of the fusion candidates. The SV events must be run in somatic mode (for more information see DRAGEN Structural Variant Calling pipeline). The following is an example command line for running an end-to-end RNA-Seq experiment with a somatic SV VCF file. When the SV VCF input is provided to the RNA fusion caller, the following additional features will be reported in the `features.csv` output file: * `**#SvEvent**`: A semi-colon separated string representation of SV events matching the fusion candidate. * `**#SvType**`: A semi-colon separated list of type of the matching SV events. * `**#SomaticScore**`: The highest SomaticScore value of the matching SV events. * `**#SvDistance**`: The maximum distance between any SV breakpoint to any fusion breakpoints (if multiple matching SV events, then minimum of all maximum distances over all SV events). * `**#LeftSvDistance**`: The distance between the left fusion breakpoint and the corresponding SV breakpoint (if multiple matching SV events, then minimum over all SV events). * `**#RightSvDistance**`: The distance between the right fusion breakpoint and the corresponding SV breakpoint (if multiple matching SV events, then minimum over all SV events). * `**#SvPresent**`: Set to 1 if matching SV event is present, otherwise 0. * `**#SvAbsent**`: Set to 1 if no matching SV event is present, otherwise 0. [PreviousRNA Alignmentchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment) [NextGene Expression Quantificationchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/gene-expression-quantification) Last updated 7 months ago Was this helpful? * [Running DRAGEN Gene Fusion](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/gene-fusion-detection#running-dragen-gene-fusion) * [Gene Fusion Output and Filters](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/gene-fusion-detection#gene-fusion-output-and-filters) * [Gene Fusion Options and Filters](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/gene-fusion-detection#gene-fusion-options-and-filters) * [Running RNA fusion detection with a somatic SV evidence](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/gene-fusion-detection#running-rna-fusion-detection-with-a-somatic-sv-evidence) Was this helpful? Copy dragen \ -r \ -1 \ -2 \ -a \ --output-dir \ --output-file-prefix \ --RGID \ --RGSM \ --enable-rna true \ --enable-rna-gene-fusion true --rna-gf-sv-vcf --- # scATAC | DRAGEN v4.3 | DRAGEN The DRAGEN Single-Cell ATAC (scATAC) Pipeline can process single-cell ATAC-Seq data sets from reads to a cell-by-peak read count matrix. The pipeline includes the following functions: * ATAC-Seq alignment. * Cell-barcode error correction for the barcode read. * Chromatin accessibility peak calling. * Fragment counting per cell and peak to measure chromatin accessibility. * Sparse matrix output and QC metrics. ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3660538983-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FpFn0gLe5V859SXejQNAW%252Fuploads%252Fgit-blob-8e8ca21bf156c37214915579feb2aa1ae2af5094%252FscATACworkflow.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=fd08a590&sv=2) The functionality and options related to alignment and gene annotation are identical to DNA pipelines. For information, see [DRAGEN DNA Pipeline](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline) . [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#input-files) Input Files -------------------------------------------------------------------------------------------------------------------------------------------------------- ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#alignment-reference) Alignment Reference Use a standard DRAGEN DNA reference genome or hashtable for the scATAC Pipeline. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#read-input) Read Input The DRAGEN scATAC Pipeline requires both the genomic sequence and the barcode sequence for each fragment (read) as input. The genomic sequence is aligned to the reference genome to determine the expressed gene, the single-cell barcode sequence is used to identify the unique cell. When starting from FASTQ, you can either include the UMI in the read name or provide separate cell-barcode FASTQ files. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#umi-in-read-name) UMI in Read Name Provide the genomic reads as a paired-end FASTQ files with the Barcode sequence in the eighth field of the read-name line. Separate sequences using a colon. The following example uses read2 (`sample.R2.fastq.gz`) as the genomic read. In the example, the `GAAACTCGTTCAGCGC` sequence is the barcode read and the `ACAG...` sequence is the genomic read. These FASTQ files can be generated by bclConvert and bcl2fastq using the UMI settings to define the single-cell barcode read. If using bclConvert, enter the barcode information using the `OverrideCycles1` setting. For more information, see the _BCL Convert Software Guide (document # 1000000094725)_. Note: bclConvert refers to the entire single-cell barcode sequence as UMI. Enter the following command line option to use the generated FASTQ files from bclConvert: `dragen -1 -2 --umi-source=qname` The option is also compatible with the `--fastq-list` input options and with read input from BAM files. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#single-cell-atac-fastq-list-files) Single-cell ATAC Fastq-list Files A single-cell ATAC fastq-list file is a CSV file with the following mandatory columns: Column Description `Lane` Sequencing lane `RGID` Read group ID `RGSM` Read group sample `RGLB` Read group library `Read1File` Read 1 FASTQ file `Read2File` Read 2 FASTQ file `UmiFile` Read 3 FASTQ file (FASTQ file with cell-barcodes) An example is shown below: #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#separate-umi-fastq-files) Separate `UMI Fastq` Files An alternative option is to provide the genomic and barcode sequences as three separate FASTQ files. Two files contain only the genomic reads and one contains the corresponding barcode-reads in the same order. This file is similar to how read-pairs are normally handled. If using separate UMI files, the sequencing system run setup and bclConvert are not aware of the UMI and treat it as normal read sequence by default. Enter the following command line option to use the separate UMI FASTQ files: `dragen -1 -2 --umi-fastq= --umi-source=fastq` To use this method with multiple FASTQ files, follow these steps: 1. Enter the barcode FASTQ files as read1 in the `fastq-list` file, and then enter the genomic read FASTQ files matching the default fastq\_list.csv generated by bclConvert as read2 and umifile. 2. Enter the following command: `dragen --fastq-list fastq_list.csv --umi-source=umifile` #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#using-multiple-libraries) Using Multiple Libraries The scATAC pipeline can process a single biological sample per DRAGEN run. To process multiple single-cell libraries together, split the single sample into multiple single-cell libraries with a unique set of cells in each DRAGEN keeps the cells (barcodes and UMIs) from each library separate and provides merged outputs across all. Read groups are used to specify the library for each FASTQ file using the RGLB attribute. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#dragen-single-cell-settings) DRAGEN Single-cell Settings ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- To use the scATAC workflow, enter `--enable-single-cell-atac=true`. This section includes information on additional scATAC settings. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#barcode-position) Barcode Position By default the scATAC workflow assumes that the overall barcode sequence is made up of a single-cell barcode (possibly split into multiple blocks). Enter the following command to identify the location of the single-cell barcode: `--scatac-barcode-position [++...][(:-)|(:+)]` `blockPos` describes the offset of the first and last inclusive base of the block and is formatted as `_`. For example, for a library with a 16 bp cell-barcode, enter: `--scatac-barcode-position 0_15`. For a library with the cell-barcode split into three blocks of 9 bp separated by fixed linker sequences and an 8 bp UMI, enter: `--scatac-barcode-position=0_8+21_29+43_51`. By default, the barcode position is assumed to be indicated on the forward strand. To explicitly specify the forward strand, enter: `--scatac-barcode-position 0_15:+` or `--scatac-barcode-position=0_8+21_29+43_51:+`. Conversely, to specify the reverse strand, enter: `--scatac-barcode-position 0_15:-` or `--scatac-barcode-position=0_8+21_29+43_51:-`. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#known-barcode-lists) Known Barcode Lists You can provide a list of cell barcode sequences to include using the following command: `--scatac-barcode-sequence-list ` In the case where the `--scatac-barcode-position` parameter is not split into multiple blocks (see Barcode Position section) the file must contain one possible cell barcode sequence per line. Differently, when the barcode position is split into multiple blocks, the file must contain a list composed by multiple sections (one for each block): each section must indicate the possible cell barcode block sequences for the corresponding block. Each section should start with a line with prefix `#-`, e.g.: The input file might be compressed with gzip (`*.txt.gz`). During cell-barcode error correction any observed barcodes that do not match a sequence specified in the file are considered errors. If possible, the barcodes are corrected to a similar allowed sequence. See [Barcode Error Correction](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#barcode-error-correction) for more information. If the barcodes cannot be corrected, they are filtered out. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#cell-filtering) Cell Filtering DRAGEN uses a threshold on the total count of unique reads per cell barcode, to determine which barcodes are likely to correspond to single-cells in the original sample, instead of background noise. The threshold is determined based on the distribution of counts along barcodes and on the expected number of true cells in the sample. * `--single-cell-number-cells` --- \[Optional\] Set the expected number of cells. The default is 3000. Adjust only if the expected number of cells is so far from the default that DRAGEN does not call the correct cell filtering threshold automatically. * `--single-cell-threshold` --- Specify the method for determining the count threshold value. The available values are `fixed`, `ratio`, or `inflection`. * If using `ratio`, DRAGEN estimates the expected number of cells as `max(T_e, T_m)`. `T_m` is a threshold based on a fraction of the counts seen in most abundant cell-barcodes. `T_e` is a threshold based on a fraction of the least abundant expected cell. * If using `inflection`, DRAGEN estimates the count threshold by analyzing inflection points in the cumulative distribution of counts. * If using `fixed`, the count threshold is set to force the expected number of cells (`--single-cell-number-cells` option), rather than estimating it from the data. The exact number of passing cells might be slightly larger than the number of requested single-cells because several cells in the tail of the count distribution can have the same count. For example, to set a fixed number of cells rather than use the automatically determined threshold, use the following command: `--single-cell-threshold=fixed --single-cell-number-cells=X` The command forces DRAGEN to select the top X cells and extra cells with the same number of counts of the last selected cell. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#additional-options) Additional Options The following are additional options you can use to configure the Single-Cell ATAC Pipeline settings. * `--qc-enable-depth-metrics` --- Set to `false` to disable depth metrics for faster run time. The default is `true`. * `--scatac-write-fragments` --- Set to `true` to write counted fragments to the disk (in both `tsv` (`.scATAC.fragments.tsv`) and `BigWig` (`.scATAC.fragments.bigwig`) format). The default is `false`. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#command-line-example) Command-line Example -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The following is an example command line to run the DRAGEN Single Cell ATAC Pipeline. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#outputs) Outputs ------------------------------------------------------------------------------------------------------------------------------------------------ Single-cell ATAC outputs are found in the standard DRAGEN output location using the prefix ``. in case of a single library and the prefix `.`. in case of multiple libraries. All single-cell ATAC output files contain word `scATAC` in their names. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#counts) Counts The following three files provide information about per-cell chromatin accessibility level in matrix market (`*.mtx`) format: * `.scATAC.matrix.mtx.gz` * Count of unique fragments for each cell/peak pair in sparse matrix format. * `.scATAC.barcodes.tsv.gz` * Cell-barcode sequence for each cell from the matrix. This includes all cell-barcodes. * `.scATAC.peaks.tsv.gz` * Peak name and ID for each peak in the matrix. The subset of barcodes corresponding to passing cells can be found under the Filter column in `.scATAC.barcodeSummary.tsv` indicated by values `PASS` and `FAIL`. The output includes filtered matrix files which only include the per-cell chromatin accessibility level for the `PASS` cells in matrix market (`*.mtx`) format. The `scATAC.peaks.tsv.gz` file is common for the unfiltered and filtered matrices: * `.scATAC.filtered.matrix.mtx.gz` * Count of unique UMIs for each **filtered** cell/peak pair in sparse matrix format. * `.scATAC.filtered.barcodes.tsv.gz` * Cell-barcode sequence for each **filtered** cell from the matrix. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#loading-output-in-a-dense-matrix) Loading output in a dense matrix Some users might want to explore the output matrix in a human-readable format. To do so, a possible way would be to load the matrix in a "dense" dataframe in python (similar methodologies can be used in alternative programming languages). It is important to remember, however, that when possible a "sparse" representation of the matrix is preferable, due to the significant usage of memory and disk space of "dense" matrices. Several tools are available to work efficiently with "sparse" representations of single cell matrices (e.g., scanpy in python). The matrix can be converted into a "dense" representation through two python modules: `scanpy` and `pandas`. This has been tested with python 3.10.0, scanpy 1.9.3, pandas 1.5.3. First, it is necessary to install the required libraries: Within python, the matrix can be loaded in "dense" representation using the following commands: The matrix can be saved through different output formats (e.g., CSV), although this is not recommended due to high disk usage. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#alignments) Alignments Alignments of the genomic reads are sorted by coordinate and output as a BAM file. Each alignment is annotated with an `XB` tag containing the cell-barcode. The alignments use the original sequences without any errors corrected. Fragments that did not have an associated barcode read, for example fragments trimmed on the input data, do not have `XB` tag. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#overall-metrics) Overall Metrics The `.scATAC_metrics.csv` file contains per sample scATAC metrics. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#barcode-read-metrics) Barcode Read Metrics * Invalid barcode read: Overall barcode sequence failed basic checks. For example, the barcode read was missing or too short. * Error free cell-barcode: Reads with cell-barcode sequences that were not altered during error correction. For example, if the read was an exact match to the allow list. * Error corrected cell-barcode: Reads with cell-barcode sequences successfully corrected to a valid sequence. * Filtered cell-barcode: Reads with cell-barcode sequences that could not be corrected to a valid sequence. For example, the sequence does not match allow list with at most one mismatch. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#genomic-fragment-metrics) Genomic Fragment Metrics * Fragments passing filters: Non-chimeric non-mitochondrial fragments that align to primary contigs with a high mapping quality (greater than 30 by default). * Non-primary contig fragments: Fragments that align to non-primary contigs (any contigs that are not autosome, X and Y). * Chimeric fragments: Fragments with the two reads aligning to different contigs. * Mitochondrial fragments: Fragments aligning to the mitochondrial contigs. * Low mapping quality fragments: Fragments with the two reads aligning with a mapping quality set to some specific value (default is 30). * Improperly mapped fragments: The two reads in the fragment are not mapped in proper pair (SAM flag "read mapped in proper pair" is set to 0). #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#cell-metrics) Cell Metrics * Fragment threshold for passing cells: Number of fragments required for a cell-barcode to pass filtering. * Passing cells: Number of cell-barcodes that passed the filters. * Fraction peak fragments in passing cells: Percentage of counted fragments intersecting peaks assigned to cells that passed the filters. * Fraction fragments in passing cells: Percentage of all counted fragments assigned to cells that passed the filters. * Median fragments per cells: Total counted fragments per cell that passed the filters. * Median peaks per cells: Peaks with at least one fragment per cell that passed the filters. * Total peaks detected: Peaks with at least one fragment in at least one cell that passed the filters. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#per-cell-metrics) Per-cell metrics The `.scATAC.barcodeSummary.tsv` contains summary statistics for each unique cell-barcode per cell after error correction. * ID: Unique numeric ID for the cell-barcode. * Barcode: The cell-barcode sequence. * TotalFragments: Total fragments with the cell-barcode sequence. * UniqueFragments: Unique fragments counted towards a peak. * NonPrimaryContigFragments: Unique non-primary contig framgnets. * ChimericFragments: Unique chimeric fragments. * LowMapqFragments: Unique low mapping quality fragments. * MitochondrialFragments: Unique fragments mapped to mitochondrial genome. * Peaks: Unique peaks detected. * Filter: The following are the available filter values: * `PASS`: Cell-barcode passes the filter. * `LOW`: UMI count is below threshold. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#barcode-error-correction) Barcode error correction ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Cell-barcode sequences from the input reads are error corrected based on the frequency with which each one is seen and an optional allow list of expected cell-barcode sequences. A cell-barcode sequence is considered a neighbor of another cell-barcode if there is at most one mismatch. A cell-barcode sequence is corrected to its neighbor in the following circumstances. When corrected, all reads with the cell-barcode are assigned instead to the neighboring cell-barcode. The sequence error correction scheme is similar to the directional algorithm described in (Smith, Heger and Sudbery, 2020). * The neighboring cell-barcode is at least two times more frequent across all input reads. * The neighboring cell-barcode is on the cell-barcode allow list, but the original cell-barcode is not. To avoid overcounting cell-barcodes based on sequence errors, cell-barcode error correction is performed among all reads with the same cell-barcode mapping to the same peak region. Cell-barcode sequences that are likely errors of another cell-barcodes are not counted. Ref: Smith, T., Heger, A. and Sudbery, I., 2020. UMI-Tools: Modeling Sequencing Errors In Unique Molecular Identifiers To Improve Quantification Accuracy. \[PDF\] Cold Spring Harbor Laboratory Press. Available at: \[Accessed 1 March 2022\]. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#peak-calling) Peak Calling ---------------------------------------------------------------------------------------------------------------------------------------------------------- DRAGEN calls peaks using an algorithm based on MACS, version 3 (Zhang et al., 2008). To customize the behavior of the peak calling algorithm, modify any of the command line parameters specified in the table below. Command line parameter Meaning Default value `atac-peak-qvalue` Threshold for q-value to call peaks 0.05 `atac-peak-fold-change` Fold change threshold (relative to the background) to call peaks 1.0 `atac-peak-min-length` Minimum length of a peak, bp NA\* `atac-peak-max-gap` Maximum pileup gap (if the gap is larger - initiate another peak), bp NA\*\* (\*), (\*\*) - default value is computed automatically as the mean fragment length. Alternatively, if fragment counting needs to be performed on a pre-specfified set of peaks, provide a peak BED file using command line parameter `atac-peak-bed-file`. Ref: Zhang, Y., Liu, T., Meyer, C.A., Eeckhoute, J., Johnson, D.S., Bernstein, B.E., Nusbaum, C., Myers, R.M., Brown M., Li. W. and Liu, X.S., 2008. Model-based Analysis of ChIP-Seq (MACS). \[PDF\] Available at: \[Accessed 1 March 2022\]. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#peak-annotation) Peak Annotation ---------------------------------------------------------------------------------------------------------------------------------------------------------------- DRAGEN annotates each peak with respect to a gene symbol as _promoter_, _distal_, or _intergenic_ depending on the genomic position of both the peak and the gene. The following rules are used to determine the annotation of a peak: * If a peak overlaps with promoter region (-1000bp, +100bp) of any transcription start site (TSS), it is annotated as a _promoter_ peak of the gene. * If a peak is within 200kb of the closest TSS, and if it is not a promoter peak of the gene of the closest TSS, it will be annotated as a _distal_ peak of that gene. * If a peak overlaps the body of a transcript, and it is not a promoter nor a distal peak of the gene, it will be annotated as a _distal_ peak of that gene with distance set as zero. * If a peak has not been mapped to any gene at the step, it will be annotated as an _intergenic_ peak without a gene symbol assigned. To enable peak annotation in DRAGEN scATAC-Seq workflow, specify a gene annotation file (GTF) using the option `-a`. Peak annotations are written to a file with name `.scATAC.peaks.tsv` and each annotation is represented as a row with the following 6 columns: * `Chromosome number` * `Start position` * `End position` * `Gene symbol` * `Distance from peak to gene` * `Peak annotation` (i.e, promoter, distal, or intergenic). [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#transcription-factor-motif-analysis) Transcription Factor Motif Analysis -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- In this analysis, peaks are matched to a set of known transcription factor (TF) binding sites and for each cell barcode the fragment counts are grouped based on transcription factors their peaks are assigned to. This results in a more compact representation of chromatin accessibility patterns. To enable TF motif analysis, specify a database of position-weight matrices (PWM) corresponding to transcription factor motifs in JASPAR format: `--atac-jaspar-database=JASPAR2022_CORE_non-redundant_pfms_jaspar.txt` DRAGEN will produce two files `.scATAC.tf.matrix.mtx.gz` and `.scATAC.tf.motifs.tsv.gz` which combined with file `.tf.barcodes.tsv.gz` represent the cell-by-TF count matrix in matrix market format. [PreviousscRNAchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scrna) [NextSingle-Cell Multiomicschevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics) Last updated 7 months ago Was this helpful? * [Input Files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#input-files) * [Alignment Reference](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#alignment-reference) * [Read Input](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#read-input) * [DRAGEN Single-cell Settings](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#dragen-single-cell-settings) * [Barcode Position](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#barcode-position) * [Known Barcode Lists](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#known-barcode-lists) * [Cell Filtering](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#cell-filtering) * [Additional Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#additional-options) * [Command-line Example](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#command-line-example) * [Outputs](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#outputs) * [Counts](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#counts) * [Alignments](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#alignments) * [Overall Metrics](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#overall-metrics) * [Per-cell metrics](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#per-cell-metrics) * [Barcode error correction](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#barcode-error-correction) * [Peak Calling](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#peak-calling) * [Peak Annotation](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#peak-annotation) * [Transcription Factor Motif Analysis](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-scatac#transcription-factor-motif-analysis) Was this helpful? Copy @D00626:253:CAT5WANXX:4:1302:8433:85343:GAAACTCGTTCAGCGC ACAGGTCAGCTGGGAGCTTCTGCCCCCACTGCCTAGGGACCAACAGGGGCAGGAGGCAGTCACTGACCCCGAGAAGTTTGCATCCTGCACAGCTAGAGATC + CCCCBFGGGGGGGGGGGGGGGGGGBDCGGGE>1BGDFGG0F@FBGGGBCDGGGGGGGGGGGGGGGGGGGGGGGDGGGGGGGGGGGGGFGGGGFGEGGGGGE Copy Lane,RGID,RGSM,RGLB,Read1File,Read2File,UmiFile 1,atac,sample1,illumina1,scATAC1.R1.fastq.gz,scATAC1.R2.fastq.gz,scATAC1.R3.fastq.gz 2,atac,sample1,illumina2,scATAC2.R1.fastq.gz,scATAC2.R2.fastq.gz,scATAC2.R3.fastq.gz Copy #-Block1 ... #-Block2 ... Copy dragen \ --enable-single-cell-atac=true \ --umi-source=fastq \ --scatac-barcode-position 0_15 \ -r reference_genomes/Mus_musculus/mm10/DRAGEN/8 \ -1 lib1_S7_L001_R1_001.fastq.gz \ -2 lib1_S7_L001_R2_001.fastq.gz \ --umi-fastq lib1_S7_L001_R3_001.fastq.gz \ --RGID=1 \ --RGSM=sample1 \ --output-dir=/staging/out \ --output-file-prefix=sample1 Copy > pip install -U scanpy pandas Copy # import libraries import pandas as pd import scanpy as sc # define path to input files matrix_path = "path/to/matrix.mtx.gz" peaks_path = "path/to/peaks.tsv.gz" barcodes_path = "path/to/barcodes.tsv.gz" # load matrix through scanpy adata = sc.read_mtx(matrix_path).T adata.var_names = pd.read_csv(peaks_path, sep="\t", header=None, compression="gzip")[1] adata.obs_names = pd.read_csv(barcodes_path, sep="\t", header=None, compression="gzip")[0] # convert scanpy internal format (AnnData) to dense pandas DataFrame df = pd.DataFrame(adata.X.todense(), index=adata.obs_names, columns=adata.var_names) # save it as CSV file df.to_csv("output_matrix.csv") --- # Noise Baselines | DRAGEN v4.3 | DRAGEN ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/resource-files/prebuilt-baseline-files#snv-systematic-noise-files) SNV Systematic Noise Files In somatic variant calling mode, systematic noise filter can be used to remove systematic noise observed in normal samples. Using this filter is essential for tumor-only and is recommended for tumor-normal mode. A systematic noise file can be generated by running the somatic TO pipeline on normal samples. We recommend using a systematic noise file based on normal samples that match the library prep of the tumor samples. For more details please look at [DRAGEN Variant Calling user guide](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/small-variant-calling/somatic-mode#systematic-noise-filtering) Alternately, a set of prebuilt SNV systematic noise BED files can be downloaded from the [DRAGEN Software Support Site pagearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) Assay Library Prep #Samples Sample type Coverage Systematic Noise File Name Comments WGS mixed 40 FF 70x WGS\_hg38\_v2.0.0\_systematic\_noise.snv.bed.gz can be used for FF WGS IDPF 46 PBMC,BMA 200x IDPF\_WGS\_hg38\_v.2.0.0\_systematic\_noise.snv.bed.gz can be used for heme WGS TruSeq PCR 42 FFPE 100x FFPE\_WGS\_hg38\_v2.0.0\_systematic\_noise.snv.bed.gz can be used for FFPE WES mixed 60 mix FF & FFPE 100x WES\_hg38\_v2.0.0\_systematic\_noise.snv.bed.gz can be used for FF, FFPE #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/resource-files/prebuilt-baseline-files#snv-systematic-noise-file-compatibility) SNV Systematic Noise File Compatibility Below is the table summarizing the compatibility of different SNV baseline noise versions and different DRAGEN major versions (from DRAGEN 3.7 to DRAGEN 4.3). SNV Systematic Noise Version 3.7 3.8 3.9 3.10 4.0 4.1 4.2 4.3 4.4 v2.0.0 ✔️ ✔️ v1.1.0 ✔️ v1.0.0 ✔️ ✔️ ✔️ ✔️ ✔️ ✔️ ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/resource-files/prebuilt-baseline-files#sv-systematic-noise-files) SV Systematic Noise Files When DRAGEN-SV is used in the somatic mode (tumor-only or tumor-normal), a BEDPE file with a set of paired-end regions in the BEDPE file format can be specified to filter out sequencing/systematic noise and also recurrent germline calls. The systematic noise BEDPE file is built using VCFs that were generated by the DRAGEN-SV tumor-only pipeline when run on normal samples that do not necessarily match to the subject the tumor sample was taken from. The file might contain several dozen samples. For more details please look at [DRAGEN SV user guide](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/sv-calling#systematic-noise-filtering) . A set of prebuilt SV systematic noise BEDPE files can be downloaded from the [DRAGEN Software Support Site pagearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) Assay Library Prep #Samples Sample type Coverage Systematic Noise File Name Comments WGS TruSeq PCR Free 100 1kg cell lines 30x WGS\_hg38\_v3.1.0\_systematic\_noise.sv.bedpe.gz can be used for FF, FFPE WGS TruSeq PCR Free 100 1kg cell lines 30x WGS\_hg19\_v3.1.0\_systematic\_noise.sv.bedpe.gz can be used for FF, FFPE WGS TruSeq PCR Free 100 1kg cell lines 30x WGS\_hs37d5\_v3.1.0\_systematic\_noise.sv.bedpe.gz can be used for FF, FFPE WGS IDPF 46 PBMC, BMA 200x WGS\_FF\_Heme\_hg38\_v3.1.0\_systematic\_noise.sv.bedpe.gz can be used for heme WGS IDPF 46 PBMC, BMA 200x WGS\_FF\_Heme\_hg19\_v3.1.0\_systematic\_noise.sv.bedpe.gz can be used for heme WGS IDPF 46 PBMC, BMA 200x WGS\_FF\_Heme\_hghs37d5\_chr\_v3.1.0\_systematic\_noise.sv.bedpe.gz can be used for heme WES/panels untested #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/reference/resource-files/prebuilt-baseline-files#sv-systematic-noise-file-compatibility) SV Systematic Noise File Compatibility Below is the table summarizing the compatibility of different SV baseline noise versions and different DRAGEN major versions (from DRAGEN 3.7 to DRAGEN 4.3). SV Systematic Noise Version 3.7 3.8 3.9 3.10 4.0 4.1 4.2 4.3 4.4 v3.1.0 ✔️ v3.0.0 ✔️ v2.0.0 ✔️ v1.0.0 ✔️ ✔️ [PreviousResource Fileschevron-left](https://help.dragen.illumina.com/dragen-v4.3/reference/resource-files) [NextSupplementary Informationchevron-right](https://help.dragen.illumina.com/dragen-v4.3/reference/additional-resources) Last updated 7 months ago Was this helpful? * [SNV Systematic Noise Files](https://help.dragen.illumina.com/dragen-v4.3/reference/resource-files/prebuilt-baseline-files#snv-systematic-noise-files) * [SV Systematic Noise Files](https://help.dragen.illumina.com/dragen-v4.3/reference/resource-files/prebuilt-baseline-files#sv-systematic-noise-files) Was this helpful? --- # DRAGEN Methylation Pipeline | DRAGEN v4.3 | DRAGEN The epigenetic methylation of cytosine bases in DNA can have a dramatic effect on gene expression, and bisulfite sequencing is the most common method for detecting epigenetic methylation patterns at single-base resolution. This technique involves chemically treating DNA with sodium bisulfite, which converts unmethylated cytosine bases to uracil, but does not alter methylated cytosines. Subsequent PCR amplification converts any uracils to thymines. A bisulfite sequencing library can either be nondirectional or directional. For nondirectional, each double-stranded DNA fragment yields four distinct strands for sequencing, post-amplification, as shown in the following figure: ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3660538983-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FpFn0gLe5V859SXejQNAW%252Fuploads%252Fgit-blob-24e783dcfb6795ff7243fcef3cdcddec2ec602a7%252Fdragen-methylation-pipeline.NondirectionalBisulfateSequencing.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=78ad6770&sv=2) * Bisulfite Watson (BSW), reverse complement of BSW (BSWR), * Bisulfite Crick (BSC), reverse complement of BSC (BSCR) For directional libraries, the four strand types are generated, but adapters are attached to the DNA fragments such that only the BSW and BSC strands are sequenced (Lister protocol). Less commonly, the BSWR and BSCR strands are selected for sequencing (eg, PBAT). BSW and BSC strands: * A, G, T: unchanged * Methylated C remains C * Unmethylated C converted to T BSWR and BSCR strands: * Bases complementary to original Watson/Crick A, G, T bases remain unchanged * G complementary to original Watson/Crick methylated C remains G * G complementary to original Watson/Crick unmethylated C becomes A Therefore, several steps are performed to map methylation, as shown in the following flowchart: ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3660538983-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FpFn0gLe5V859SXejQNAW%252Fuploads%252Fgit-blob-2de5c4006452b3a93bca21fad27620c94eb1a71f%252Fdragen-methylation-pipeline.flowChart.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=b428b760&sv=2) Details on each part of the mapping process can be found below. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-methylation-pipeline#single-pass-methylation-mapping) Single-pass Methylation Mapping DRAGEN methylation mapping works in one single alignment run. During alignment, the mapper considers all possible base and reference conversions for the read and emits the single best alignment to a particular methylation strand if one exists. Any read (pair) that did not have a single best scoring alignment across all methylation strands tested appears in the output BAM with MAPQ 0. The output BAM in single-pass might contain mapped reads that do not have the XM, XR, and XG methylation tags. Methylation data from reads that do not have methylation tags are not tallied into the reports or metrics files. `--enable-methylation-calling` must be set to true to enable single-pass methylation mapping. A read must meet the following requirements to have methylation tags in the output BAM and to get tallied into the reports and metrics: * The read and its mate (if applicable) are mapped with MAPQ above the value specified using `--methylation-mapq-threshold`. The default value is 0. * The read is not part of an improper pair. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-methylation-pipeline#build-a-methylation-hash-table) Build a Methylation Hash Table -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The DRAGEN methylation pipeline requires a methylation-specific hash table in which combines both C to T and G to A conversions of contigs from the original FASTA. Specifically, each contig appears twice, once with C bases converted to T bases, and once with G bases converted to A bases. When `--ht-methylated-combined=true` is set, the DRAGEN hash table builder will create a methylation hash table in a sub-directory of the output directory called `methyl_converted`. When running the DRAGEN mapper, the top level directory should be provided (parent of methyl\_converted), and DRAGEN will automatically locate the methylation sub-directory and use it as needed. The top level directory can be used for regular mapping. Due to the base conversions in the methylation hash table, short seeds map poorly. So the default and recommended seed length of the methylation hash table is 27. The following is an example command line for a methylation hash table. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-methylation-pipeline#methylation-calling) Methylation Calling ---------------------------------------------------------------------------------------------------------------------------------------------------------- Different methylation protocols require the generation of two or four alignments per input read, followed by an analysis to choose a best alignment and determine which cytosines are methylated. DRAGEN can automate this process by generating a single output BAM file with Bismark-compatible tags (XR, XG, and XM) that can be used for methylation calling and other downstream workflows. When the `--methylation-protocol` option is set to a valid value other than none, DRAGEN automatically produces the required set of alignment runs. Each alignment run includes the appropriate base conversions on the reads, base conversions on the reference, and constraints on whether reads must be forward-aligned or reverse complement (RC) aligned with the reference. The following options are automatically configured: * \--generate-md-tags true * \--Aligner.global 1 * \--Aligner.no-unpaired 1 * \--Aligner.aln-min-score 0 * \--Aligner.min-score-coeff -0.2 * \--Aligner.match-score 0 * \--Aligner.mismatch-pen 4 * \--Aligner.gap-open-pen 6 * \--Aligner.gap-ext-pen 1 * \--Aligner.supp-aligns 0 * \--Aligner.sec-aligns 0 * \--seed-density 1 Because global alignments (end-to-end in the reads) are generated, DRAGEN recommends trimming any artifacts introduced by library prep and adapter sequences. The following table describes the properties of the alignment runs: Protocol BAM Reference Read 1 Read 2 Orientation Constraint **directional** 1 C->T C->T G->A Forward-only 2 G->A C->T G->A RC-only **non-directional, or directional-complement** 1 C->T C->T G->A Forward-only 2 G->A C->T G->A RC-only 3 C->T G->A C->T RC-only 4 G->A G->A C->T Forward-only **PBAT** 3 C->T G->A C->T RC-only 4 G->A G->A C->T Forward-only In directional protocols, the library is prepared such that only the BSW and BSC strands are sequenced. Thus, alignment runs are performed with the two combinations of base conversions and orientation constraints best suited for these strands (directional runs 1 and 2 above). With nondirectional protocols, reads from each of the four strands are equally likely, so alignment runs must be performed with two more combinations of base conversions and orientation constraints (nondirectional runs 3 and 4 above). In PBAT protocols, the library is prepared so only the BSWR and BSCR strands are sequenced. Only two alignment runs are performed with the combinations of base conversions and orientation constraints best suited for these strands (runs 3 and 4). The directional-complement protocol can also be used for PBAT or similar libraries where mainly the BSWR and BSCR strands are sequenced. With this protocol, all four aligner runs are performed, but relatively few good alignments are expected from the runs for the BSW and BSC strands, so DRAGEN is automatically tuned to a faster analysis mode for those runs. The following is an example DRAGEN command line for the directional protocol: [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-methylation-pipeline#sort-and-duplicate-reads-options) Sort and Duplicate Reads Options ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ An end-to-end (ie., fastq->bam->cytosine report) run can be performed as follows: * To generate sorted alignment output (in BAM format), set `--enable-sort` to true. * To detect duplicate reads, set `--enable-duplicate-marking` to true. * \[Optional\]To remove duplicate reads, set `--remove-duplicates` to true. * \[Optional\]Set `--methylation-generate-cytosine-report` and `--methylation-generate-mbias-report` to either false or true according to user need. By default, DRAGEN methylation performs strand-aware dedup in concordance with Bismark. Strand-aware dedup partitions the mapped reads into four groups, one per methylation strand. Within each group, DRAGEN performs a normal dedup. For paired reads, the strand of the pair is defined as the strand of the first read in the pair. The following example demonstrates strand-aware dedup for paired-end reads. The example pairs all map to the same position, but the first read in each pair (BAM flag 83 and 99) is mapped to a different methylation strand, as shown by the different values of the XR and XG tags. None of these pairs are marked as duplicates. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-methylation-pipeline#umi-unique-molecular-identifiers-support) UMI (Unique Molecular Identifiers) support ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ DRAGEN support fastq files that contains [UMIarrow-up-right](https://www.illumina.com/techniques/sequencing/ngs-library-prep/multiplexing/unique-molecular-identifiers.html) barcode during the alingment phase. The principle and requirement are identical to DNA UMI. Briefly, during library prep, (methyl-treated) DNA fragments could be barcoded by unique molecular identifiers, so that true signals from the original fragments can be separated from PCR error and sequencing error, which enables more accurate methylation calling. The fastq files need to have UMI barcode in 7th field of the QNAME. eg. @NS500561:434:H5LC2BGXJ:1:11101:10798:1359:_**CACATGA+ACATTC**_ 1:N:0:TGGTACCTAA+AGTACTCATG To enable UMI, either set `--umi-enable true` if you are using random UMI (common), or set `--tso500-solid-umi true` if you are using the same non-random UMIs as the TSO500 solid panel. If so, read collapsing will be performed among reads with the same UMI that are mapped to the same genomic location at the same strand, either from top (OT/CTOT) or bottom (OB/CTOB) strand. See DRAGEN DNA Pipeline / Unique Molecular Identifiers for more details. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-methylation-pipeline#using-taps-support) Using TAPS support -------------------------------------------------------------------------------------------------------------------------------------------------------- TET-Assisted Pyridine Borane Sequencing (TAPS) is a new assay which directly converts methylated C to T, whereas typical bisulfite conversion converts unmethylated C to T. This approach preserves genomic complexity and uses less destructive chemicals to enable lower input DNA. To enable analysis of FASTQ data generated through TAPS, set `--methylation-TAPS` to true. By default, the option is false. This option is performed only during the alignment step and is not necessary when generating methylation cytosine and M-Bias reports from an existing BAM. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-methylation-pipeline#methylation-related-bam-tags) Methylation-Related BAM Tags ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- When `--enable-methylation-calling` is set to true, DRAGEN analyzes the alignments produced for the configured `--methylation-protocol` and generates a single output BAM file that includes methylation-related tags for all mapped reads. As in Bismark, reads without a unique best alignment are excluded from the output BAM. The added tags are as follows. Tag Brief Description Description XR:Z Read conversion For the best alignment, which base-conversion was performed on the read: CT or GA. XG:Z Reference conversion For the best alignment, which base-conversion was performed on the reference: CT or GA XM:Z Methylation call A byte-per-base methylation string. The XM:Z (methylation call) tag contains a byte that corresponds to each base in the sequence of the read. Each position that does not involve a cytosine contains a period (.). Each position that does involve a cytosine contains a letter. The letter indicates the context (CpG, CHG, CHH, or unknown). The case indicates methylation. Methylated positions use upper-case and unmethylated positions use lower-case. The letters used at cytosine positions are as follows. Character Methylated? Context . not cytosine not cytosine z No CpG Z Yes CpG X No CHG X Yes CHG h No CHH H Yes CHH u No Unknown U Yes Unknown [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-methylation-pipeline#methylation-cytosine-and-m-bias-reports) Methylation Cytosine and M-Bias Reports -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- You can use DRAGEN to generate a genome-wide cytosine methylation report. Your command line options settings depend on if you are running using FASTQ through the aligner or a prealigned BAM that already contains the methylation tags. * For FASTQ input, set `--methylation-generate-cytosine-report=true` * For BAM input, set `--methylation-reports-only=true` To keep all cytosines from your reference in the `CX_report`, even if they are not included in the input sequences, set `--methylation-keep-ref-cytosine true`. The default value is false. Setting this option to true increases run time and the `CX_report` file size. To compress the cytosine report, set `--methylation-compress-cx-report = true`. The default value is false. DRAGEN outputs a compressed `*.CX_report.txt.gz`, instead of a `*.CX_report.txt`. The position and strand of each C in genome are given in the first three fields of the report. A record with a - in the strand field is used for a G in the reference FASTA. The counts of methylated and unmethylated Cs covering the positions are given in the fourth and fifth fields. The C context in the reference (CG, CHG, or CHH) is given in the sixth field. The trinucleotide sequence context is given in the last field (eg, CCC, CGT, CGA, and so on) The cytosine report only includes records for positions that have one or more spanning alignments. The following is an example cytosine report record: `chr2 24442367 + 18 0 CG CGC` To generate an M-bias report, set `--methylation-generate-mbias-report` to true. This report contains three tables for single-ended data with one table for each C-context and six tables for paired-end data. Each table is a series of records, with one record per read base position. For example, the first record for the CHG table contains the counts of methylated Cs (field 2) and unmethylated Cs (field 3) that occur in the first read base position, and restricts to those reads in which the first base is aligned to a CHG location in the genome. Each record of a table also includes the percent methylated C bases (field 4) and the sum of methylated and unmethylated C counts (field 5). The following is an example M-bias record for read base position 10: `10 7335 2356 75.69 9691` For data sets with paired-end reads that overlap, both the cytosine and M-bias reports do not report any Cs in the second read that overlaps the first read. In addition, 1-based coordinates are used for positions in both reports.. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-methylation-pipeline#output-metrics) Output Metrics ------------------------------------------------------------------------------------------------------------------------------------------------ The quality of each methylation run can be summarized in the following two metric files. * `*.mapping_metrics.csv`—Contains mapping-specific metrics that are generated for the alignment phase, including benchmarks like number of total reads, aligned reads, deduped reads, base quality, etc. * `*.methyl_metrics.csv`—Contains methylation-specific metrics that are generated for the methylation calling phase, including benchmarks like the total number of cytosines analyzed, count and rate of methylation in each cytosine context, strand of the best alignment, etc. [PreviousSingle-Cell Multiomicschevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-single-cell-pipeline/dragen-multiomics) [NextDRAGEN Amplicon Pipelinechevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-amplicon-pipeline) Last updated 7 months ago Was this helpful? * [Build a Methylation Hash Table](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-methylation-pipeline#build-a-methylation-hash-table) * [Methylation Calling](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-methylation-pipeline#methylation-calling) * [Sort and Duplicate Reads Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-methylation-pipeline#sort-and-duplicate-reads-options) * [UMI (Unique Molecular Identifiers) support](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-methylation-pipeline#umi-unique-molecular-identifiers-support) * [Using TAPS support](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-methylation-pipeline#using-taps-support) * [Methylation-Related BAM Tags](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-methylation-pipeline#methylation-related-bam-tags) * [Methylation Cytosine and M-Bias Reports](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-methylation-pipeline#methylation-cytosine-and-m-bias-reports) * [Output Metrics](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-methylation-pipeline#output-metrics) Was this helpful? Copy dragen --build-hash-table true \ --output-directory=sample.output.directory \ --ht-reference=sample.input.fa \ --ht-methylated-combined=true Copy dragen --enable-methylation-calling true \ --methylation-protocol directional \ --ref-dir /staging/ref/mm10/methylation --RGID RG1 --RGCN CN1 \ --RGLB LIB1 --RGPL illumina --RGPU 1 --RGSM Samp1 \ --intermediate-results-dir /staging/tmp \ -1 /staging/reads/samp1_1.fastq.gz \ -2 /staging/reads/samp1_2.fastq.gz \ --output-directory /staging/outdir \ --output-file-prefix samp1_directional\_prot Copy pair1 83 lambda 44001 60 150M = 43651 ... XR:Z:CT XG:Z:GA pair1 163 lambda 43651 60 150M = 44001 ... XR:Z:GA XG:Z:GA pair2 83 lambda 44001 60 150M = 43651 ... XR:Z:GA XG:Z:CT pair2 163 lambda 43651 60 150M = 44001 ... XR:Z:CT XG:Z:CT pair3 147 lambda 44001 60 150M = 43651 ... XR:Z:GA XG:Z:CT pair3 99 lambda 43651 60 150M = 44001 ... XR:Z:CT XG:Z:CT --- # Illumina Connected Annotations | DRAGEN v4.3 | DRAGEN Illumina Connected Annotations, also known as Illumina Annotation Engine (IAE) or Nirvana provides translational research-grade annotation of genomic variants (SNVs, MNVs, insertions, deletions, indels, STRs, gene fusions, and SVs (including CNVs). It can be run as a stand-alone package, or integrated into larger software tools that require variant annotation. Users can annotate VCF files by enabling annotation on the DRAGEN command-line or by running the standalone tool. The input to Illumina Connected Annotations are VCFs and the output is a structured JSON representation of all annotation and sample information (as extracted from the VCF). Illumina Connected Annotations handles multiple alternate alleles and multiple samples with ease. **NOTE:** Before running Annotations, the external data sources, gene models, and reference genome needs to be downloaded from our annotation server. By default, the Annotations binaries are located in the `/opt/dragen//share/nirvana` directory. This directory includes two files: the Downloader and Nirvana (Illumina Connected Annotations). [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/nirvana#limitations) Limitations ---------------------------------------------------------------------------------------------------------------------- Illumina Connected Annotations and the Downloader are compatible with the following platforms: * CentOS 7, Oracle 8 and other modern Linux distributions using x64 processors. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/nirvana#download-data-files) Download Data Files -------------------------------------------------------------------------------------------------------------------------------------- > For more upto date and detailed documentation please visit [Illumina Connected Annotations Download Dataarrow-up-right](https://illumina.github.io/IlluminaConnectedAnnotationsDocumentation/3.23/introduction/getting-started#downloading-the-data-files) To store annotation data files, create a top-level directory. The created directory contains three subdirectories: * Cache contains gene models. * SupplementaryAnnotation contains external data sources like dbSNP and gnomAD. * References contains the reference genome. The following command-line options are used. Option Value Example Description \--ga GRCh37, GRCh38, or Both GRCh38 Genome assembly \--out output directory ~/Data Top-level output directory Download data files as follows. 1. To create a data directory, enter the following command. This example creates the Data directory in your home directory. 1. Download the files for a genome assembly. This example downloads the genome assembly GRCh38. You can use the same command to resynchronize the data sources with the Illumina Connected Annotations servers, including the following actions: * Remove obsolete files, such as old versions of data sources, from the output directory. * Download newer files. The following is the created output: **NOTE:** If the DRAGEN server does not have an internet connection, the Downloader executable can be copied to a non-DRAGEN server that is connected to the internet to download the annotation data. Once the download has completed, the annotation data can then be copied locally to the DRAGEN server for subsequent annotation. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/nirvana#annotate-files-via-dragen-command-line) Annotate Files (via DRAGEN command-line) ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ To automatically annotate output VCFs, please add the following command-line arguments: Argument Example Description \--enable-variant-annotation true enables annotation if the pipeline supports it \--variant-annotation-data /path/to/your/NirvanaData the location where you downloaded the Nirvana annotation files \--variant-annotation-assembly GRCh38 the genome assembly - either GRCh37 or GRCh38. hg19 is handled properly by using GRCh37 All the command-line arguments shown together: [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/nirvana#annotate-files-via-standalone-illumina-connected-annotations-tool) Annotate Files (via standalone Illumina Connected Annotations tool) ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ 1. If you have not generated a VCF file, download a VCF file using the following command. Annotations supports uncompressed VCF files and bgzip compressed VCF files. VCF files that have been compressed by standard gzip are not supported. 1. To annotate the file, enter the following command: The following are the available command line options: Option Value Example Description \-c directory ~/Data/Cache/ Cache directory \-r directory ~/Data/References/Homo\_sapiens.GRCh38.Nirvana.dat Reference directory \--sd directory ~/Data/SupplementaryAnnotation/GRCh38 Supplementary annotation directory \-i path HiSeq.10000.vcf.gz Input VCF path \-o prefix HiSeq.10000 Output path prefix Using the example above, Annotations generates the following output called `HiSeq.10000.json.gz`. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/nirvana#json-output-file) JSON Output File -------------------------------------------------------------------------------------------------------------------------------- Annotations produces an output file in JSON format. Please refer to [Illumina Connected Annotations JSONarrow-up-right](https://illumina.github.io/IlluminaConnectedAnnotationsDocumentation/file-formats/illumina-annotator-json-file-format#header) for detailed description of the JSON file. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/nirvana#version-history) Version History ------------------------------------------------------------------------------------------------------------------------------ Annotations binaries have been included with DRAGEN since v3.5. The table below indicates which version of Annotations binaries were included with different DRAGEN releases, and their AI annotation capabilities. > The Annotations binaries distributed with DRAGEN can not be changed. Never versions of Annotations are backward compatible, and can therefore annotate output files from older DRAGEN releases. DRAGEN version(s) Annotations version AI annotations 4.3 3.23 spliceAI, primateAI3D 3.9, 3.10, 4.0, 4.1, 4.2 3.16.1 spliceAI, primateAI 3.8 3.14 spliceAI, primateAI 3.6, 3.7 3.9.0 spliceAI, primateAI 3.5 3.6.0 spliceAI, primateAI [PreviousBCL conversionchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/bcl-conversion) [NextORA Compressionchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/ora-compression) Last updated 7 months ago Was this helpful? * [Limitations](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/nirvana#limitations) * [Download Data Files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/nirvana#download-data-files) * [Annotate Files (via DRAGEN command-line)](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/nirvana#annotate-files-via-dragen-command-line) * [Annotate Files (via standalone Illumina Connected Annotations tool)](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/nirvana#annotate-files-via-standalone-illumina-connected-annotations-tool) * [JSON Output File](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/nirvana#json-output-file) * [Version History](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/nirvana#version-history) Was this helpful? Copy mkdir ~/Data Copy /share/nirvana/Downloader --ga GRCh38 --out ~/Data Copy --------------------------------------------------------------------------- Downloader (c) 2024 Illumina, Inc. 3.23.0 --------------------------------------------------------------------------- - downloading manifest... 37 files. - downloading file metadata: - finished (00:00:00.8). - downloading files (22.123 GB): - downloading 1000_Genomes_Project_Phase_3_v3_plus_refMinor.rma.idx (GRCh38) - downloading MITOMAP_20200224.nsa.idx (GRCh38) - downloading ClinVar_20200302.nsa.idx (GRCh38) - downloading REVEL_20160603.nsa.idx (GRCh38) - downloading phyloP_hg38.npd.idx (GRCh38) - downloading ClinGen_Dosage_Sensitivity_Map_20200131.nsi (GRCh38) - downloading MITOMAP_SV_20200224.nsi (GRCh38) - downloading dbSNP_151_globalMinor.nsa.idx (GRCh38) - downloading ClinGen_Dosage_Sensitivity_Map_20190507.nga (GRCh38) - downloading PrimateAI_0.2.nsa.idx (GRCh38) - downloading ClinGen_disease_validity_curations_20191202.nga (GRCh38) - downloading 1000_Genomes_Project_Phase_3_v3_plus.nsa.idx (GRCh38) - downloading SpliceAi_1.3.nsa.idx (GRCh38) - downloading dbSNP_153.nsa.idx (GRCh38) - downloading TOPMed_freeze_5.nsa.idx (GRCh38) - downloading MITOMAP_20200224.nsa (GRCh38) - downloading gnomAD_2.1.nsa.idx (GRCh38) - downloading ClinGen_20160414.nsi (GRCh38) - downloading gnomAD_gene_scores_2.1.nga (GRCh38) - downloading 1000_Genomes_Project_(SV)_Phase_3_v5a.nsi (GRCh38) - downloading MultiZ100Way_20171006.pcs (GRCh38) - downloading 1000_Genomes_Project_Phase_3_v3_plus_refMinor.rma (GRCh38) - downloading ClinVar_20200302.nsa (GRCh38) - downloading OMIM_20200409.nga (GRCh38) - downloading Both.transcripts.ndb (GRCh38) - downloading REVEL_20160603.nsa (GRCh38) - downloading PrimateAI_0.2.nsa (GRCh38) - downloading dbSNP_151_globalMinor.nsa (GRCh38) - downloading Both.sift.ndb (GRCh38) - downloading Both.polyphen.ndb (GRCh38) - downloading Homo_sapiens.GRCh38.Nirvana.dat - downloading 1000_Genomes_Project_Phase_3_v3_plus.nsa (GRCh38) - downloading phyloP_hg38.npd (GRCh38) - downloading SpliceAi_1.3.nsa (GRCh38) - downloading TOPMed_freeze_5.nsa (GRCh38) - downloading dbSNP_153.nsa (GRCh38) - downloading gnomAD_2.1.nsa (GRCh38) - finished (00:04:10.1). Description Status --------------------------------------------------------------------------- 1000_Genomes_Project_(SV)_Phase_3_v5a.nsi (GRCh38) OK 1000_Genomes_Project_Phase_3_v3_plus.nsa (GRCh38) OK 1000_Genomes_Project_Phase_3_v3_plus.nsa.idx (GRCh38) OK 1000_Genomes_Project_Phase_3_v3_plus_refMinor.rma (GRCh38) OK 1000_Genomes_Project_Phase_3_v3_plus_refMinor.rma.idx (... OK Both.polyphen.ndb (GRCh38) OK Both.sift.ndb (GRCh38) OK Both.transcripts.ndb (GRCh38) OK ClinGen_20160414.nsi (GRCh38) OK ClinGen_Dosage_Sensitivity_Map_20190507.nga (GRCh38) OK ClinGen_Dosage_Sensitivity_Map_20200131.nsi (GRCh38) OK ClinGen_disease_validity_curations_20191202.nga (GRCh38) OK ClinVar_20200302.nsa (GRCh38) OK ClinVar_20200302.nsa.idx (GRCh38) OK Homo_sapiens.GRCh38.Nirvana.dat OK MITOMAP_20200224.nsa (GRCh38) OK MITOMAP_20200224.nsa.idx (GRCh38) OK MITOMAP_SV_20200224.nsi (GRCh38) OK MultiZ100Way_20171006.pcs (GRCh38) OK OMIM_20200409.nga (GRCh38) OK PrimateAI_0.2.nsa (GRCh38) OK PrimateAI_0.2.nsa.idx (GRCh38) OK REVEL_20160603.nsa (GRCh38) OK REVEL_20160603.nsa.idx (GRCh38) OK SpliceAi_1.3.nsa (GRCh38) OK SpliceAi_1.3.nsa.idx (GRCh38) OK TOPMed_freeze_5.nsa (GRCh38) OK TOPMed_freeze_5.nsa.idx (GRCh38) OK dbSNP_151_globalMinor.nsa (GRCh38) OK dbSNP_151_globalMinor.nsa.idx (GRCh38) OK dbSNP_153.nsa (GRCh38) OK dbSNP_153.nsa.idx (GRCh38) OK gnomAD_2.1.nsa (GRCh38) OK gnomAD_2.1.nsa.idx (GRCh38) OK gnomAD_gene_scores_2.1.nga (GRCh38) OK phyloP_hg38.npd (GRCh38) OK phyloP_hg38.npd.idx (GRCh38) OK --------------------------------------------------------------------------- Peak memory usage: 52.3 MB Time: 00:04:12.2 Copy --enable-variant-annotation=true --variant-annotation-data=/path/to/your/NirvanaData --variant-annotation-assembly=GRCh38 Copy curl -O https://raw.githubusercontent.com/HelixGrind/DotNetMisc/master/TestFiles/HiSeq.10000.vcf.gz Copy /share/nirvana/Nirvana -c ~/Data/Cache/ \ -r ~/Data/References/Homo_sapiens.GRCh38.Nirvana.dat \ --sd ~/Data/SupplementaryAnnotation/GRCh38 -i HiSeq.10000.vcf.gz -o HiSeq.10000 Copy --------------------------------------------------------------------------- Illumina Connected Annotations (c) 2024 Illumina, Inc. 3.23.0 --------------------------------------------------------------------------- Initialization Time Positions/s --------------------------------------------------------------------------- Cache 00:00:01.9 SA Position Scan 00:00:00.4 23,867 Reference Preload Annotation Variants/s --------------------------------------------------------------------------- chr1 00:00:00.4 00:00:03.7 2,651 Summary Time Percent --------------------------------------------------------------------------- Initialization 00:00:02.3 25.7 % Preload 00:00:00.4 5.4 % Annotation 00:00:03.7 41.5 % Peak memory usage: 1.284 GB Time: 00:00:08.0 --- # High Coverage Analysis | DRAGEN v4.3 | DRAGEN While DRAGEN secondary analysis is capable of supporting up to 1000x coverage, its default settings are tuned for a more typical sample size in the ~100x range. So if you find that the processing of your large sample doesn't complete, or gives unexpected results, there are options available to improve the behavior. Users may want to analyze high amounts of data using the DRAGEN secondary analysis. For instance, in somatic contexts it can be beneficial to sequence the tumor at a very high depth to detect mutations at even lower frequencies. DRAGEN reliably supports a total average coverage of up to 1000x. As the input read data can grow excessively, but the system memory is limited, DRAGEN can only keep a subset of the input in RAM at the same time. The area reserved for the read data is called _bin\_memory_. Higher bin\_memory size means that bigger chunks can be processed simultaneously, but less memory is available to the rest of DRAGEN or for other processes. After the map-align step, reads are loaded into the bin\_memory, looking for regions of zero coverage. A set of reads that spans two such zero-coverage loci is a _callable region_. The memory used by a callable region is determined by the number of reads and their length. For instance, a long region with few reads per position uses the same amount of memory as a short region with a spike in coverage. The size of a callable region must stay well below the size of the bin\_memory. To this end, any callable region that surpasses the `--vc-max-callable-region-memory-usage` threshold is cut into smaller regions. Due to these cuts, the accuracy of variant calls in the vicinity may be affected. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/high-coverage#command-line-options) Command Line Options ------------------------------------------------------------------------------------------------------------------------------------------------------------------ The following options can be used to change the bin\_memory and callable region size. * `--bin_memory` Set the amount of memory reserved for read data. Defaults to at least 20GB for germline and 40GB for a somatic run. * `--vc-max-callable-region-memory-usage` Set the maximum size of a single callable region. Default is 13GB. [PreviousStar Allele Callerchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller) [NextCheckFingerprintchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/checkfingerprint) Last updated 7 months ago Was this helpful? Was this helpful? --- # DRAGEN v4.5 | DRAGEN [Getting Startedchevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/getting-started) [DRAGEN Host Softwarechevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-host-software) [DRAGEN Appschevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-apps) [DRAGEN Recipeschevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-recipes) [DRAGEN Reference Supportchevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-reference-support) [DRAGEN DNA Pipelinechevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-dna-pipeline) [DRAGEN RNA Pipelinechevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-rna-pipeline) [DRAGEN Single Cell Pipelinechevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-single-cell-pipeline) [Illumina TruPath Genome Pipelinechevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-trupath-pipeline) [DRAGEN Methylation Pipelinechevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-methylation-pipeline) [DRAGEN MRD Pipelinechevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/mrd) [DRAGEN Amplicon Pipelinechevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-amplicon-pipeline) [DRAGEN 16S Pipelinechevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-16s-pipeline) [DRAGEN K-mer Classifierchevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/kmer-classifier) [DRAGEN Microbial Enrichment Pluschevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dme-plus-overview) [BCL conversionchevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/bcl-conversion) [DRAGEN QC Metricschevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/qc-metrics-reporting) [Illumina Connected Annotationschevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/nirvana) [ORA Compressionchevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/ora-compression) [Command Line Optionschevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/command-line-options) [DRAGEN Reportschevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-reports) [Tools and Utilitieschevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/tools-and-utilities) [PreviousDeployment Optionschevron-left](https://help.dragen.illumina.com/overview/deployment-options) [NextGetting Startedchevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/getting-started) Last updated 20 days ago Was this helpful? Was this helpful? --- # Tools and Utilities | DRAGEN v4.3 | DRAGEN ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/tools-and-utilities#monitoring-system-health) Monitoring System Health When DRAGEN runs, a daemon `dragend` is started that communcates with the FPGA card. This daemon will monitor FPGA temperature while DRAGEN is running and abort DRAGEN when the temperature exceeds a configured threshold. To display the current temperature of the DRAGEN server FPGA, use the dragen\_info -t command. Copy % dragen_info -t FPGA Temperature: 42C  (Max Temp: 49C, Min Temp: 39C) #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/tools-and-utilities#logging) Logging All hardware events are logged to /var/log/messages and /var/log/dragen\_mond.log. The following shows an example in /var/log/messages of a temperature alarm: Copy Jul 16 12:02:34 komodo dragen_mond[26956]: WARNING: FPGA software over temperature alarm has been triggered -- temp threshold: 85 (Chip status: 0x80000001) Jul 16 12:02:34 komodo dragen_mond[26956]: Current FPGA temp: 86, Max temp: 88, Min temp: 48 Jul 16 12:02:34 komodo dragen_mond[26956]: All dragen processes will be stopped until alarm clears Jul 16 12:02:34 komodo dragen_mond[26956]: Terminating dragen in process 1510 with SIGUSR2 signal By default, temperature is logged to /var/log/dragen\_mond.log every hour: Copy Aug 01 09:16:50 Setting FPGA hardware max temperature threshold to 100 Aug 01 09:16:50 Setting FPGA software max temperature threshold to 85 Aug 01 09:16:50 Setting FPGA software min temperature threshold to 75 Aug 01 09:16:50 FPGA temperatures will be logged every 3600 seconds Aug 01 09:16:50 Current FPGA temperature is 52 (Max temp = 52, Min temp = 52) Aug 01 10:16:50 Current FPGA temperature is 53 (Max temp = 56, Min temp = 49) Aug 01 11:16:50 Current FPGA temperature is 54 (Max temp = 56, Min temp = 49) If DRAGEN is executing when a thermal alarm is detected, the following is displayed in the terminal window of the DRAGEN process: If you see this message, stop running the DRAGEN software. Do the following to alleviate the overheating condition on the card: * Be sure that there is ample air flow over the card. Consider moving the card to a slot where there is more air flow, adding another fan or increasing the fan speed. * Give the card more space in the box. If there are available PCIe slots, move the card so that it has empty slots on either side. Contact Illumina Technical Support if you are having trouble resolving the thermal alarm on your system. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/tools-and-utilities#hardware-alarms) Hardware Alarms The following table lists the hardware events logged by the monitor when an alarm is triggered: ID Description Monitor Action 0 Software overheating Terminate usage until DRAGEN server FPGA cools to software minimum temperature. 1 Hardware overheating Fatal. Aborts dragen software; system reboot required 2 Board SPD overheating Logged as nonfatal 3 SODIMM overheating Logged as nonfatal 4 Power 0 Fatal. Aborts dragen software; system reboot required 5 Power 1 Fatal. Aborts dragen software; system reboot required 6 DRAGEN server FPGA power Logged as nonfatal 7 Fan 0 Logged as nonfatal 8 Fan 1 Logged as nonfatal 9 SE5338 Fatal. Aborts dragen software; system reboot required 10--30 Undefined (Reserved) Fatal. Aborts dragen software; system reboot required Fatal alarms prevent the DRAGEN host software from running and require a system reboot. When a software overheating alarm is triggered, the monitor looks for and aborts any running DRAGEN processes. The monitor continues to abort any new DRAGEN processes until the temperature decreases to the minimum threshold and the hardware clears the chip status alarm. When the software overheating alarm clears, DRAGEN jobs can resume executing. Contact Illumina Technical Support with details from the log files if any of these alarms are triggered on your system. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/tools-and-utilities#hardware-accelerated-compression-and-decompression) Hardware-Accelerated Compression and Decompression Gzip compression is ubiquitous in bioinformatics. FASTQ files are often gzipped, and the BAM format itself is a specialized version of gzip. For that reason, DRAGEN provides hardware support for accelerating compression and decompression of gzipped data. If your input files are gzipped, DRAGEN detects that and decompresses the files automatically. If your output is BAM files, then the files are automatically compressed. DRAGEN provides standalone command-line utilities to enable you to compress or decompress arbitrary files. These utilities are analogous to the Linux gzip and gunzip commands, but are named dzip and dunzip (dragen zip and dragen unzip). Both utilities are able to accept as input a single file, and produce a single output file with the .gz file extension removed or added, as appropriate. For example: Currently, dzip and dunzip have the following limitations and differences from gzip/gunzip: * Each invocation of these tools can handle only a single file. Additional file names (including those produced by a wildcard \* character) are ignored. * They cannot be run at the same time as the DRAGEN host software. * They do not support the command line options found in gzip and gunzip (eg, --recursive, --fast, ‑‑best, ‑‑stdout). ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/tools-and-utilities#licensing-and-usage) Licensing and Usage For a DRAGEN server, there are two tools provided during the install process to assist with licensing of the system. * dragen\_lic - A command line tool which can be utilized to perform licensing actions by a user. * dragen\_licd - A daemon running in the background which communicates once a day with an Illumina License Server to perform automated licensing actions. Usage reporting is a key component of the DRAGEN Licensing infrastructure. When a license is installed, any unreported usage data will automatically be uploaded to Illumina's License server. Note that Communication to the Illumina server is secured by encryption over an HTTP connection. Usage data entails the following information for each individual run * run date * run duration * licensing quota consumed (number of bases) in that run * run status * software version used for the run. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/tools-and-utilities#dragen_lic-command-line-tool) dragen\_lic command line tool Used to perform licensing actions by a user via the command line. For versions lower than 4.3, this tool can be found at `/opt/edico/bin/dragen_lic`. For 4.3 and higher, it can be found at `/usr/bin/dragen_lic` Common actions would be to view licensing information (expiration date and remaining quota) and installing licenses. Examples of these actions are shown below. **Viewing License Information** There are three options for viewing licesing information. Examples for each one are below. * Default Output (i.e. no additional arguments). This is the legacy method for retreiving license information, and prior to DRAGEN v4.3 this was the only method available. In general it is recommended to instead use one of the following two options. * Basic Output (i.e. using the -b flag). This is the recommended method to view license information by a human user as the output is simplified to be more readable. * JSON Output (i.e. using the -j flag). This is the recommended method to view license information by a machine user as the output is already in a machine readable format. * Basic mode (-b) will not show a license if it has been expired by more then 90 days. * Basic mode (-b) and JSON mode (-j) collapse the "DRAGEN Core" licenses into a single entry, as this is effectively how these licenses and bundled and sold. **Installing Licenses** To install licenses you must be running as a root user. There are two options for installing licenses manually. Examples for each one are below. * Manual License File install (i.e. using the '-i ' argument). This can be used to install a license binary file (ending with the .bin extension). * Automatic License File install (i.e. using the '-i auto' argument). For DRAGEN servers which are connected to the internet, this will instruct the server to reach out to Illumina's License Server and download and automatically install the latest license file provided by Illumina. This is recommended method for installing a license. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/tools-and-utilities#dragen_licd-background-daemon) dragen\_licd background daemon For DRAGEN servers which are connected to the internet, this background process self-activates daily to automatically retrieve and install the latest license files associated with this server from Illumina's License Server. As part of this process, any unreported usage data is also uploaded. [PreviousDRAGEN Reportschevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reports) [NextDRAGEN Serverchevron-right](https://help.dragen.illumina.com/dragen-v4.3/reference/platform-maintenance) Last updated 7 months ago Was this helpful? * [Monitoring System Health](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/tools-and-utilities#monitoring-system-health) * [Hardware-Accelerated Compression and Decompression](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/tools-and-utilities#hardware-accelerated-compression-and-decompression) * [Licensing and Usage](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/tools-and-utilities#licensing-and-usage) Was this helpful? Copy ********************************************************** **  Received external signal -- aborting dragen.        ** **  An issue has been detected with the dragen card.    ** **  Check /var/log/messages for details.                ** **                                                      ** **  It may take up to a minute to complete shutdown.    ** ********************************************************** Copy dzip file1      \# produces output file file1.gz\ dunzip file2.gz \# produces output file file2 Copy $ dragen_lic LICENSE_MSG| ---- Board (SN: ) ---- LICENSE_MSG| License Genome : used 68.0/100250 Gbases since 2023-Dec-05 (67961825900 bases, 0.1%) LICENSE_MSG| issued=2023-Dec-04, start=2023-Dec-01, expiry=2023-Dec-19, period=15 months LICENSE_MSG| License JointGenotype : used 0.0 Grecords since around 2023-Dec-04 (0 records, unlimited) LICENSE_MSG| issued=2023-Dec-04, start=2023-Dec-01, expiry=2023-Dec-19, period=15 months LICENSE_MSG| License Transcriptome : used 0.0 Gbases since around 2023-Dec-04 (0 bases, unlimited) LICENSE_MSG| issued=2023-Dec-04, start=2023-Dec-01, expiry=2023-Dec-19, period=15 months LICENSE_MSG| License GATK-accelerated: used 0.0 Gbases since around 2023-Nov-06 (0 bases, unlimited) LICENSE_MSG| issued=2023-Nov-06, start=2021-Oct-13, expiry=2023-Dec-13, period=1 month LICENSE_MSG| License Somatic : used 0.0 Gbases since 2023-Dec-04 (0 bases, unlimited) LICENSE_MSG| issued=2023-Dec-04, start=2023-Dec-01, expiry=2023-Dec-19, period=15 months LICENSE_MSG| License CNV : used 0.0 Grecords since around 2023-Dec-04 (0 records, unlimited) LICENSE_MSG| issued=2023-Dec-04, start=2023-Dec-01, expiry=2023-Dec-19, period=15 months LICENSE_MSG| License TSO500 : used 0.0 Gbases since around 2023-Nov-06 (0 bases, unlimited) LICENSE_MSG| issued=2023-Nov-06, start=2021-Oct-13, expiry=2023-Dec-13, period=1 month LICENSE_MSG| License TSO500Solid : used 24.0 Gbases since around 2023-Nov-06 (24000000000 bases, unlimited) LICENSE_MSG| issued=2023-Nov-06, start=2021-Oct-13, expiry=2023-Dec-13, period=1 month LICENSE_MSG| License Compression : used 0.0/100250 Gbases since 2023-Dec-04 (0 bases, 0.0%) LICENSE_MSG| issued=2023-Dec-04, start=2023-Dec-01, expiry=2023-Dec-19, period=15 months LICENSE_MSG| License TSO500_HRD : used 0.0 Gbases since around 2023-Nov-06 (0 bases, unlimited) LICENSE_MSG| issued=2023-Nov-06, start=2021-Oct-13, expiry=2023-Dec-13, period=1 month LICENSE_MSG| License TSO500Combined : used 23518260.0 Gbases since around 2022-Jul-15 (23518260000000000 bases, unlimited) LICENSE_MSG| issued=2022-Jul-15, start=2022-Jul-13, expiry=2050-Jan-01, period=12 months LICENSE_MSG| -- License dongle LICENSE_MSG| STATUS : OK LICENSE_MSG| DONGLE SN: LICENSE_MSG| RELEASE : LICENSE_MSG| CHIPID : * LICENSE_MSG| DNA: active, accelerators=DNA LICENSE_MSG| issue=2023-Dec-04, start=2021-Oct-13, expiry=2050-Jan-01 LICENSE_MSG| RNA: active, accelerators=RNA LICENSE_MSG| issue=2023-Dec-04, start=2021-Oct-13, expiry=2050-Jan-01 LICENSE_MSG| GZIP: active, accelerators=GZIP LICENSE_MSG| issue=2023-Dec-04, start=2021-Oct-13, expiry=2050-Jan-01 LICENSE_MSG| GUNZ: active, accelerators=GUNZ LICENSE_MSG| issue=2023-Dec-04, start=2021-Oct-13, expiry=2050-Jan-01 LICENSE_MSG| HMM: active, accelerators=HMM LICENSE_MSG| issue=2023-Dec-04, start=2021-Oct-13, expiry=2050-Jan-01 LICENSE_MSG| SMW: active, accelerators=SMW LICENSE_MSG| issue=2023-Dec-04, start=2021-Oct-13, expiry=2050-Jan-01 LICENSE_MSG| RANS: active, accelerators=RANS LICENSE_MSG| issue=2023-Dec-04, start=2021-Oct-13, expiry=2050-Jan-01 LICENSE_MSG| GRAPH: active, accelerators=GRAPH LICENSE_MSG| issue=2023-Dec-04, start=2021-Oct-13, expiry=2050-Jan-01 LICENSE_MSG| DeepRTA: active, accelerators=DeepRTA LICENSE_MSG| issue=2023-Dec-04, start=2021-Oct-13, expiry=2050-Jan-01 Copy $ dragen_lic -b ---- Board (SN: ) ---- DRAGEN Core: Status: Active Used: 68.0 Gbases since 2023-Dec-05 Quota: 100250 Gbases Expiry: 2024-Feb-13 Includes; Genome, JointGenotype, CNV, Somatic, Transcriptome License(s) Compression: Status: Active - !!! EXPIRING IN LESS THAN 30 DAYS !!! Used: 0 Gbases since 2024-Jan-12 Quota: Unlimited Expiry: 2024-Feb-13 TSO500Combined: Status: Active Used: 23518284 Gbases since around 2022-Jul-15 Quota: Unlimited Expiry: 2050-Jan-01 TSO500_HRD: Status: Active - !!! EXPIRING IN LESS THAN 30 DAYS !!! Used: 0 Gbases since around 2024-Jan-07 Quota: Unlimited Expiry: 2024-Feb-13 Generated on 2024-01-16 18:54 Copy $ dragen_lic -j {"dragen_boards":[{"board_sn":"","installed_licenses":{"Compression":{"expiry":"2025-Mar-19","quota_limit":100250,"remaining":95027,"start":"2023-Dec-01","status":"Active","units":"Gbases","used":5223},"TSO500Combined":{"expiry":"2050-Jan-01","quota_limit":-1,"start":"2022-Jul-13","status":"Active","units":"Gbases","used":23518284},"TSO500_HRD":{"expiry":"2024-Feb-13","quota_limit":-1,"start":"2021-Oct-13","status":"Active","units":"Gbases","used":0},"dragen_core":{"expiry":"2024-Feb-13","includes":{"CNV":{"expiration":"2025-Mar-19","status":"Active"},"Genome":{"expiration":"2024-Feb-13","status":"Active"},"JointGenotype":{"expiration":"2025-Mar-19","status":"Active"},"Somatic":{"expiration":"2025-Mar-19","status":"Active"},"Transcriptome":{"expiration":"2025-Mar-19","status":"Active"}},"quota_limit":-1,"start":"2023-Oct-14","status":"Active","units":"Gbases","used":79708}}}],"generated":1705098370} Copy sudo /usr/bin/dragen_lic -i /tmp/new_license.bin Copy sudo /usr/bin/dragen_lic -i auto --- # Sorting and Duplicate Marking | DRAGEN v4.3 | DRAGEN ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/sort-dupmark#sorting) Sorting The map/align system produces a BAM file sorted by reference sequence and position by default. Creating this BAM file typically eliminates the requirement to run samtools sort or any equivalent postprocessing command. The `‑‑enable-sort option` can be used to enable or disable creation of the BAM file, as follows: * To enable, set to true. * To disable, set to false. On the reference hardware system, running with sort enabled increases run time for a 30x full genome by about 6--7 minutes. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/sort-dupmark#duplicate-marking) Duplicate Marking Marking or removing duplicate aligned reads is a common best practice in whole-genome sequencing. Not doing so can bias variant calling and lead to incorrect results. The DRAGEN system can mark or remove duplicate reads, and produces a BAM file with duplicates marked in the FLAG field, or with duplicates entirely removed. In testing, enabling duplicate marking adds minimal run time over and above the time required to produce the sorted BAM file. The additional time is approximately 1--2 minutes for a 30x whole human genome, which is a huge improvement over the long run times of open source tools. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/sort-dupmark#the-duplicate-marking-algorithm) The Duplicate Marking Algorithm The DRAGEN duplicate-marking algorithm is modeled on the Picard toolkit's MarkDuplicates feature. All the aligned reads are grouped into subsets in which all the members of each subset are potential duplicates. For two pairs to be duplicates, they must have the following: * Identical alignment coordinates (position adjusted for soft- or hard-clips from the CIGAR) at both ends. * Identical orientations (direction of the two ends, with the left-most coordinate being first). In addition, an unpaired read may be marked as a duplicate if it has identical coordinate and orientation with either end of any other read, whether paired or not. Unmapped read pairs are never marked as duplicates. When DRAGEN has identified a group of duplicates, it picks one as the best of the group, and marks the others with the BAM duplicate flag (0x400, or decimal 1024). For this comparison, duplicates are scored based on the average sequence Phred quality. Pairs receive the sum of the scores of both ends, while unpaired reads get the score of the one mapped end. The idea of this score is to try, all other things being equal, to preserve the reads with the highest-quality base calls. If two reads (or pairs) have exactly matching quality scores, DRAGEN breaks the tie by choosing the pair with the higher alignment score. If there are multiple pairs that also tie on this attribute, then DRAGEN chooses a winner arbitrarily. The score for an unpaired read R is the average Phred quality score per base, calculated as follows: ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3660538983-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FpFn0gLe5V859SXejQNAW%252Fuploads%252Fgit-blob-615795568e95f46778a5951ee84fc03732f60fb5%252Fsort-dupmark.ScoreEquation.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=112871d7&sv=2) Where R is a BAM record, QUAL is its array of Phred quality scores, and dedup-min-qual is a DRAGEN configuration option with default value of 15. For a pair, the score is the sum of the scores for the two ends. This score is stored as a one-byte number, with values rounded down to the nearest one-quarter. This rounding may lead to different duplicate marks from those chosen by Picard, but because the reads were very close in quality this has negligible impact on variant calling results. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/sort-dupmark#duplicate-marking-limitations) Duplicate Marking Limitations The limitations to DRAGEN duplicate marking implementation are as follows: * When there are two duplicate reads or pairs with very close Phred sequence quality scores, DRAGEN might choose a different winner from that chosen by Picard. These differences have negligible impact on variant calling results. * If using a single FASTQ file as input, DRAGEN accepts only a single library ID as a command-line argument (RGLB). For this reason, the FASTQ inputs to the system must be already separated by library ID. Library ID cannot be used as a criterion for distinguishing non-duplicates. * DRAGEN does not distinguish between optical and PCR duplicates. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/sort-dupmark#duplicate-marking-settings) Duplicate Marking Settings The following options can be used to configure duplicate marking in DRAGEN: * `--enable-duplicate-marking` Set to true to enable duplicate marking. When `\--enable-duplicate-marking is enabled`, the output is sorted, regardless of the value of the enable-sort option. * `--remove-duplicates` Set to true to suppress the output of duplicate records. If set to false, set the 0x400 flag in the FLAG field of duplicate BAM records. When --remove-duplicates is enabled, then enable- duplicate-marking is forced to enabled as well. * `--dedup-min-qual` Specifies the Phred quality score below which a base should be excluded from the quality score calculation used for choosing among duplicate reads. [PreviousDRAGEN FASTQCchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dragen-fastqc) [NextSmall Variant Callingchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/small-variant-calling) Last updated 7 months ago Was this helpful? * [Sorting](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/sort-dupmark#sorting) * [Duplicate Marking](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/sort-dupmark#duplicate-marking) Was this helpful? --- # Filter Duplicate Variants | DRAGEN v4.3 | DRAGEN DRAGEN can find and remove variants that are common to separate VCF files. DRAGEN supports the following modes: * **Small indel deduplication**—If using a structural variant VCF and a small variant VCF, DRAGEN filters all small indels in the structural variant VCF that appear and are passing in the small variant VCF (`PASS` in the `FILTER` column of the small variant VCF file). Using this feature, DRAGEN will create a new VCF (without changing SV and SNV VCF files) that contains variants in SV VCF that are not matching a variant from SNV VCF file. The new deduplicated SV VCF file will have the same prefix passed by `--output-file-prefix` followed by `sv.small_indel_dedup.vcf.gz` as suffix. The diagram below describes the small indel deduplication pipeline. You must provide a reference genome to generate the VCF files to normalize the variants. DRAGEN normalizes variants by trimming and left shifting by up to 500 bases. An instance of utilizing this feature is when incorporating both SV and SNV callers in somatic workflows, which can increase sensitivity and prevent the occurrence of replicated variants within genes such as FLT3 and KMT2A. ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3660538983-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FpFn0gLe5V859SXejQNAW%252Fuploads%252Fgit-blob-a03e882eac99831c85a6bc617f4b1b4a305f03bf%252Fvariant_merger.svg%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=6a8f077&sv=2) image * **SMN deduplication**—If using a small variant VCF and a DRAGEN-STR repeats VCF, DRAGEN filters any lines in the small variant VCF that have the same chromosome and position as lines in the repeats VCF with the INFO tag `VARID=SMN`. A reference genome is not required. Use the following command line options to input VCF or gVCF files. The input files are not altered. * `vd-sv-vcf`—Specify a structural variant VCF or gVCF. * `vd-small-variant-vcf`—Specify a small variant VCF or gVCF. * `vd-eh-vcf`—Specify a DRAGEN-STR repeats VCF or gVCF. DRAGEN determines the name and type of the output file as follows. Component Description Output prefix If a value is specified for `output-file-prefix`, the prefix is used as usual. If the value is not valid, the name of the filtered input is used as the prefix. Deduplication mode The prefix is followed by `.small_indel_dedup` or `.smn_dedup` depending on the deduplication mode used. File type The output file type matches the input file type (VCF or gVCF). If `enable-vcf-compression` is set to `true`, the output file is gzip compressed, regardless of if the input file was compressed. The name of the match log is either `match_log.smn_dedup.txt` or `match_log.small_indel_dedup.txt` depending on which deduplication mode you use. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/variant-deduplication#command-line-options) Command-Line Options -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- You can use the following command line options for variant deduplication. Option Description `enable-variant-deduplication` To enable variant deduplication, set to `true`. The default is `false`. `enable-vcf-indexing` To generate tabix index files, set to 'true'. The default is 'true'. `vd-output-match-log` To log matching lines to a text file, set to true. The default is false. For each match, the two matching lines follow each other, then by a new line. The following is an example command for an SMN deduplication standalone run: You can also run small indel deduplication automatically on outputs from the DRAGEN joint caller where both structural variant and small variant callers are enabled. To run small indel deduplication automatically, set `enable-variant-deduplication` to `true`, and make sure the `vd-sv-vcf`, `vd-small-indel-vcf`, and `vd-eh-vcf` input options are not set. Only small indel deduplication can be run automatically. The following is an example command for an automatic small indel deduplication run. [PreviousVNTR Callingchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling) [NextPloidy Callingchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/ploidy-calling) Last updated 7 months ago Was this helpful? Was this helpful? Copy dragen --enable-map-align false \ --enable-variant-deduplication true \ --vd-small-variant-vcf \ --vd-eh-vcf \ --output-directory /tmp/ \ --vd-output-match-log true \ Copy dragen \ --ref-dir --output-directory \ --output-file-prefix \ -b \ --enable-map-align false \ --enable-variant-caller true \ --enable-sv true \ --enable-variant-deduplication true \ --vd-output-match-log true \ --- # Downsampling | DRAGEN v4.3 | DRAGEN [Fractional (Raw Reads) Downsamplingchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/downsampling/fractional-downsampler) [Effective Coverage Downsamplingchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/downsampling/downsampling) [PreviousDRAGEN Fragmentomicschevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/biomarkers/biomarker-fragmentomics) [NextFractional (Raw Reads) Downsamplingchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/downsampling/fractional-downsampler) Last updated 7 months ago Was this helpful? Was this helpful? --- # ORA Compression | DRAGEN v4.3 | DRAGEN DRAGEN ORA Compression is a fully lossless compression, that compresses \*.fastq and \*.fastq.gz files into \*.fastq.ora files. DRAGEN ORA supports FASTQ generated by Illumina sequencing systems. When using the ORA format, the md5 checksum of the FASTQ content is preserved after a compression and decompression cycle to ensure a lossless compression. DRAGEN ORA Compression requires a separate license. Decompression and ingestion of \*.fastq.ora files into the DRAGEN map/align does not require a license. If your DRAGEN server is connected to a network, DRAGEN ORA compression can be used after installing DRAGEN v3.8 or later. If your DRAGEN server is offline, contact Illumina Customer Service. For human data generated by the NovaSeq 6000, NextSeq 1000, or NextSeq 2000 sequencing systems, the compression ratio is expected to be up to 6x compared to the \*.fastq.gz. The compressed file uses the \*.fastq.ora extension. Input of DRAGEN ORA Compression is \*.fastq or \*.fastq.gz. Input can be a single file or a list of files. A list of files can be specified on the command line, or from a \*.fastq-list.csv generated by the BCL Convert BaseSpace Sequence Hub App or DRAGEN BCL convert. Input located in local storage, AWS S3 or Azure Blob storage is supported. \*.fastq.ora files are decompressed into \*.fastq.gz. Note: \*.fastq.ora can be generated starting from BCL. To convert BCL into \*.fastq.ora, specific commands need to be used. Follow the [DRAGEN ORA compression from BCL](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/bcl-conversion#dragen-ora-compression-from-bcl) instructions. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/ora-compression#ora-reference) ORA Reference ---------------------------------------------------------------------------------------------------------------------------------- To compress or decompress ORA files, you must provide the ORA reference files and specify an ORA reference directory. Several references to compress data from different species and from different type of human data are supported. Refer to the list of supported references below. You can download ORA reference files from the [DRAGEN Software Support Site pagearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) . To ensure proper management of the reference files, do not change any of the file names of the downloaded archive. To specify an ORA reference directory, do as follows. 1. Download the `oradata-2.tar.gz` (or archive relevant to your studied model) from the DRAGEN Software Support Site. 2. Move the file to the location you would like to contain the reference directory in, and then enter the following to extract the contents. `tar -xzvf oradata-2.tar.gz` 3. Set the `--ora-reference` command line option to the extracted `/oradata` folder path. The oradata folder should follow the following structures: When only one reference is handled: `--ora-reference` should still point to the parent oradata folder. When one ore more references are handled: You can select at compression which reference species to use with option `--ora-compression-species `. If unspecified, Homo sapiens reference will be used by default. Using a reference species that does not match the organism sequenced in your FASTQ file will still produce valid ORA compressed file, albeit with lower compression ratio. If the oradata folder pointed by `--ora-reference` does not contain the requested species, DRAGEN will stop with error. At decompression, detection of the species used to compress the ORA file is automatic. DRAGEN will look for the appropriate species in the oradata folder pointed by `--ora-reference`. If it is missing, DRAGEN will stop with an error message indicating the name of the missing species. In that case download it from the [DRAGEN Software Support Site pagearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) . [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/ora-compression#command-line-options) Command Line Options ------------------------------------------------------------------------------------------------------------------------------------------------ The following example command contains the required DRAGEN ORA compression options to compress regular human data: `dragen --enable-map-align false --ora-input --enable-ora true --ora-reference <...> --output-directory <...>` or `dragen --enable-map-align false --fastq-list --enable-ora true --ora-reference <...> --output-directory <...>` The following example command contains the required DRAGEN ORA decompression options (for human and non-human data): `dragen --enable-map-align false --ora-input --enable-ora true --ora-decompress true --ora-reference <...> --output-directory <...>` The following examples command contains the required options to compress FASTQs of a fastq-list.csv file containing multiple samples (regular human data): When all samples must be compressed: `dragen --enable-map-align false --fastq-list --enable-ora true --fastq-list-all-samples true --ora-reference <...> --output-directory <...>` When only specific samples must be compressed: `dragen --enable-map-align false --fastq-list --enable-ora true --fastq-list-sample-id --ora-reference <...> --output-directory <...>` The following examples command contains the required options to achieve an interleaved compression of paired-read files from a fastq-list.csv file (regular human data) : `dragen --enable-map-align false --fastq-list --enable-ora true --ora-interleaved-compression true --ora-reference <...> --output-directory <...>` The following example command contains the required DRAGEN ORA compression options to compress non-human or specific human data, chicken data in this case: `dragen --enable-map-align false --ora-input --enable-ora true --ora-compression-species --ora-reference <...> --output-directory <...>` The following example command prints the file information summary of an ORA compressed file. Compression or decompression is not performed. `dragen --enable-map-align false --ora-input --enable-ora=true --ora-print-file-info=true` The following example command compares FASTQ file checksum and decompressed FASTQ.ORA file checksum and outputs "ORA integrity check successful" if both checksums are equal or "integrity check failed" if checksums are not equal. `dragen --enable-map-align false --ora-input --enable-ora=true --ora-reference <...> --ora-check-file-integrity=true` The following example command contains the required DRAGEN ORA compression options to print the list of available references: `dragen --enable-map-align false --enable-ora true --ora-list-species true` The following are the command line options for running DRAGEN ORA Compression and Decompression. Option Required Description \--enable-map-align Yes Set to `false`to perform compression only. In this case, only the compression license gets deducted. Set to `true` to perform the compression in parallel of the map/align step. In this case, the compression license AND the DRAGEN license get deducted. When set to `true` all the options required to process with the map/align step must be provided. \--enable-ora Yes Set to `true` to enable FASTQ file compression and decompression. Decompression must be enabled using the `--ora-decompress` option. \--ora-reference Yes Path to the directory that contains the compression reference and index file. \--ora-input Yes (or --fastq-list) Specifies the input files for compression or decompression. \--fastq-list Yes (or --ora-input) Specifies a .csv file with list of FASTQ files to be compressed. This option is not specific to the DRAGEN ora compression and the usage is explained in the FASTQ CSV File Format Section of this manual. Compression of a list of FASTQ containing different species is not supported while decompression of FASTQ containing different species is supported. \--ora-input2 No Used for interleaved compression of paired-read files when input files are specified with `--ora-input`. Specify the paired-read files corresponding to files secified in `--ora-input` to achieve paired-read file compression into one single interleaved file. The number of files and the order of paired-read files in `--ora-input` and `--ora-input2` should match. \--ora-interleaved-compression No Used for interleaved compression of paired-read files when input files are specified with `--fastq-list`. Set to `true` to enable paired-read file compression into one single interleaved file. Each line of the fastq-list.csv file is the two corresponding paired-read files with same count of reads. \--ora-compression-species No Sring to specify the reference species to compress data on. Possible values `` as listed in the list of references supported below or `homo_sapiens_bisulfite`. If not used, default compresses on regular human reference. \--ora-decompress No Set to `true` to enable decompress mode. The default value is false. Note: fastq.ora files compressed with non-human or specific human references cannot be decompressed on DRAGEN versions older that v4.3. \--force No Compresses to output directory even if the compressed file already exists. The existing compressed file is overwritten. \--ora-threads-per-file <#> No Manually controls the number of CPU threads for compressing each FASTQ input file. The default value is 8. \--ora-parallel-files <#> No Manually controls the number of input FASTQ files processed in parallel. The default value is 4. \--ora-print-file-info No Set to `true` to print file information summary of ORA compressed files. Requires ORA file as input. Note: this option cannot be used simultaneously with the --ora-decompress option and the --ora-check-file-integrity option. \--ora-list-species No Set to `true` to print the list of supported references. Note: the printed list may not be exhaustive, if you don't find your species check the most up-to-date list in the [DRAGEN Software Support Site pagearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) . \--ora-check-file-integrity No Set to `true`to perform and output result of FASTQ file and decompressed FASTQ.ORA integrity check. The default value is false. Note: this option cannot be performed in the same command line than the compression itself as it requires fastq.ora format for the --ora-input argument. \--ora-enable-md5 No Set to `true` to compute md5 checksum of fastq.ora files during the compression and generate an ora.md5sum file with md5 checksum printed. \--ora-delete-input-files No Set to `true` to automatically delete the input FASTQ file from the disk upon completion of compression \--ora-original-name No At decompression, set to `true` to retrieve the name of the original FASTQ before compression. Default re-uses the name of the FASTQ.ORA provided as input. Use the `--output-directory` option to specify the directory to store output compressed/decompressed files. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/ora-compression#interleaved-compression) Interleaved Compression ------------------------------------------------------------------------------------------------------------------------------------------------------ There are two methods to achieve a paired compression aka interleaved compression: * when using `--ora-input` and `--ora-input2`. The nth file of the `--ora-input` list is compressed together with the nth file of the `--ora-input2` * when using `--fastq-list`and `--ora-interleaved-compression` set to `true`. The paired-read files from the nth line of fast-list.csv are compressed together Both files are interleaved within a single ORA output file with file name containing `-interleaved`. Using these options to compress paired files together improves compression by up to 10%. If decompressing an ORA file that contains paired data, the file is automatically decompressed to two separate files. To map an ORA file that contains paired interleaved data with the DRAGEN mapper, use the `--interleaved` option. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/ora-compression#how-to-use-ora-input-files-with-dragen-map-align) How to use ORA input files with DRAGEN Map/Align -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- DRAGEN can directly process ORA files. The same options as the other FASTQ input file types can be used. To use the ORA file, replace the FASTQ file name with the ORA file name and specify the ORA reference directory using `--ora-reference`. The following command represents paired-end in two matched ORA FASTQ files (-1 and -2 options). [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/ora-compression#list-of-supported-references) List of supported references ---------------------------------------------------------------------------------------------------------------------------------------------------------------- Below is a list of supported references. This list may not be exhaustive, the most up-to-date list of supported references can be found on the [DRAGEN Software Support Site pagearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) . Either the whole database or specific references can be dowloaded. Model Valid string value Size Human Homo\_sapiens 6.5 GB Human methylated data Homo\_sapiens\_bisulfite 11 GB Pig Sus\_scrofa 5.0 GB Chicken Gallus\_gallus 3.8 GB Rice Oryza\_sativa 1.9 GB Arabidopsis Arabidopsis\_thaliana 478 MB Wheat Triticum\_aestivum 13 GB Cattle Bos\_taurus 5.3 GB Soybean Glycine\_max 2.0 GB Rat Rattus\_norvegicus 4.5 GB Maize Zea\_mays 4.2 GB Zebrafish Danio\_rerio 4.8 GB Mouse Mus\_musculus 4.5 GB Roundworm Caenorhabditis\_elegans 569 MB [PreviousIllumina Connected Annotationschevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/nirvana) [NextCommand Line Optionschevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/command-line-options) Last updated 7 months ago Was this helpful? * [ORA Reference](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/ora-compression#ora-reference) * [Command Line Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/ora-compression#command-line-options) * [Interleaved Compression](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/ora-compression#interleaved-compression) * [How to use ORA input files with DRAGEN Map/Align](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/ora-compression#how-to-use-ora-input-files-with-dragen-map-align) * [List of supported references](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/ora-compression#list-of-supported-references) Was this helpful? Copy oradata ├── lena_index_V2 └── refbin Copy oradata ├── homo_sapiens_bisulfite │ ├── lena_index_V2 │ └── refbin ├── gallus_gallus │ ├── lena_index_V2 │ └── refbin └── mus_musculus ├── lena_index_V2 └── refbin Copy dragen -r -1 -2 \ --ora-reference \ --output-directory --output-file-prefix \ --RGID --RGSM --- # Biomarkers | DRAGEN v4.3 | DRAGEN [Tumor Mutational Burdenchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/biomarkers/biomarker-tmb) [Microsatellite Instabilitychevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/biomarkers/biomarker-msi) [Homologous Recombination Deficiencychevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/biomarkers/biomarker-hrd) [BRCA Large Genomic Rearrangmentchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/biomarkers/biomarker-brca-large-rearrangement) [DRAGEN Fragmentomicschevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/biomarkers/biomarker-fragmentomics) [PreviousHLA Typingchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing) [NextTumor Mutational Burdenchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/biomarkers/biomarker-tmb) Last updated 7 months ago Was this helpful? Was this helpful? --- # Indel Re-aligner (Beta) | DRAGEN v4.3 | DRAGEN [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/indel-realigner#use-disclaimer) Use Disclaimer -------------------------------------------------------------------------------------------------------------------------------------------------------- Indel Re-Aligner is a beta feature. Beta features are provided AS-IS with limited testing and are used at the user’s sole risk. Illumina is not responsible for any loss of data, incorrect results, failures, costs, liabilities, or damages that may result from the use of beta features. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/indel-realigner#overview) Overview -------------------------------------------------------------------------------------------------------------------------------------------- The DRAGEN Indel Re-aligner is a consensus based re-alignment step, independent from other DRAGEN callers and pipelines. Re-aligned reads are reflected in the output bam file, and their original alignment is described in an OA tag. The implementation is similair to the Indel Re-aligner tool that was found in GATK3. The tool is designed to reduce false positive SNP's by considering evidence of near-by indels. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/indel-realigner#description) Description -------------------------------------------------------------------------------------------------------------------------------------------------- The pipeline is comprised of two concurent steps: Interval creation and re-alignment. The interval creation step identifies genomic intervals for which there is evidence of insertions or deletions in the CIGAR's of properly paired (if paired) reads aligned with positive mapq. To output these intervals as a text file, use the command line argument `--ir-write-intervals-file=true`. Each line will describe a genomic interval as chrom:start-end, or chrom:start for intervals of length one. The start and end positions are both inclusive and 1-based. The intervals file will be written to the DRAGEN output directory, with the suffix `realign-intervals.txt` For each genomic interval, the realigment step groups all aligned reads that intersect the interval. If there are more than `ir-max-num-reads` reads that intersect the interval, it is skipped. The following reads are then discarded from the re-alignment analysis: * Non-primary aligned reads. * Reads whose mapping quality is zero. * Paired end reads that mapped to different contigs. * Paired end reads that mapped to the same contig with start positions more than `ir-max-distance-between-mates` apart. Reads that have not been skipped are candidates for re-alignment. If there are more than `ir-max-num-candidates` candidates, the interval is skipped. From each re-alignment candidate, a consensus read is generated from any read that has a single indel that is not the first or last CIGAR operation excluding clip operations. If there are more than `ir-max-number-consensus` consensus reads, the interval is skipped. Each re-alignment candidate is then scored against each consensus to determine the winning consensus. If the combined score for the interval against the winning consensus is better than the score against the reference by a differnce of at least `ir-realignment-threshold`, the reads start position, CIGAR, and NM tag are updated to reflect the re-alignment. The scoring used is hamming distance weighted by base qualities. OA tags that describe the original alignment are added to any re-aligned reads. Mate positions of reads whose mate was re-aligned are updated as well. When the re-alignment step is complete, a summary will be printed to standard out. It will describe the number of intervals found, sum of the lengths of all intevals, number of reads that intersected intervals, number of reads that got re-aligned, and the number of reads that were skipped due to memory constraints. Such reads will be documented in the DRAGEN log. This may happen in regions with very deep coverage. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/indel-realigner#limitations) Limitations -------------------------------------------------------------------------------------------------------------------------------------------------- DRAGEN Indel Re-aligner is an experimental optional feature. It is not enabled by default and Illumina does not recommend enabling Indel Re-alignment with the DRAGEN VC. Enabling Indel Re-alignment will slow down a DRAGEN Map/Align + VC run roughly by a factor of two. The DRAGEN Indel Re-aligner is designed to improve the quality of the DRAGEN BAM output for downstream analysis. The DRAGEN small variant caller pipeline does not read the output BAM, and has its own internal haplotype assembly step which will usually recover most of the artifacts found during Indel Re-alignment. Limited testing indicates that there may be a small improvement in DRAGEN small variant calls when Indel Re-alignment is enabled. The Indel Re-alignment pipeline cannot run with: * The UMI pipeline. * The Methylation pipelines. * `--qc-coverage-ignore-overlaps=true`. * SA tag generation (`--generate-sa-tags=true`). * The Expansion Hunter pipeline. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/indel-realigner#command-line-options) Command Line Options -------------------------------------------------------------------------------------------------------------------------------------------------------------------- Name Description Default Value `enable-indel-realigner` Enable indel re-alignment False `ir-write-intervals-file` Output a file with the reference intervals that contain evidence for re-alignment. False `ir-max-num-reads` Max number of reads in an interval for re-alignment. 20,000 `ir-max-num-candidates` Max number of re-alignment candidates in an interval for re-alignment. 256 `ir-max-num-consensus` Max number of consenses reads in an interval for re-alignment. 256 `ir-max-distance-between-mates` Max number of re-alignment candidates in an interval for re-alignment. 100,000 `ir-realignment-threshold` Minimal improvement of sum of mismatching base qualities to merit realignment. 50 [PreviousUnique Molecular Identifierschevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/unique-molecular-identifiers) [NextStar Allele Callerchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller) Last updated 4 months ago Was this helpful? * [Use Disclaimer](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/indel-realigner#use-disclaimer) * [Overview](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/indel-realigner#overview) * [Description](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/indel-realigner#description) * [Limitations](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/indel-realigner#limitations) * [Command Line Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/indel-realigner#command-line-options) Was this helpful? --- # DRAGEN Apps | DRAGEN [Clinical Research Workflowschevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-apps/dragen-apps) [DRAGEN Germline Enrichment ICA Appchevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-apps/dragen-germline-enrichment-ica-app) [DRAGEN Enrichment BSSH Appchevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-apps/dragen-enrichment-bssh-app) [DRAGEN Germline Enrichment from BCLs BSSH Appchevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-apps/dragen-germline-enrichment-from-bcls-bssh-app) [PreviousDRAGEN Secondary Analysischevron-left](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-host-software/dragen-platform) [NextClinical Research Workflowschevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-apps/dragen-apps) Last updated 20 days ago Was this helpful? Was this helpful? --- # DRAGEN Reports | DRAGEN v4.3 | DRAGEN DRAGEN Reports is a docker image that provides tools for generating rich, interactive and self-contained HTML reports from DRAGEN's output files. These reports combine data from QC, trimming, mapping, variant and other DRAGEN modules to create a comprehensive summary of a multi-sample workflow, as well as more detailed reports for individual samples. The DRAGEN Reports rpm packages may be downloaded from the [Dragen Product Files Support Sitearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) . [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reports#requirements) Requirements ------------------------------------------------------------------------------------------------------------------------------- The DRAGEN Reports tools are provided as a Docker image. To use it, you will need the following: * An x64 processor * Docker installed and running In addition, while DRAGEN Reports can accept files output by any DRAGEN process (such as from a local DRAGEN box, DRAGEN equipped sequencing instrument, or the Cloud), it can only work with local files. For how to access your data and transfer it to your local environment, please refer to the user documentation for the appropriate service or instrument. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reports#dragen-reports-components) DRAGEN Reports Components --------------------------------------------------------------------------------------------------------------------------------------------------------- Within the DRAGEN Reports docker image are multiple executables for generating different types of report files * **dragen-reports** - Generates reports for a single DRAGEN workflow or pipeline and it's samples * **dragen-summary-reports** - Generate reports for a NovaSeqX run, containing multiple containing multiple DRAGEN workflows [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reports#dragen-reports) Dragen-reports ----------------------------------------------------------------------------------------------------------------------------------- The **dragen-reports** tool produces both workflow- and sample-level reports. A workflow-level report combines data from multiple samples analyzed by DRAGEN in the same way in order to faciliate comparssions across a batch of samples. Users can navigate to a report for a specific sample by clicking on the link in the Sample column of most workflow report tables. ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3660538983-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FpFn0gLe5V859SXejQNAW%252Fuploads%252Fgit-blob-df8cab881592f26539b52e910787a812a019055d%252Fdragen-reports.pipeline.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=f696ab5d&sv=2) Sample-level reports are also generated to allowed for more detailed analysis of individual samples. For example, under the DRAGEN-FastQC tab in a workflow report we are only able to plot the mean base quality, while for individual samples we can display a box-and-whisker plot for each position, as shown below. ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3660538983-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FpFn0gLe5V859SXejQNAW%252Fuploads%252Fgit-blob-24c2693c9daa2d47c8e6ce9ce3060c61efd37973%252Fdragen-reports.sample.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=5e4fca2a&sv=2) To navigate back from a sample report to the multi-sample summary, users can click on the "Back to Pipeline" button located in the upper-left corner of the report. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reports#options) Options The following command-line options are available from DRAGEN Reports Option Argument Description \-V, --version Output the version string of the DRAGEN-Reports executable \-v, --verbose Emit additional debug information whiile generating the report \-m, --manifest Filepath Specify a manifest file describing the report to be written \-n, --run-name String Manually set the run-name in the output report \-d, --directory Comma-separated list DRAGEN output directories to generate a report from \-o, --output Filepath Generate output report to the specified location \-s, --samples Comma-separated list Generate only individual reports for the specified sample(s) \-S, --sample-output Filepath Generate sample report to the specified location \-f, --force Force overwriting of any pre-existing report files \-T, --timestamp String Manually set the timestamps in the output reports \-h, --help Display this help command In addition, the following Docker options are recommended when calling DRAGEN Reports via Docker: Option Argument Description \-v, --volume String Bind mount a volume \--rm Automatically remove the container when it exits \--platform String Set platform if server is multi-platform capable ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reports#manifests) Manifests The **dragen-reports** tool can output reports with different contents for different DRAGEN workflows, tailoring the resulting reports to create only the most relevant plots and tables. To utilize this feature, users can use the _\-m / --manifest_ option to specify a _Manifest JSON file_ to specify the desired output reports. These files function as recipes for different reports - specifying which files to look for, which data to process, and which plots to draw. These files are all available in the DRAGEN Reports docker under the **/opt/dragen-reports/manifests** directory. Manifest Name Pipeline Description germline\_wgs.json Germline Report DragenGermline Default manifest targetting WGS workflows and supporting most DRAGEN variant callers somatic\_wgs.json Somatic Report DragenSomatic Manifest for somatic tumor-only analysis germline\_enrichment.json Enrichment Report DragenEnrichment Manifest for whole exome or germline gene panel analysis somatic\_enrichment.json Enrichment Report DragenEnrichment Manifest for somatic exomes or somatic gene panel analysis rna.json RNA Report DragenRna Manifest for RNA-Seq & RNA Quantification workflows methylation.json Methylation Report DragenMethylation Manifest for the DRAGEN Methylation workflow demux.json Demultiplex Report Demux Manifest for DRAGEN demultiplex processes bcl\_convert.json BCL Convert Report BCLConvert Manifest for BCLConvert and library QC workflows ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reports#sample-script) Sample Script Below is a sample Bash script for running the **dragen-reports** tool from the DRAGEN Reports docker image, with a line-by-line explanation 1. Call to Docker to mount a new container and execute a command in it 2. Specify the platform being run. In this case I am running on a Mac, so I specify that I need an image that can run on an _amd64_ processor 3. Mount the input data from our local machine to the Docker container 4. Mount the desired output location from our local machine to the Docker container 5. Docker _remove_ argument, tellind Docker to unmount and delete the container when the process is finished 6. Specify that Docker image to be run, in this case _dragen\_reports v4.3.0_ 7. Call to execute the **dragen-reports** command-line tool within the container 8. _\--force_ option, to enable over-writing of any pre-existing output files 9. _\--directory_ option specifying the input data directory mount on Line #3 10. _\--output_ option specifying the report file to output 11. _\--manifest_ option to specify the manifest file to use - in this case we are requesting a WGS Germline report [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reports#dragen-summary-reports) Dragen-summary-reports --------------------------------------------------------------------------------------------------------------------------------------------------- The **dragen-summary-reports** tool produces reports for DRAGEN analysis results from a NovaSeqX instrument. These reports summarize which DRAGEN workflows were run, which version of DRAGEN was used, which sample project they belonged to (if any), and how many samples and errors there were. If a workflow-level report is available for a given workflow, such as one generated by **dragen-reports**, that report will be linked from the _Workflow_ column. In addition sub-reports are created for any sample projects, containing the subset of data for that specific project, and linked from the _Sample Project_ column. ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3660538983-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FpFn0gLe5V859SXejQNAW%252Fuploads%252Fgit-blob-2419f5c6eb49a12ce404d2ca0d8fd1102f00e8c3%252Fdragen-reports.summary.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=4e0a2a59&sv=2) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reports#options-1) Options The following command-line options are available from DRAGEN Summary Reports Option Argument Description \-V, --version Output the version string of the DRAGEN-Reports executable \-v, --verbose Emit additional debug information whiile generating the report written \-n, --run-name String Manually set the run-name in the output report \-d, --directory Directory path DRAGEN output directories to generate a report from \-o, --output Filepath Generate output report to the specified location \-f, --force Force overwriting of any pre-existing report files \-h, --help Display this help command In addition, the following Docker options are recommended when calling DRAGEN Summary Reports via Docker: Option Argument Description \-v, --volume String Bind mount a volume \--rm Automatically remove the container when it exits \--platform String Set platform if server is multi-platform capable ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reports#example-script) Example Script 1. Call to Docker to mount a new container and execute a command in it 2. Specify the platform being run. In this case I am running on a Mac, so I specify that I need an image that can run on an _amd64_ processor 3. Mount the input data and output location from our local machine to the Docker container 4. Docker _remove_ argument, tellind Docker to unmount and delete the container when the process is finished 5. Specify that Docker image to be run, in this case _dragen\_reports v4.3.0_ 6. Call to execute the **dragen-summary-reports** command-line tool within the container 7. _\--force_ option, to enable over-writing of any pre-existing output files 8. _\--directory_ option specifying the input data directory mount on Line #3 9. _\--output_ option specifying the report file to output **NOTE** Due to the fact that **dragen-summary-reports** makes extensive use of symbolic links to prevent the duplication of large report files, the _\--directory_ and _\--output_ arguments must point to the same mounted volume [PreviousCommand Line Optionschevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/command-line-options) [NextTools and Utilitieschevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/tools-and-utilities) Last updated 5 months ago Was this helpful? * [Requirements](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reports#requirements) * [DRAGEN Reports Components](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reports#dragen-reports-components) * [Dragen-reports](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reports#dragen-reports) * [Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reports#options) * [Manifests](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reports#manifests) * [Sample Script](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reports#sample-script) * [Dragen-summary-reports](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reports#dragen-summary-reports) * [Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reports#options-1) * [Example Script](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reports#example-script) Was this helpful? Copy 1 docker run \ 2 --platform="linux/amd64" \ 3 -v /Users/bbowman/DragenReportTestData/ceph1463:/data \ 4 -v /Users/bbowman/DragenReportDockerTest/output:/output \ 5 --rm \ 6 dragen_reports:4.3.0 \ 7 dragen-reports \ 8 -f \ 9 -d /data \ 10 -o /output/report.html 11 -m /opt/dragen-reports/manifests/germline_wgs.json Copy 1 docker run \ 2 --platform="linux/amd64" \ 3 -v /Users/bbowman/DragenSummaryReportTestData/wgs_01:/data \ 4 --rm \ 5 dragen_reports:4.3.0 \ 6 dragen-summary-reports \ 7 -f \ 8 -d /data \ 9 -o /data/AggregateReports/summary.html --- # Repeat Expansion Detection | DRAGEN v4.3 | DRAGEN Short tandem repeats (STRs) are regions of the genome consisting of repetitions of short DNA segments called repeat units. STRs can expand to lengths beyond the normal range and cause mutations called repeat expansions. Repeat expansions are responsible for many diseases, including Fragile X syndrome, amyotrophic lateral sclerosis, and Huntington's disease. DRAGEN includes a repeat expansion detection tool for STRs, DRAGEN-STR. DRAGEN-STR Performs sequence-graph based realignment of reads that originate inside and around each target repeat. DRAGEN-STR then genotypes the length of the repeat in each allele based on these graph alignments. DRAGEN-STR is designed for PCR-free whole genome samples. Repeats are only genotyped if the coverage at the locus is at least 10x, but a minimum of 30x is recommended. Sequencing reads must be paired-end with a minimum read length of 100 (2x100bp). DRAGEN-STR cannot be run on multiple FASTQ files that are assigned to different library IDs in the `fastq_list.csv` file. DRAGEN-STR does not support somatic analysis. > NOTE: > > DRAGEN STR is based on the ExpansionHunter tool. For more information about implementation details and performance assessment refer to these [publications](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#str-expansion-detection) > . [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/repeat-expansions#repeat-expansion-detection-options) Repeat Expansion Detection Options -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- To enable DRAGEN repeat expansion detection, the following command-line options are required. * `--repeat-genotype-enable=true` * `--repeat-genotype-specs=` You can use the `--sample-sex` option to specify the sex of the sample. The following options are optional. * `--repeat-genotype-region-extension-length=` (default 1000 bp) * `--repeat-genotype-min-baseq=` (default 20) For more information on the specification file specified by `--repeat-genotype-specs` option, see [Repeat Expansion Specification Files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/repeat-expansions#repeat-expansion-specification-files) . The main output of repeat expansion detection is a VCF file that contains the variants found via this analysis. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/repeat-expansions#repeat-expansion-specification-files) Repeat Expansion Specification Files ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ The repeat-specification (also called variant catalog) JSON file defines the repeat regions for DRAGEN-STR to analyze. Default repeat-specification for some pathogenic and polymorphic repeats are in the `/resources/repeat-specs/` directory, based on the reference genome used with DRAGEN. You can create specification files for new repeat regions by using one of the provided specification files as a template. See the [catalog documentationarrow-up-right](https://github.com/Illumina/ExpansionHunter/blob/master/docs/04_VariantCatalogFiles.md) for details on the format. `--repeat-genotype-specs` is required for DRAGEN-STR. If the option is not provided, DRAGEN attempts to autodetect the applicable catalog file from `/resources/repeat-specs/` based on the reference provided. Users can choose between any of the three default repeat-specification files packaged with DRAGEN using the command line option: `--repeat-genotype-use-catalog=`. The `default` option includes ~60 repeats. The `default_plus_smn` option includes the SMN repeat in addition to all the repeats in the `default` catalog. The expanded catalog includes ~174K repeats, see [Covered Repeat Regions](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/repeat-expansions#covered-repeat-regions) . If `--repeat-genotype-use-catalog` is not specified on the command line, then the `default` catalog is used. The repeat genotyping results will be incorrect if the selected reference genome is not compatible with the repeat specification file. When this occurs, many repeats may be marked as "LowDepth" in the VCF output file or estimated to have zero length. This can be further confirmed by visualizing read alignments with the [REViewer visualization toolarrow-up-right](https://github.com/Illumina/REViewer) . [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/repeat-expansions#covered-repeat-regions) Covered Repeat Regions -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The `default` variant catalog contains specifications on disease-causing repeats located in AFF2, AR, ARX\_1, ARX\_2, ATN1, ATXN1, ATXN10, ATXN2, ATXN3, ATXN7, ATXN8OS, BEAN1, C9ORF72, CACNA1A, CBL, CNBP, COMP, CSTB, DAB1, DIP2B, DMD, DMPK, EIF4A3, FMR1, FOXL2, FXN, GIPC1, GLS, HOXA13\_1, HOXA13\_2, HOXA13\_3, HOXD13, HTT, JPH3, LRP12, MARCHF6, NIPA1, NOP56, NOTCH2NLC, NUTM2B-AS1, PABPN1, PHOX2B, PPP2R2B, PRDM12, PRNP, RAPGEF2, RFC1, RUNX2, SAMD12, SOX3, STARD7, TBP, TBX1, TCF4, TNRC6A, VWA1, XYLT1, YEATS2, ZIC2 and ZIC3 genes. More information about disease-causing repeats can also be found [herearrow-up-right](https://gnomad.broadinstitute.org/short-tandem-repeats?dataset=gnomad_r3) . For the `expanded` variant catalog, apart from the aforementioned disease-causing repeats, there are ~174K additional polymorphic repeats. They are initially detected using STR-Finder from the 1000 Genomes Project. After that, the candidate repeats are filtered out based on a customized quality control pipeline, see details [herearrow-up-right](https://github.com/Illumina/RepeatCatalogs) . DRAGEN-STR can detect pathogenic expansions of FXN, ATXN3, ATN1, AR, DMPK, HTT, FMR1, ATXN1, C9ORF72 repeats with high accuracy (see [publications](https://help.dragen.illumina.com/dragen-v4.3/reference/citing-dragen#str-expansion-detection) ). The pathogenicity status of some repeats might depend on the presence of sequence interruptions or motif changes that DRAGEN-STR does not call. If you would like to visually inspect the relevant read alignments, you can use a Repeat Expansion Viewer third-party tool. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/repeat-expansions#repeat-expansion-detection-output-files) Repeat Expansion Detection Output Files ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/repeat-expansions#vcf-output-file) VCF Output File The results of repeat genotyping are output as a separate VCF file, which provides the length of each allele at each callable repeat defined in the repeat-specification catalog file. The name is `.repeats.vcf` (\*.gz). The VCF output file lists with the following fields first. Table 2 Core VCF Fields Field Description CHROM Chromosome identifier POS Position of the first base before the repeat region in the reference ID Always `.` REF The reference base at position POS ALT List of repeat alleles in format `` . N is the number of repeat units. If REF, then `.`. QUAL Always `.` FILTER LowDepth filter is applied when the overall locus depth is below 10x or number of reads that span one or both breakends is below 5. Table 3 Additional INFO Fields Field Description END Position of the last base of the repeat region in the reference REF Number of repeat units spanned by the repeat in the reference RL Reference length in bp VARID Variant ID from the variant catalog RU Repeat unit in the reference orientation REPID Variant ID from the variant catalog Table 4 GENOTYPE (Per Sample) Fields Field Description GT Genotype SO Type of reads that support the allele. Values can be SPANNING, FLANKING, or INREPEAT. These values indicate if the reads span, flank, or are fully contained in the repeat. REPCN Number of repeat units spanned by the allele REPCI Confidence interval for REPCN ADSP Number of spanning reads consistent with the allele ADFL Number of flanking reads consistent with the allele ADIR Number of in-repeat reads consistent with the allele LC Locus Coverage For example, the following VCF entry describes the ATXN1 repeat in a sample NA13537. In this example, the first allele spans 33 repeat units while the second allele spans 58 repeat units. The repeat unit is TGC (RU INFO field), so the sequence of the first allele is TGC x 33 and the sequence of the second allele is TGC x 58. The repeat spans 30 repeat units in the reference (REF INFO field). The length of the short allele was estimated from spanning reads (SPANNING) while the length of the expanded allele was estimated from in-repeat reads (INREPEAT). The confidence interval for the size of the expanded allele is (52,71). There are 4 spanning and 69 flanking reads consistent with the repeat allele of size 33 that is 4 reads fully contain the repeat of size 33 and 69 flanking reads overlap at most 33 repeat units. There are 83 flanking and 4 in-repeat reads consistent with the repeat allele of size 58. The average coverage of this locus is 37.46x. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/repeat-expansions#additional-output-files) Additional Output Files The sequence-graph alignments of reads in the targeted repeat regions are output in a BAM file. You can use a specialized GraphAlignmentViewer tool available on GitHub to visualize the alignments. Programs like Integrative Genomics Viewer (IGV) are not designed for displaying graph-aligned reads and cannot visualize these BAMs. The BAMs store graph alignments in custom XG tags using the format `,,`. * **LocusName**\---A locus identifier that matches the corresponding entry in the repeat expansion specification file. * **StartPosition**\---The starting alignment position of a read on the first graph node. * **GraphCIGAR**\---The alignment of a read against the graph starting from that position. GraphCIGAR consists of a sequence of graph node identifiers and linear CIGARS describing the alignment of the read to each node. Quality scores in the BAM file are binary. High-scoring bases are assigned a score of 40, and low-scoring bases are assigned a score of 0. [PreviousAllele Specific CNV for Somatic WES CNVchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/cnv-calling/somatic-cnv-calling-wes-ascn) [NextDe Novo Repeat Expansion Detectionchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/repeat-expansions/de-novo-str-detection) Last updated 7 months ago Was this helpful? * [Repeat Expansion Detection Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/repeat-expansions#repeat-expansion-detection-options) * [Repeat Expansion Specification Files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/repeat-expansions#repeat-expansion-specification-files) * [Covered Repeat Regions](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/repeat-expansions#covered-repeat-regions) * [Repeat Expansion Detection Output Files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/repeat-expansions#repeat-expansion-detection-output-files) * [VCF Output File](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/repeat-expansions#vcf-output-file) * [Additional Output Files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/repeat-expansions#additional-output-files) Was this helpful? Copy #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA13537 chr6 16327864 . G , . PASS END=16327954;REF=30;RL=90;RU=TGC;VARID=ATXN1;REPID=ATXN1 GT:SO:REPCN:REPCI:ADSP:ADFL:ADIR:LC 1/2:SPANNING/INREPEAT:33/58:33-33/52-71:4/0:69/83:0/4:37.459459 --- # DUX4 Rearrangement Caller | DRAGEN v4.3 | DRAGEN [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dux4-rearrangement-caller#overview) Overview ------------------------------------------------------------------------------------------------------------------------------------------------------ The DUX4 Rearrangement Caller identifies the events of potential structural rearrangements between DUX4 and other genes (including IGH). The primary support for the DUX4 Rearrangement Caller is for human reference hg38. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dux4-rearrangement-caller#functionality) Functionality ---------------------------------------------------------------------------------------------------------------------------------------------------------------- The DUX4 Rearrangement Caller has the following features: * call DUX4 Rearrangement events from various format of genomic data like FASTQ, BAM, CRAM. * scan the whole genome and identify potential DUX4 rearrangement events. * run in parallel with the host DRAGEN software with minimal overhead. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dux4-rearrangement-caller#prerequisites) Prerequisites ---------------------------------------------------------------------------------------------------------------------------------------------------------------- * Sequencing dataset to be tumor-only, paired-end and whole-genome sequencing * Sequencing dataset with mean coverage range between 25X to 120X * Sequencing dataset with mean fragment length between 300 to 500bp * Sequencing dataset with mean read length between 100 to 151bp * A reference genome that is compatible with DRAGEN software. You can download prebuilt reference genomes from our website or build your own customized version with: `dragen --build-hash-table true --output-directory --ht-reference [options]` The DRAGEN DUX4 caller has been validated with a cohort of samples that fall within the above defined parameters. If you have datasets that don't comply with the above parameters, you can bypass the requirements check by specifying `--dux4-skip-santiy-check true` to obtain experimental results. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dux4-rearrangement-caller#basic-usage) Basic usage ------------------------------------------------------------------------------------------------------------------------------------------------------------ The basic syntax of the DRAGEN command line is: `dragen [global options] [pipeline options] [output options]` * The global options are common to all pipelines and control the general behavior of DRAGEN, such as the input and output files/directories, the reference genome, and the license file. * The pipeline options are specific to each pipeline and control the parameters and features of the analysis, such as the variant callers, the filters and the annotations. * The output options control the format and content of the output files, such as the VCF, BAM, and the metrics files. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dux4-rearrangement-caller#input-files-and-command-line-options) Input files and command line options -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- For DUX4 caller, a simple and quick example would be: where DRAGEN analysis will take in sequencing data from fastq format (BAM, CRAM, ORA also acceptable) and map/align the reads to the reference genome, the mapped and sorted reads will be consumed by DUX4 caller. Alternatively, DRAGEN DUX4 caller can start from bam format input by skipping the map/align step (assuming bam file is sorted and with duplicates being marked): What's more, DUX4 caller can run in parallel with other variant callers: Finally, you will find DUX4 VCF results in the directory of --output-dir with prefix being specified by --output-file-prefix. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dux4-rearrangement-caller#output-format) Output format ---------------------------------------------------------------------------------------------------------------------------------------------------------------- The DUX4 VCF will contain positive calls that represent translocation events across gene pairs. Each event will consist of a set of 4 VCF Breakend records to describe the potential translocation event. Each record will contain PR:SR:SRPB tags to describe the number of fragment that support the events, where PR stands for number of spanning paired reads, SR stands for number of spanning split reads and SRPB stands for number of support read pairs per billion reads being processed. We predefined two sets of genomics target regions, "CoreDUX4" regions and "ExtendedDUX4" regions, to optimize the events detection process, where "CoreDUX4" regions is a subset of "ExtendedDUX4" regions. An output VCF example will look like this: [PreviousPopulation Haplotyping (Beta)chevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/population-haplotyping) [NextDRAGEN RNA Pipelinechevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline) Last updated 7 months ago Was this helpful? * [Overview](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dux4-rearrangement-caller#overview) * [Functionality](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dux4-rearrangement-caller#functionality) * [Prerequisites](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dux4-rearrangement-caller#prerequisites) * [Basic usage](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dux4-rearrangement-caller#basic-usage) * [Input files and command line options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dux4-rearrangement-caller#input-files-and-command-line-options) * [Output format](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dux4-rearrangement-caller#output-format) Was this helpful? Copy dragen \ -r ${HASHTABLE_DIR} \ --enable-map-align=true \ --enable-map-align-output=false \ --enable-sort=true \ --enable-duplicate-marking=true \ --tumor-fastq1 test_IGH_DUX4.bam.r1.fastq \ --tumor-fastq2 test_IGH_DUX4.bam.r2.fastq \ --enable-dux4-caller=true \ --output-dir=${OUT_DIR} \ --output-file-prefix=${OUTPUT_PREFIX} Copy dragen \ -r ${HASHTABLE_DIR} \ --enable-map-align=false \ --enable-map-align-output=false \ --enable-sort=false \ --tumor-bam-input test_IGH_DUX4.bam \ --enable-dux4-caller=true \ --output-dir=${OUT_DIR} \ --output-file-prefix=${OUTPUT_PREFIX} Copy dragen \ -r ${HASHTABLE_DIR} \ --enable-map-align=true \ --enable-map-align-output=false \ --enable-sort=true \ --enable-duplicate-marking=true \ --tumor-fastq1 test_IGH_DUX4.bam.r1.fastq \ --tumor-fastq2 test_IGH_DUX4.bam.r2.fastq \ --enable-dux4-caller=true \ --enable-sv=true \ --enable-variant-caller=true \ --output-dir=${OUT_DIR} \ --output-file-prefix=${OUTPUT_PREFIX} Copy ##FILTER= ##INFO= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT Sample001 chr3 75667931 ExtendedDUX4:IGH:Bnd_W T T[chr14:105586938[ . CoreDUX4Present SVTYPE=BND;CIPOS=.,.;EVENTTYPE=TRA;MATEID=ExtendedDUX4:IGH:Bnd_X;TotalReadsNum=1837703714 GT:PR:SR:SRPB 1/1:.,129:.,42:93.05\ chr3 75667932 ExtendedDUX4:IGH:Bnd_V G ]chr14:105586937]G . CoreDUX4Present SVTYPE=BND;CIPOS=.,.;EVENTTYPE=TRA;MATEID=ExtendedDUX4:IGH:Bnd_U;TotalReadsNum=1837703714 GT:PR:SR:SRPB 1/1:.,129:.,42:93.05 chr4 190020407 CoreDUX4:IGH:Bnd_W C C[chr14:105586938[ . PASS SVTYPE=BND;CIPOS=.,.;EVENTTYPE=TRA;MATEID=CoreDUX4:IGH:Bnd_X;TotalReadsNum=1837703714 GT:PR:SR:SRPB 1/1:.,128:.,42:92.51\ chr4 190020408 CoreDUX4:IGH:Bnd_V C ]chr14:105586937]C . PASS SVTYPE=BND;CIPOS=.,.;EVENTTYPE=TRA;MATEID=CoreDUX4:IGH:Bnd_U;TotalReadsNum=1837703714 GT:PR:SR:SRPB 1/1:.,128:.,42:92.51 chr14 105586937 CoreDUX4:IGH:Bnd_U T T[chr4:190020408[ . PASS SVTYPE=BND;CIPOS=.,.;EVENTTYPE=TRA;MATEID=CoreDUX4:IGH:Bnd_V;TotalReadsNum=1837703714 GT:PR:SR:SRPB 1/1:.,128:.,42:92.51\ chr14 105586937 ExtendedDUX4:IGH:Bnd_U T T[chr3:75667932[ . CoreDUX4Present SVTYPE=BND;CIPOS=.,.;EVENTTYPE=TRA;MATEID=ExtendedDUX4:IGH:Bnd_V;TotalReadsNum=1837703714 GT:PR:SR:SRPB 1/1:.,129:.,42:93.05\ chr14 105586938 CoreDUX4:IGH:Bnd_X A ]chr4:190020407]A . PASS SVTYPE=BND;CIPOS=.,.;EVENTTYPE=TRA;MATEID=CoreDUX4:IGH:Bnd_W;TotalReadsNum=1837703714 GT:PR:SR:SRPB 1/1:.,128:.,42:92.51\ chr14 105586938 ExtendedDUX4:IGH:Bnd_X A ]chr3:75667931]A . CoreDUX4Present SVTYPE=BND;CIPOS=.,.;EVENTTYPE=TRA;MATEID=ExtendedDUX4:IGH:Bnd_W;TotalReadsNum=1837703714 GT:PR:SR:SRPB 1/1:.,129:.,42:93.05 --- # DRAGEN FASTQC | DRAGEN v4.3 | DRAGEN DRAGEN FastQC is a tool for calculating common metrics used for quality control of high-throughput sequencing data. The tool is modeled after the metrics generated by Babraham Institute's FastQC tool. The metrics are generated automatically on all DRAGEN map-align workflows with no additional run time and output in a CSV format file called `\.fastqc_metrics.csv`. All metrics are calculated and reported separately for each mate-pair. For users only interested in sample QC or would like to obtain FastQC results only, DRAGEN provides a mode to generate the `fastqc_metrics.csv` file directly. By default DRAGEN FastQC and read-trimming are run as preprocessing steps to standard sequence alignment workflows. If DNA alignment is not needed or if QC results are needed more quickly, the mapping and BAM output portions of the workflow can be disabled. The workflow only outputs key metric files and runs ~70% faster. This option is available on the command-line by entering `--fastqc-only=true` after the DRAGEN command. > If FastQC runs stand-alone, then the license will not be consumed. If FastQC runs with map-align enabled, then the license will be consumed. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dragen-fastqc#differences-from-the-babraham-institutes-fastqc) Differences from the Babraham Institutes' FastQC ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- DRAGEN FastQC is a complete reimplementation of the original FastQC tool developed by the Babraham Institute (henceforth BI-FastQC). The reimplementation of FastQC in DRAGEN, however, has been modified to take advantage of the hardware-acceleration provided by the DRAGEN Field-Programmable Gate Array (FPGA) for a significant speed improvement. As such, there are some differences in how the values are calculated and the resulting metrics will not be exactly identical between the two tools. The most significant differences are described below. * **Binning:** BI-FastQC uses a customizable binning strategy with a default of 5bp bins, while DRAGEN uses an algorithmic binning strategy based on the Granularity setting described below. In general, this should mean that DRAGEN provides more precise results at default settings. * **Outputs:** BI-FastQC text output contain the same information as their plots in tabular format, while DRAGEN-FastQC outputs it's raw data. For example, BI-FastQC both plots an outputs the average base quality per-position, while DRAGEN outputs the average base quality by both position and nucleotide. This allows for a more detailed analysis of the data, but requires slightly more work to generate the associated plot. * **Rounding:** DRAGEN consistently rounds it's calculations to the nearest integer, while the original FastQC uses a mixture of rounding and taking the mathematical floor, leading DRAGEN-FastQC to provide incrementally higher results for some metrics. * **Smoothing:** Both DRAGEN-FastQC and BI-FastQC utilize smoothing techniques for their distributions of %GC, to account for the fact that 151bp do not divide evenly into 100 percentile bins. However, to take advantage of the speed offered by the FPGA, DRAGEN utilizes a slightly different algorithm than BI-FastQC which results in slightly different results. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dragen-fastqc#metric-granularity) Metric Granularity -------------------------------------------------------------------------------------------------------------------------------------------------------------- It is not possible due to memory constraints to guarantee single-base resolution for all metrics. DRAGEN provides an algorithmic solution for binning via --fastqc-granularity. DRAGEN allocates 256 bins in memory for each size or position-based metric. The granularity value of 4–7 inclusive can be used to determine the bin size. High values use smaller bins for greater resolution. Lower values can be used to create larger bins for larger read-lengths Granularity Single Base Resolution (bp) Resolution at 150 (bp) Recommended Read-Lengths (bp) 7 1-255 1 <256 6 1-128 2 \>=256 and <507 5 1-64 4 \>=507 and <4031 4 1-32 8 \>=4031 If a value for --fastqc-granularity is not provided by the user, DRAGEN will attempt to estimate the read length of the input data and set the granularity accordingly. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dragen-fastqc#adapter-and-kmer-sequence-files) Adapter and Kmer Sequence Files ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- To include metrics for adapter or other sequence content, DRAGEN FastQC needs to be provided with the desired sequences in FASTA format. DRAGEN provides two options for this purpose, `--fastqc-adapter-file` for adapter sequences and `--fastqc-kmer-file` for any additional kmers of interest so that users can add sequences of interest without changing the expected adapter results. DRAGEN FastQC can accept up to a combined total of 16 adapters and kmer sequences. Each sequence can be a maximum of 12 bp in length. By default, DRAGEN uses the adapter file located at `/config/adapter_sequences.fasta`. The file contains the following same adapter sequences as Babraham's FastQC v 0.11.10 and later. * Illumina Universal Adapter--AGATCGGAAGAG * Illumina Small RNA 3' Adapter--TGGAATTCTCGG * Illumina Small RNA 5' Adapter--GATCGTCGGACT * Nextera Transposase Sequence--CTGTCTCTTATA [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dragen-fastqc#fastqc-metrics-output) FastQC Metrics Output -------------------------------------------------------------------------------------------------------------------------------------------------------------------- The FastQC metrics are output to a CSV file format in the run output directory called * .fastqc\_metrics.csv The reported metrics are broken down into eight sections by metric type. Each section is broken down further into separate rows by either the length, position, or other relevant categorical variables. The following are the metric sections. * **Read Mean Quality**\---Total number of reads. Each average Phred-scale quality value is rounded to the nearest integer. * **Positional Base Mean Quality**\---Average Phred-scale quality value of bases with a specific nucleotide and at a given location in the read. Locations are listed first and can be either specific positions or ranges. The nucleotide is listed second and can be A, C, G, or T. N or ambiguous bases are assumed to have the system default value, usually QV2. * **Positional Base Content**\---Number of bases of each specific nucleotide at given locations in the read. Locations are given first and can be either specific positions or ranges. The nucleotide is listed second and can be A, C, G, T, N. * **Read Lengths**\---Total number of reads with each observed length. Lengths can be either specific sizes or ranges, depending on settings specified using `--fastqc-granularity`. * **Read GC Content**\---Total number of reads with each GC content percentile between 0 % and 100 %. * **Read GC Content Quality**\---Average Phred-scale read mean quality for reads with each GC content percentile between 0% and 100%. * **Sequence Positions**\---Number of times an adapter or other kmer sequence is found, starting at a given position in the input reads. Sequences are listed first in the metric description in quotes. Locations are listed second and can be either specific positions or ranges. * **Positional Quality**\---Phred-scale quality value for bases at a given location and a given quantile of the distribution. Locations are listed first and can be either specific positions or ranges. Quantiles are listed second and can be any whole integer 0–100. The following are examples rows from each section. Section Mate Metric Value READ MEAN QUALITY Read1 Q38 Reads 965377 ... POSITIONAL BASE MEAN QUALITY Read1 ReadPos 145-152 T Average Quality 34.49 POSITIONAL BASE MEAN QUALITY Read1 ReadPos 150 T Average Quality 34.44 POSITIONAL BASE MEAN QUALITY Read1 ReadPos 256+ T Average Quality 36.99 ... POSITIONAL BASE CONTENT Read1 ReadPos 145-152 A Bases 113362306 POSITIONAL BASE CONTENT Read1 ReadPos 150 A Bases 14300589 POSITIONAL BASE CONTENT Read1 ReadPos 256+ A Bases 13249068 ... READ LENGTHS Read1 150bp Length Reads 77304421 READ LENGTHS Read1 144-151bp Length Reads 77304421 READ LENGTHS Read1 \>=255bp Length Reads 1000000 ... READ GC CONTENT Read1 50% GC Reads 140878674373 ... READ GC CONTENT QUALITY Read1 50% GC Reads Average Quality 36.20 ... SEQUENCE POSITIONS Read1 'AGATCGGAAGAG' 137bp Starts 20 SEQUENCE POSITIONS Read1 'AGATCGGAAGAG' 137-144bp Starts 23 ... POSITIONAL QUALITY Read1 ReadPos 150 50% Quantile QV 37 POSITIONAL QUALITY Read1 ReadPos 145-152 50% Quantile QV 37 ... [PreviousRead Trimmingchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming) [NextSorting and Duplicate Markingchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/sort-dupmark) Last updated 7 months ago Was this helpful? * [Differences from the Babraham Institutes' FastQC](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dragen-fastqc#differences-from-the-babraham-institutes-fastqc) * [Metric Granularity](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dragen-fastqc#metric-granularity) * [Adapter and Kmer Sequence Files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dragen-fastqc#adapter-and-kmer-sequence-files) * [FastQC Metrics Output](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dragen-fastqc#fastqc-metrics-output) Was this helpful? --- # DRAGEN Host Software | DRAGEN v4.3 | DRAGEN You use the DRAGEN host software program _dragen_ to build and load reference genomes, and then to analyze sequencing data by decompressing the data, mapping, aligning, sorting, duplicate marking with optional removal, and variant calling. Invoke the software using the _dragen_ command. The command line options are described in the following sections. Command line options can also be set in a configuration file. For more information on configuration files, see [Configuration Files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#configuration-files) . If an option is set in the configuration file and is also specified on the command-line, the command line option overrides the configuration file. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#command-line-options) Command-line Options ----------------------------------------------------------------------------------------------------------------------------------------------------- The following are examples of frequently used command lines: * Build Reference/Hash Table Copy dragen --build-hash-table true --ht-reference \ --output-directory [options] * Run Map/Align and Variant Caller (\*.fastq to \*.vcf) Copy dragen -r --output-directory \ --output-file-prefix [options] -1 \ [-2 ] --RGID --RGSM --enable-variant-caller true * Run Map/Align (\*.fastq to \*.bam) Copy dragen -r --output-directory \ --output-file-prefix [options] \ -1 [-2 ] \ --RGID --RGSM * Run Variant Caller Only (\*.bam to \*.vcf) Copy dragen -r --output-directory \ --output-file-prefix [options] -b \ --enable-map-align false \ --enable-variant-caller true * Re-map and Run Variant Caller (\*.bam to \*.vcf) Copy dragen -r --output-directory \ --output-file-prefix [options] -b \ --enable-map-align true \ --enable-variant-caller true * Run BCL Converter (BCL to \*.fastq) Copy dragen --bcl-conversion-only true --bcl-input-directory \ --output-directory * Run RNA Map/Align (\*.fastq to \*.bam) Copy dragen -r --output-directory \ --output-file-prefix [options] -1 \ [-2 ] --enable-rna true For recommended command lines in typical use cases, see [DRAGEN Recipes](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes) . ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#reference-genome-options) Reference Genome Options Before you can use the DRAGEN system for aligning reads, you must load a reference genome and its associated hash tables onto the PCIe card. For information on preprocessing a reference genome's FASTA files into the native DRAGEN binary reference and hash table formats, see [Prepare a Reference Genome](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-reference-support/prepare-a-reference-genome) . You must also specify the directory containing the preprocessed binary reference and hash tables with the `-r [or --ref-dir]` option. This argument is always required. Use the following command to load the reference genome and hash tables to DRAGEN card memory separately from processing reads. `dragen -r /staging/human/reference/hg19/hg19.fa.k_21.f_16.m_149` Use the `-l (--force-load-reference)` option to force the reference genome to load even if it is already loaded. `dragen -l -r /staging/human/reference/hg19/hg19.fa.k_21.f_16.m_149` The time needed to load the reference genome depends on the size of the reference, but for typical recommended settings, it takes approximately 30--60 seconds. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#operating-modes) Operating Modes DRAGEN has two primary modes of operation, as follows: * Mapper/aligner * Variant caller DRAGEN is capable of performing each mode independently or as an end-to-end solution. DRAGEN also allows you to enable and disable decompression, sorting, duplicate marking, and compression along the DRAGEN pipeline. * **Full pipeline mode** To execute full pipeline mode, set `--enable-variant-caller` to `true` and provide input as unmapped reads in \*.fastq, \*.bam, or \*.cram formats. DRAGEN performs decompression, mapping, aligning, sorting, and optional duplicate marking and feeds directly into the variant caller to produce a VCF file. In this mode, DRAGEN uses parallel stages throughout the pipeline to drastically reduce the overall run time. * **Map/align mode** Map/align mode is enabled by default. Input is unmapped reads in \*.fastq, \*.bam, or \*.cram format. DRAGEN produces an aligned and sorted BAM or CRAM file. To mark duplicate reads at the same time, set `‑-enable‑duplicate‑marking` to `true`. * **Variant caller mode** To execute variant caller mode, set the `--enable-variant-caller` option to true, and set `--enable-map-align` option to false. The input must be a mapped and aligned BAM/CRAM file. DRAGEN produces a VCF file. DRAGEN will force-enable re-sorting of the BAM, because a number of read statistics and estimates are required for the Variant Caller to operate effectively. Setting `--enable-sort` to `false` will be overridden. BAM files cannot be duplicate marked in the DRAGEN pipeline prior to variant calling if they have not already been marked. Use the end-to-end mode of operation to take advantage of the mark-duplicates feature. * **RNA-Seq data** To enable processing of RNA-Seq--based data, set `--enable-rna` to `true`. DRAGEN uses the RNA spliced aligner during the mapper/aligner stage. DRAGEN dynamically switches between the required modes of operation.. * **Bisulfite MethylSeq data** To enable processing of Bisulfite MethylSeq data, set the `--enable-methylation-calling` option to true. DRAGEN automates the processing of data for Lister (directional) and Cokus (nondirectional) protocols to generate a single BAM with bismark-compatible tags. Alternatively, you can run DRAGEN in a mode that produces a separate BAM file for each combination of the C->T and G->A converted reads and references. To enable this mode of processing, you need to build a set of reference hash tables with `--ht-methylated` enabled, and run DRAGEN with the appropriate `‑‑methylation-protocol` setting. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#output-options) Output Options The following command line options for output are mandatory: * `--output-directory `—Specifies the output directory for generated files. * `--output-file-prefix `\-Specifies the output file prefix. DRAGEN appends the appropriate file extension onto this prefix for each generated file. * `-r [--ref-dir ]`—Specifies the reference hash table. The following examples do not include these mandatory options. For mapping and aligning, the output is sorted and compressed into BAM format by default before saving to disk. The user can control the output format from the map/align stage with the `--output-format ` option. If the output file exists, the software issues a warning and exits. To force overwrite if the output file already exists, use the `-f [ --force ]` option. For example, the following commands output to a compressed BAM file, and then forces overwrite: `dragen ... -f` `dragen ... -f --output-format bam` To generate a BAI-format BAM index file (\*.bai), set `--enable-bam-indexing` to true. The following example outputs to a SAM file, and then forces overwrite: `dragen ... -f --output-format sam` The following example outputs to a CRAM file, and then forces overwrite: `dragen ... -f --output-format cram` DRAGEN only outputs lossless CRAM files. All QNAMEs and BAM tags are preserved in the CRAM. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#alignment-tags) Alignment tags DRAGEN can generate mismatch difference (MD) tags, as described in the BAM standard. The feature is turned off by default because there is a small performance cost to generate these strings. To generate MD tags, set `--generate-md-tags` to true. To generate ZS:Z alignment status tags, set `--generate-zs-tags` to true. These tags are only generated in the primary alignment and when a read has suboptimal alignments qualifying for secondary output (even if none were output because `--Aligner.sec-aligns` was set to 0). The following are valid tag values: Tag Tag meaning `ZS:Z:R` Multiple alignments with similar score were found. `ZS:Z:NM` No alignment was found. `ZS:Z:QL` An alignment was found but it was below the quality threshold. To generate SA:Z tags, set `--generate-sa-tags` to true (the default). These tags provide alignment information (position, cigar, orientation) of groups of supplementary alignments, which are useful in structural variant calling. To generate pair score in a ps:i tag, set `--generate-ps-tags` to true (false by default for DNA, true for RNA). The pair score is used in DRAGEN for computing MAPQ and can be used to check how well alignment candidate pairs score against each other. DRAGEN can also output mate alignment tags. To generate the mate cigar (in the MC:Z tag), set `--generate-mc-tags` to true (this is the default). To generate the mate mapping quality (in the MQ:i) tag, set `--generate-mq-tags` to true (this is the default). To generate mate sequence (in the R2:Z tag) and mate base qualities (in the Q2:Z tag), set `--generate-r2-tags` to true (default is false) and set `--generate-q2-tags` to true (default is false) respectively. Please note that when enabled, R2:Z and Q2:Z tags are emitted only for improperly paired read alignments with fragment length atleast 1000 bp. Also, our methylation pipelines currently do not support the output of mate alignment tags. DRAGEN also outputs a graph alignment tag ga:Z `--generate-ga-tags` (true by default for DNA, false for RNA) when applicable. This tag is used to describe the best alt contig alignment which improved the score of a primary-contig alignment at its liftover position. It can also be used to describe read alignments to alt contigs for which there is no liftover and the primary alignment is unmapped. For example, cases when the read maps best to an alt contig describing a novel long-insertion that is not present in the reference. In addition, read alignments that have been marked as unmapped because they map to auto-detected decoy contigs not present in the original user-provided FASTA also have their alignments described in the ga tag. The ga tag uses the same format as the SA tag used to describe supplementary alignments. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#cram-output) CRAM Output When CRAM is selected as output, DRAGEN generates a CRAM file with the following features: * CRAM format V3.0 is produced * The CRAM is lossless. Lossy compression is never employed and not optional * Quality score compression is lossless. Read names are preserved * Only the GZIP compression algorithm is employed for maximum compatibility. bgzip, lzma not employed. rANS is used for quality scores * All input BAM tags are preserved * The reference used to compress the CRAM file, is the DRAGEN Hash Table provided during the map/align run. When decompressing the CRAM with a FASTA file and 3rd party tools, the FASTA that was used to generate the Hash Table must be used. * A CRAM index is produced in .crai format * CRAM output is only possible when sort is enabled. CRAM alignments will always be positionally sorted The following list of default settings are used for the CRAM output CRAM option Value Description SEQS\_PER\_SLICE 2000 Max sequences per slice BASES\_PER\_SLICE SEQS\_PER\_SLICE\*500 Max bases per slice SLICE\_PER\_CNT 1 Max slices per container embed\_ref 0 Do not embed reference sequence noref 0 Do not use non-referenced based encoding multiseq \-1 Do not use multiple references per slice unsorted 0 Do not use unsorted mode use\_bz2 0 Do not compress using bzip2 use\_lzma 0 Do not compress using lmza use\_rans 1 Use rANS for quality score compression binning NONE Qual score binning not used preserve\_aux\_order 1 Preserve all aux tags and order (incl RG,NM,MD) preserve\_aux\_size 0 Aux tag sizes not preserved ('i', 's', 'c') lossy\_read\_names 0 Preserve read names lossy 0 Do not enable Illumina 8 quality-binning system ignore\_md5 0 Enable all checking of checksums decode\_md 0 Do not (re)generate MD and NM tags ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#input-options) Input Options DRAGEN can process reads in FASTQ format or BAM/CRAM format. DRAGEN supports the following compression options for FASTQ input files. * Uncompressed * gzip or bgzip compression * ORA compression. To use ORA compression, you must provide an ORA reference and reference directory. See ORA Compression and Decompression. If your input FASTQ files are gzipped, DRAGEN automatically decompresses the files using hardware-accelerated decompression, and then streams the reads into the mapper. If your files end in \*.ora, DRAGEN automatically decompresses the files using ORA decompression, and then streams the reads into the mapper. The same FASTQ command-line options apply for all compression formats. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#fastq-input-files) FASTQ Input Files FASTQ input files can be single-ended or paired-end, as shown in the following examples. * Single-ended in one FASTQ file (-1 option) * Paired-end in two matched FASTQ files(-1 and -2 options) * Paired-end in a single interleaved FASTQ file(`--interleaved (-i)` option) Both bcl2fastq and the DRAGEN BCL command use a common file naming convention, as follows: `_S<#>___.fastq.gz` Older versions of bcl2fastq and DRAGEN could segment FASTQ samples into multiple files to limit file size or to decrease the time to generate them. For Example: These files do not need to be concatenated to be processed together by DRAGEN. To map/align any sample, provide the first file in the series (`-1 _001.fastq`). DRAGEN reads all segment files in the sample consecutively for both of the FASTQ file sequences specified using the -1 and -2 options for paired-end input and for compressed fastq.gz files. To turn the behavior off, set `‑‑enable-auto-multifile` to false on the command line. DRAGEN can also optionally read multiple files by the sample name given in the file name, which can be used to combine samples that have been distributed across multiple BCL lanes or flow cells. To enable this feature, set the `--combine-samples-by-name` option to true If the FASTQ files specified on the command-line use the Casava 1.8 file naming convention shown above and additional files in the same directory share that sample name, those files and all their segments are processed automatically. Note that sample name, read number, and file extension must match. Index barcode and lane number can differ. To avoid impacting system performance, input files must be located on a fast file system. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#multiple-fastq-input-files) Multiple FASTQ Input Files To process multiple FASTQ input files as one sample, it is recommended that you use the `--fastq-list ` option to specify the name of a CSV file containing the list of FASTQ files, instead of using the `--combine-samples-by-name` option. For example: Using a CSV file avoids having to concatenate the FASTQ files, for cases where there are multiple FASTQ files for a sample such as top-up scenarios or where FASTQ files are split across lanes. It also allows you to name the FASTQ input files, input from multiple subdirectories, and add BAM tags specified explicitly for each read group. DRAGEN automatically generates a CSV file of the correct format during BCL conversion to FASTQ. The CSV file is named `fastq_list.csv` and contains an entry for each FASTQ file or paired-end file pair produced during the run. **FASTQ CSV File Format** The first line of the CSV file specifies the title of each column, and is followed by one or more data lines. All lines in the CSV file must contain the same number of comma-separated values and should not contain white space or other extraneous characters. Column titles are case-sensitive. The following column titles are required: * RGID--Read Group * RGSM--Sample ID * RGLB--Library * Lane--Flow cell lane * Read1File--Full path to a valid FASTQ input file * Read2File--Full path to a valid FASTQ input file. Required for paired-end input. If not using paired-end input, leave empty. Each FASTQ file referenced in the CSV list can be referenced only once. All values in the Read2File column must be either nonempty and reference valid files, or they must all be empty. When generating a BAM file using fastq-list input, one read group is generated per unique RGID value. The BAM header contains RG tags for the following read groups: * ID (from RGID) * SM (from RGSM) * LB (from RGLB) You can specify additional tags for each read group by adding a column title. The column title must be only four upper-case characters and begin with RG. For example, to add a PU (platform unit) tag, add a column named RGPU and specify the value for each read group in this column. All column titles must be unique. A fastq-list file can contain files for more than one sample. If a fastq-list file contains only one unique RGSM entry, then no additional options need to be specified, and DRAGEN processes all files listed in the fastq-list file. If there is more than one unique RGSM entry in a fastq-list file, `--fastq-list-sample-id ` must be used in addition to `--fastq-list ` to process only a specific sample from the CSV file. Only the entries in the fastq-list file with an RGSM value that match the specified SampleID are processed. * Independent processing and output for multiple individual samples in one run is not supported. * To process all listed files together as one sample, regardless of the RGSM value, the option `--fastq-list-all-samples=true` can be used instead of `--fastq-list-sample-id`. Note For a single run, only one BAM and VCF output file are produced because all input read groups are expected to belong to the same sample. To process multiple samples independently from one BCL conversion run, DRAGEN must be run multiple times using different values for the \`--fastq-list-sample-id\` option. There is no option to specify groupings or subsets of RGSM values for more complex filtering, but the fastq-list file can be modified to achieve the same effect. The following is an example FASTQ list CSV file with the required columns: If you use the `--tumor-fastq-list` option for somatic input, use the `--tumor-fastq-list-sample-id SampleID>` option to specify the sample ID for the corresponding FASTQ list, as shown in the following example: **Tumor-Normal Pairs Input** If using fastq\_lists or tumor\_fastq\_lists comprising of multiple samples (RGSMs) in somatic mode, you can use a loop to iterate through the two lists to create tumor-normal pairs for testing. Create a \*.txt file with the RGSM of each normal sample to be tested (one per line), and then create a separate \*.txt file with the RGSM of the tumor samples to be tested. Make sure that the tumor sample RGSM is listed in the same order as the corresponding normal samples and to include a blank line after the last sample. You can use the following example script to perform testing in somatic mode. Each iteration takes one entry from the tumor samples list and one entry from the normal samples list (from top to bottom) to create a tumor-normal pair as input for the DRAGEN run. The following are examples of the FASTQ lists and samples lists used as input for the script. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#fastq-ora-input-files) FASTQ ORA Input Files You can use the same options as the other FASTQ input file types for ORA files. To use the ORA file, replace the FASTQ file name with the ORA file name and specify the ORA reference directory using `--ora-reference`. See ORA Compression and Decompression for more information on ORA reference files. The following command represents paired-end in two matched ORA FASTQ files (-1 and -2 options). #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#bam-input-files) BAM Input Files BAM files can be used as input to the mapper/aligner. By default `--enable-map-align` is true. You can use the BAM file as input to the variant caller by setting the `--enable-map-align` option to false. When you specify a BAM file as input, with map/align enabled, DRAGEN ignores any alignment information contained in the input file, and outputs new alignments for all reads. If the input file contains paired-end reads, it is important to specify that the input data should be sorted so that pairs can be processed together. Other pipelines would require you to re-sort the input data set by read name. DRAGEN vastly increases the speed of this operation by pairing the input reads, and sending them on to the mapper/aligner when pairs are identified. Use the `--pair-by-name` option to enable or disable this feature (the default is true). Specify single-ended input in one BAM file with the (`-b`) and `--pair-by-name=false` options, as follows: Specify paired-end input in one BAM file with the (`-b`) and `\--pair-by-name=true` options, as follows: #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#cram-input) CRAM Input You can use CRAM files as input to the DRAGEN mapper/aligner and variant caller. The DRAGEN functionality available when using CRAM input is the same as when using BAM input. By default, the CRAM compressor and decompressor uses the DRAGEN reference specified with the `--ref-dir` option. CRAM compression is reference based, and the reference used for compression is not part of the CRAM file. Therefore, the CRAM input file must have been created with the same reference than what is provided to DRAGEN for the analysis. DRAGEN supports the re-alignment of a CRAM input that was created with a different reference in one step. Re-aligning a CRAM file that was created with a different reference requires use of the `--cram-reference` option. This option will make the CRAM decompressor use the specified reference. * `--cram-reference` can be either a fasta file, or a DRAGEN hash table folder. * If pointing to a fasta file, the fasta .fai index file must be present next to the fasta file * CRAM output will always be compressed using the `--ref-dir` reference _Example: CRAM was created with hg19, re-analysis with hg38_ The following options are used for providing a CRAM input to either mapper/aligner or variant caller: * `--cram-input`\--The name and path for the CRAM file * `--cram-input`\--One usage example is paired-end input in a single CRAM file. In addition, set the `--pair-by-name option` to true. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#bcl-input-files) BCL Input Files BCL is the output format of Illumina sequencing systems. Under limited circumstances, DRAGEN can read directly from BCL for map-align operations, saving the time needed for conversion to FASTQ. DRAGEN can read directly from BCL in the following circumstances: * Only one lane is input as part of a run (specified on the command-line). * The lane has only a single sample specified in the SampleSheet.csv file. When converting BCL to FASTQ is required, DRAGEN provides a BCL to FASTQ converter (see DRAGEN BCL Data Conversion). The following example command is for BCL input with only one lane of input: For additional BCL conversion options, see Input File Types. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#handling-of-n-bases) Handling of N bases One of the techniques that DRAGEN uses to optimize handling sequences can lead to the overwriting the base quality score assigned to N base calls. When you use the `--fastq-n-quality` and `--fastq-offset` options, the base quality scores are overwritten with a fixed base quality. The default values for these options are 2 and 33 to match the Illumina minimum quality of 35 (ASCII character ‘#’). #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#read-names-for-paired-end-reads) Read Names for Paired-End Reads By a common convention, read names can include suffixes, such as `/1` or `/2`), which indicate the end of a pair the read represents. For BAM input using the `--pair-by-name` option, DRAGEN ignores these suffixes to find matching pair names. By default, DRAGEN uses the forward slash character as the delimiter for these suffixes and ignores the `/1` and `/2` when comparing names. By default, DRAGEN strips these suffixes from the original read names. DRAGEN has the following options to control how suffixes are used: * To change the delimiter character, for suffixes, use the `--pair-suffix-delimiter` option. Valid values for this option include forward-slash (/), dot (.), and colon (:). * To preserve the entire name, including the suffixes, set `--strip-input-qname-suffixes` to false. * To append a new set of suffixes to all read names, set `--append-read-index-to-name` to true. The delimiter is determined by the `--pair-suffix-delimiter` option. By default, the delimiter is a slash, so `/1` and `/2` are added to the names. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#gene-annotation-input-files) Gene Annotation Input Files When processing RNA-Seq data, you can supply a gene annotations file by using the `--annotation-file` option. Providing this file improves the accuracy of the mapping and aligning stage (see \[Input Files\]{.underline}). The file should conform to the GTF/GFF format specification and should list annotated transcripts that match the reference genome being mapped against. The similar GFF3 format is currently not supported, due to inconsistent contig naming between GENCODE and Ensembl. See the RNA user guide section for more details on potential issues and workarounds. DRAGEN can take the SJ.out.tab file (see \[SJ.out.tab\]{.underline}) as an annotations file to help guide the aligner in a two-pass mode of operation. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#networked-streaming) Networked Streaming #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#aws-s3-azure-blob-storage-and-aws-presigned-url-input-streaming) AWS S3, Azure Blob Storage, and AWS Presigned URL Input Streaming DRAGEN can stream input files directly from an AWS S3 bucket, Azure Blob storage account, or by using AWS presigned URLs (presigned URLs are not supported for Azure Blob storage at this time). With streaming, input files are not required to be downloaded locally prior to being processed. The files are streamed over the network directly into the DRAGEN processor. Input streaming is most beneficial for large input files. DRAGEN supports input streaming for BAMs and compressed FASTQ files. For FASTQ files, input streaming can be used in all the configurations, including single-end FASTQs, paired-end FASTQs, and FASTQ lists. Input streaming is supported for the following use cases: * Mapping/aligning of FASTQ and BAM. * Germline and somatic small variant calling from BAM (without remapping). For other file types that are significantly smaller in size, download them locally before running the analysis. **Streaming FASTQ Input Using AWS S3** **Streaming FASTQ Input Using Azure Blob Storage Account** **Streaming FASTQ Input Using Presigned URLs (for AWS only)** **Streaming BAM Input Using AWS S3** **Streaming BAM Input Using Azure Blob Storage Account** **Streaming BAM Input Using Presigned URLs (for AWS only)** #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#aws-s3-azure-blob-storage-output-streaming) AWS S3, Azure Blob Storage, Output Streaming DRAGEN can stream its output to an AWS S3 Bucket or an Azure Blob Storage Account Container. Output streaming is beneficial for large output files and for sharing results. **Streaming output to AWS S3** **Streaming output to Azure Blob Storage Account** #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#security-and-permissions) Security and Permissions To stream input files or write to a cloud providers storage, you must have permission to access the remote files. **AWS S3** S3 requires AWS authentication and credentials. The authentication should already be set up on the instance you are running, for example, via IAM policies. **Azure Blob Storage Account** Azure requires authentication and environment variables. DRAGEN supports two cases: (1) Using managed identities and (2) Storage account access keys. To use managed identities you must run DRAGEN on an Azure instance. The instance must have `Contributor` permissions (read/write) on the Storage Account it wants to read and write to. If the instance has a single managed identity, only the `AZ_ACCOUNT_NAME=` environment variable is required. For multiple managed identities, you must also provide the `AZR_IDENT_CLIENT_ID=` environment variable, with the client id of the identity that can access your storage bucket. This can be found on the Azure Portal. With storage account access keys, DRAGEN can write to an Azure bucket both on and off Azure instances. For this use case, find the [Storage Account Access Keyarrow-up-right](https://docs.microsoft.com/en-us/azure/storage/common/storage-account-keys-manage?toc=%2Fazure%2Fstorage%2Fblobs%2Ftoc.json&tabs=azure-portal) and set the environment variables `AZ_ACCOUNT_NAME=` and `AZ_ACCOUNT_KEY=`. **Presigned URL (AWS only)** An AWS presigned URL most likely has a query string attached to it, which provides the authentication credentials or necessary tokens to grant permission to the S3 bucket (e.g., `https://bucket-name.amazonaws.com/path/to/folder?querystring`). Currently, streaming input to DRAGEN Azure presigned URLs is not supported. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#sample-sex) Sample Sex Use the `--sample-sex` command line option to control the sex karyotype input used in downstream components, such as variant callers. If a sample sex karyotype input is not specified using the command line, the sex karyotype is automatically determined. The sex karyotype input is converted to a reference sex karyotype for use in variant calling. Other components might support sex karyotype input. Refer to the corresponding section for the component you are using. The `--sample-sex` option supports the following values. Values are not case-sensitive. * `none`: No sex karyotype input. Components use a default reference sex karyotype. * `auto`: The sex karyotype is estimated by the Ploidy Estimator. If using CNV calling, sex karyotype is determined using a separate sex estimation module. If DRAGEN cannot estimate the sex karyotype, then components do not have a sex karyotype input. This behavior is then the same as `none`. `auto` is the default value. * `female`: Sex karyotype input is XX. * `male`: Sex karyotype input is XY. The following example command lines use `--sample-sex` to specify the sex karyotype. If the value is `none`, `female`, or `male`, the Ploidy Estimator could still run and produce output, but variant callers will not use any estimated sex karyotype that is different than the sex karyotype provided via the command-line. The sex karyotype input is converted to the reference sex karyotype for the different components as follows. See the relevant component section for more information on how `--sample-sex` is used. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#reference-sex-karyotype) Reference Sex Karyotype Sex Karyotype Input CNV Caller DRAGEN-STR Ploidy Caller Small Variant Caller SV Caller XX XX XX XX XX XXYY XY XY XY XY XY XXYY XXY XY XX XY XXYY XXYY XYY XY XY XY XXYY XXYY X0 XX XY XX XXYY XXYY XXXY XY XX XY XXYY XXYY XXX XX XX XX XXYY XXYY None XX/XY XX XX XXYY XXYY * For sex karyotype input of None, CNV independently checks the coverage ratio of X and Y to determine the reference sex karyotype. Detection of minimal Y coverage will yield XY, otherwise XX. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#preservation-or-stripping-of-bqsr-tags) Preservation or Stripping of BQSR Tags The Picard Base Quality Score Recalibration (BQSR) tool produces output BAM files that include tags BI and BD. BQSR calculates these tags relative to the exact sequence for a read. If a BAM file with BI and BD tags is used as input to mapper/aligner with hard clipping enabled, the BI and/or BD tags can become invalid. The recommendation is to strip these tags when using BAM files as input. To remove the BI and BD tags, set the `--preserve-bqsr-tags` option to false. If you preserve the tags, DRAGEN warns you to disable hard clipping. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#read-group-options) Read Group Options DRAGEN assumes that all the reads in a given FASTQ belong to the same read group. DRAGEN creates a single @RG read group descriptor in the header of the output BAM file, with the ability to specify the following standard BAM attributes: Attribute Argument Description ID `--RGID` Read group identifier. If you include any of the read group parameters, RGID is required. It is the value written into each output BAM record. LB `--RGLB` Library. PL `--RGPL` Platform/technology used to produce the reads. The BAM standard allows for values CAPILLARY, LS454, ILLUMINA, SOLID, HELICOS, IONTORRENT and PACBIO. PU `--RGPU` Platform unit, eg, flowcell-barcode.lane. SM `--RGSM` Sample. CN `--RGCN` Name of the sequencing center that produced the read. DS `--RGDS` Description. DT `--RGDT` Date the run was produced. PI `--RGPI` Predicted mean insert size. If any of these arguments are present, DRAGEN adds an RG tag to all the output records to indicate that they are members of a read group. The following example shows a command line that includes read group parameters: When using the `--fastq-list` option to input multiple read groups, BAM tags (and others) are specified for each read group by adding columns to the `fastq_list.csv` file. Each column heading consists of four capital letters and each begins with 'RG'. For each column, each read group's values for that column are propagated to the output BAM file in an identically named tag. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#license-options) License Options To suppress the license status message at the end of the run, use the `--lic-no-print` option. The following shows an example of the license status message: [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#autogenerated-md5sum-for-bam-and-cram-output-files) Autogenerated MD5SUM for BAM and CRAM Output Files ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- An MD5SUM file is generated automatically for BAM and CRAM output files. The MD5SUM file has the same name as the output file, with an .md5sum extension appended (eg, whole\_genome\_run\_123.bam.md5sum). The MD5SUM file is a single-line text file that contains the md5sum of the output file, which exactly matches the output of the Linux md5sum command. The MD5SUM calculation is performed as the output file is written, so there is no measurable performance impact (compared to the Linux md5sum command, which can take several minutes for a 30x BAM). [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#configuration-files) Configuration Files --------------------------------------------------------------------------------------------------------------------------------------------------- Command line options can be stored in a configuration file. The location of the default configuration file is `/config/dragen-user-defaults.cfg`. You can override this file by using the `--config-file (-c)` option to specify a different file. The configuration file used for a given run supplies the default settings for that run, any of which can be overridden by command line options. The recommended approach is to use the dragen-user-defaults.cfg file as a template to create default settings for different use cses. Copy dragen-user-defaults.cfg, rename the copy, then modify the new file for the specific use-case. Best practice is to put options that rarely change into the configuration file and to specify options that vary from run to run on the command line. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#cloud-authentication-and-licensing) Cloud Authentication and Licensing --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Please refer to the [Cloud Licensing Reference Section](https://help.dragen.illumina.com/dragen-v4.3/reference/licensing/cloud_licensing) for guidance on how to use DRAGEN Licensing in Cloud BYOL platforms. [PreviousGetting Startedchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/getting-started) [NextDRAGEN Secondary Analysischevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software/dragen-platform) Last updated 7 months ago Was this helpful? * [Command-line Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#command-line-options) * [Reference Genome Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#reference-genome-options) * [Operating Modes](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#operating-modes) * [Output Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#output-options) * [Input Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#input-options) * [Networked Streaming](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#networked-streaming) * [Sample Sex](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#sample-sex) * [Preservation or Stripping of BQSR Tags](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#preservation-or-stripping-of-bqsr-tags) * [Read Group Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#read-group-options) * [License Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#license-options) * [Autogenerated MD5SUM for BAM and CRAM Output Files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#autogenerated-md5sum-for-bam-and-cram-output-files) * [Configuration Files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#configuration-files) * [Cloud Authentication and Licensing](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-host-software#cloud-authentication-and-licensing) Was this helpful? Copy dragen -r -1 \ --output-directory -output-file-prefix \ --RGID --RGSM Copy dragen -r -1 -2 \ --output-directory --output-file-prefix \ --RGID --RGSM Copy dragen -r -1 -i \ --RGID --RGSM Copy RDRS182520_S1_L001_R1_001.fastq.gz RDRS182520_S1_L001_R1_002.fastq.gz ... RDRS182520_S1_L001_R1_008.fastq.gz Copy dragen -r --fastq-list \ -fastq-list-sample-id -output-directory --output-file-prefix Copy RGID,RGSM,RGLB,Lane,Read1File,Read2File CACACTGA.1,RDSR181520,UnknownLibrary,1,/staging/RDSR181520_S1_L001_R1_001.fastq, /staging/RDSR181520_S1_L001_R2_001.fastq AGAACGGA.1,RDSR181521,UnknownLibrary,1,/staging/RDSR181521_S2_L001_R1_001.fastq, /staging/RDSR181521_S2_L001_R2_001.fastq TAAGTGCC.1,RDSR181522,UnknownLibrary,1,/staging/RDSR181522_S3_L001_R1_001.fastq, /staging/RDSR181522_S3_L001_R2_001.fastq AGACTGAG.1,RDSR181523,UnknownLibrary,1,/staging/RDSR181523_S4_L001_R1_001.fastq, /staging/RDSR181523_S4_L001_R2_001.fastq Copy dragen -r --tumor-fastq-list \ --tumor-fastq-list-sample-id \ --output-directory \ --output-file-prefix --fastq-list \ --fastq-list-sample-id Copy #!/bin/bash HT="/staging/HT/" tumor_fastq_list="/staging/inputs/tumor_fastq_list.csv" normal_fastq_list="/staging/inputs/normal_fastq_list.csv" tumor_samples_list="/staging/inputs/tumor_samples_list.txt" normal_samples_list="/staging/inputs/normal_samples_list.txt" while read -u 3 -r tumor_RGSM && read -u 4 -r normal_RGSM; do output_dir="/staging/results/${tumor_RGSM}_${normal_RGSM}" mkdir -p ${output_dir} dragen \ -r ${HT} \ --tumor-fastq-list ${tumor_fastq_list} \ --tumor-fastq-list-sample-id ${tumor_RGSM} \ --fastq-list ${normal_fastq_list} \ --fastq-list-sample-id ${normal_RGSM} \ --output-directory ${output_dir} \ --output-file-prefix ${tumor_RGSM}_${normal_RGSM} done 3<${tumor_samples_list} 4<${normal_samples_list} Copy Sample fastq_list.csv content: RGPL,RGID,RGSM,RGLB,Lane,Read1File,Read2File DRAGEN_RGPL,DRAGEN_RGID_N1.1,normal-1,ILLUMINA,1,/staging/inputs/normal-1_S1_L001_R1_001.fastq.gz,/staging/inputs/normal-1_S1_L001_R2_001.fastq.gz DRAGEN_RGPL,DRAGEN_RGID_N1.2,normal-1,ILLUMINA,2,/staging/inputs/normal-1_S1_L002_R1_001.fastq.gz,/staging/inputs/normal-1_S1_L002_R2_001.fastq.gz DRAGEN_RGPL,DRAGEN_RGID_N2.1,normal-2,ILLUMINA,1,/staging/inputs/normal-2_S1_L001_R1_001.fastq.gz,/staging/inputs/normal-2_S1_L001_R2_001.fastq.gz DRAGEN_RGPL,DRAGEN_RGID_N2.2,normal-2,ILLUMINA,2,/staging/inputs/normal-2_S1_L002_R1_001.fastq.gz,/staging/inputs/normal-2_S1_L002_R2_001.fastq.gz DRAGEN_RGPL,DRAGEN_RGID_N3.1,normal-3,ILLUMINA,1,/staging/inputs/normal-3_S1_L001_R1_001.fastq.gz,/staging/inputs/normal-3_S1_L001_R2_001.fastq.gz DRAGEN_RGPL,DRAGEN_RGID_N3.2,normal-3,ILLUMINA,2,/staging/inputs/normal-3_S1_L002_R1_001.fastq.gz,/staging/inputs/normal-3_S1_L002_R2_001.fastq.gz Copy Sample tumor_fastq_list.csv content: RGPL,RGID,RGSM,RGLB,Lane,Read1File,Read2File DRAGEN_RGPL,DRAGEN_RGID_T1.1,tumor-1,ILLUMINA,1,/staging/inputs/tumor-1_S1_L001_R1_001.fastq.gz,/staging/inputs/tumor-1_S1_L001_R2_001.fastq.gz DRAGEN_RGPL,DRAGEN_RGID_T1.2,tumor-1,ILLUMINA,2,/staging/inputs/tumor-1_S1_L002_R1_001.fastq.gz,/staging/inputs/tumor-1_S1_L002_R2_001.fastq.gz DRAGEN_RGPL,DRAGEN_RGID_T2.1,tumor-2,ILLUMINA,1,/staging/inputs/tumor-2_S1_L001_R1_001.fastq.gz,/staging/inputs/tumor-2_S1_L001_R2_001.fastq.gz DRAGEN_RGPL,DRAGEN_RGID_T2.2,tumor-2,ILLUMINA,2,/staging/inputs/tumor-2_S1_L002_R1_001.fastq.gz,/staging/inputs/tumor-2_S1_L002_R2_001.fastq.gz DRAGEN_RGPL,DRAGEN_RGID_T3.1,tumor-3,ILLUMINA,1,/staging/inputs/tumor-3_S1_L001_R1_001.fastq.gz,/staging/inputs/tumor-3_S1_L001_R2_001.fastq.gz DRAGEN_RGPL,DRAGEN_RGID_T3.2,tumor-3,ILLUMINA,2,/staging/inputs/tumor-3_S1_L002_R1_001.fastq.gz,/staging/inputs/tumor-3_S1_L002_R2_001.fastq.gz Copy Sample normal_samples_list content normal-1 normal-2 normal-3 Copy Sample tumor_samples_list content tumor-1 tumor-2 tumor-3 Copy dragen -r -1 -2 \ --ora-reference \ --output-directory --output-file-prefix \ --RGID --RGSM Copy dragen -r -b --output-directory \ --output-file-prefix --pair-by-name false Copy dragen -r -b --output-directory \ --output-file-prefix --pair-by-name true Copy dragen -r --cram-input --output-directory \ --output-file-prefix --cram-reference Copy dragen -r --cram-input --output-directory \ --output-file-prefix --cram-reference Copy dragen -r --cram-input --output-directory \ --output-file-prefix --pair-by-name true Copy dragen --bcl-input-dir --bcl-only-lane -r \ --output-directory --output-file-prefix Copy dragen -f \ -r /staging/human/reference/hg19/hg19.fa.k_21.f_16.m_149 \ -1 s3://s3-bucket-name/path/to/object_1.fastq.gz \ -2 s3://s3-bucket-name/path/to/object_2.fastq.gz \ --RGID object_ID \ --RGSM sample_name \ --output-directory /staging/examples/ \ --output-file-prefix streaming Copy AZ_ACCOUNT_NAME="storage-account-name" AZ_ACCOUNT_KEY="" dragen -f \ -r /staging/human/reference/hg19/hg19.fa.k_21.f_16.m_149 \ -1 https://storage-account-name.blob.core.windows.net/path/to/object_1.fastq.gz \ -2 https://storage-account-name.blob.core.windows.net/path/to/object_2.fastq.gz \ --RGID object_ID \ --RGSM sample_name \ --output-directory /staging/examples/ \ --output-file-prefix streaming Copy dragen -f \ -r /staging/human/reference/hg19/hg19.fa.k_21.f_16.m_149 \ -1 https://bucket-name.amazonaws.com/path/to/object_1.fastq.gz?querystring \ -2 https://bucket-name.amazonaws.com/path/to/object_2.fastq.gz?querystring \ --RGID object_ID \ --RGSM sample_name \ --output-directory /staging/examples/ \ --output-file-prefix streaming Copy dragen -f \ -r /staging/human/reference/hg19/hg19.fa.k_21.f_16.m_149 \ -b s3://s3-bucket-name/path/to/object_1.bam \ --output-directory /staging/examples/ \ --output-file-prefix streaming Copy AZ_ACCOUNT_NAME="storage-account-name" AZ_ACCOUNT_KEY="" dragen -f \ -r /staging/human/reference/hg19/hg19.fa.k_21.f_16.m_149 \ -b https://storage-account-name.blob.core.windows.net/path/to/object_1.bam \ --output-directory /staging/examples/ \ --output-file-prefix streaming Copy dragen -f \ -r /staging/human/reference/hg19/hg19.fa.k_21.f_16.m_149 \ -b https://bucket-name.amazonaws.com/path/to/object_1.bam?querystring \ --output-directory /staging/examples/ \ --output-file-prefix streaming Copy dragen -f \ -r /staging/human/reference/hg19/hg19.fa.k_21.f_16.m_149 \ -1 SRA056922.fastq \ --RGID object_ID \ --RGSM sample_name \ --output-directory s3://s3-bucket-name/path/to/output \ --intermediate-results-dir /staging/examples \ --output-file-prefix streaming Copy AZ_ACCOUNT_NAME="storage-account-name" AZ_ACCOUNT_KEY="" dragen -f \ -r /staging/human/reference/hg19/hg19.fa.k_21.f_16.m_149 \ -1 SRA056922.fastq \ --RGID object_ID \ --RGSM sample_name \ --output-directory https://storage-account-name.blob.core.windows.net/path/to/output \ --intermediate-results-dir /staging/examples \ --output-file-prefix streaming Copy --sample-sex FEMALE --sample-sex MALE --sample-sex NONE Copy dragen --RGID 1 --RGCN Broad --RGLB Solexa-135852 \ --RGPL Illumina --RGPU 1 --RGSM NA12878 \ -r /staging/human/reference/hg19/hg19.fa.k_21.f_16.m_149 \ -1 SRA056922.fastq --output-directory /staging/tmp/ \ --output-file-prefix rg_example Copy LICENSE_MSG| ===================================================== LICENSE_MSG| License report LICENSE_MSG|   Genome status [ACxxxxxxxxxxx] : used 1263.9 Gbases since 2018-Feb-15 (1263886160894 bases, unlimited) LICENSE_MSG|   Genome  bases [ACxxxxxxxxxxx] : 202000000 LICENSE_MSG|   Genome  bases [total]         : 202000000 --- # HLA Typing | DRAGEN v4.3 | DRAGEN DRAGEN includes a dedicated human leukocyte antigen (HLA) genotyper for calling HLA class I and class II alleles with two-field resolution (a.k.a. four-digit resolution). At this resolution, DRAGEN HLA genotyper is able to discern and report HLA alleles based on their protein sequences. For more information on HLA nomenclature, see _Nomenclature for factors of the HLA system_¹. Class I HLA typing is enabled by setting the `--enable-hla` flag to `true`. Additionally, class II HLA typing is enabled by setting the `--hla-enable-class-2` flag to `true`. For TSO500-solid or TSO500-liquid runs, HLA typing should be enabled instead through the following batch options: `--tso500-solid-hla=true` and `--tso500-liquid-hla=true` respectively. **NOTE: class II HLA typing is not supported for TSO500 runs.** [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#hla-workflow) HLA Workflow ----------------------------------------------------------------------------------------------------------------------------------------------- The HLA Caller primarily executes the following four steps: 1. Extract reads mapped to the HLA genes. These are HLA-A, -B and -C loci for class I, and HLA-DQA1, -DQB1, -DRB1 for class II loci. The human reference version is auto-detected during this step. The human reference builds hg19, hs37d5, and GRCh38 are fully supported, CHM13 build is enabled but not supported. 2. Align the extracted HLA reads to a reference set of 9,086 HLA alleles using the DRAGEN map-align processor. Only full-sequence alleles from the IMGT/HLA database (v3.45) that have also been reported on the Allele Frequency Net database were selected in building the default HLA reference resource. 3. Filter out HLA-specific alignments with sub-maximal alignment scores, and optimize the read distribution using Expectation-Maximization. 4. Select the most likely genotype for each HLA locus from a short list of candidate alleles using a homozygosity threshold set at 20%. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#reference-requirement-for-hla) Reference Requirement for HLA --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The reference directory that is supplied at command-line with `--ref-dir` must contain `anchored_hla`, a specific subdirectory with HLA-specific reference files. The DRAGEN default reference directories have been updated to contain the `anchored_hla` subdirectory. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#building-the-hla-specific-reference-subdirectory) Building the HLA-Specific Reference Subdirectory ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- An HLA-specific reference subdirectory can be built by executing Copy dragen \ --build-hash-table true \ --ht-build-hla-hashtable=true \ --output-directory={REF-DIR} This command will create `anchored_hla` as a subdirectory of the target `{REF-DIR}` supplied as an argument to `--output-directory` as above. The HLA-specific reference subdirectory can be built at the same time as the primary reference construction. An example command-line for this mode is [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#hla-resource-fasta) HLA Resource FASTA ----------------------------------------------------------------------------------------------------------------------------------------------------------- An HLA resource file, `HLA_resource.v2.fasta.gz`, is packaged with DRAGEN. It is located at `/resources/hla/HLA_resource.v2.fasta.gz` This file is used by default when building the HLA-specific hash-table as above, see [Building the HLA-Specific Reference Subdirectory](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#building-he-hla-specific-reference-subdirectory) . ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#using-custom-hla-reference-files) Using Custom HLA Reference Files An HLA allele reference FASTA file can be used as input to the hash-table building option `--ht-hla-reference`. Note: Using custom HLA reference files to generate the HLA-specific reference subdirectory `anchored_hla` is not recommended, as accuracy cannot be guaranteed. Custom input FASTA files (which can be zipped or unzipped) must contain only HLA allele sequences, and all allele names must adhere to the HLA star-allele nomenclature¹, where the first character of each allele name indicates the HLA locus, e.g. A\*02:01:01:01. Allele names extracted from such a custom input file start at the first character of the allele name (to be preceded by character '>') and end at the last character of the name or until the first delimiter character '-' is reached. The following is an illustration of a valid HLA reference input file to option `--ht-hla-reference`: Custom HLA reference files might require customized memory allocation, which can be specified with an argument to the command-line option `--ht-hla-ext-table-alloc`. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#hla-caller-pipeline-options) HLA Caller Pipeline Options ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The HLA component has no additional user-settable command-line options. Note: this HLA component replaces prior workflows. See the appropriate guide for the DRAGEN software version being used in order to determine valid parameters. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#map-align-dragen-requirement-for-hla) Map-Align DRAGEN Requirement for HLA The HLA Caller requires the DRAGEN mapper-aligner to be enabled (enabled via option `--enable-map-align=true`, or through TSO500-batch options). [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#hla-output-files) HLA Output Files ------------------------------------------------------------------------------------------------------------------------------------------------------- The HLA Caller generates a tab-delimited output file for class I and, if enabled, class II alleles. Class I results contain six class I alleles, with two alleles per class I HLA gene (HLA-A, -B and -C), and class II results contain six class II alleles, with two alleles per class II HLA gene (HLA-DQA1, -DQB1, and -DRB1). Homozygous calls show identical alleles at the respective loci. The genotype output file is `.hla.tsv`, and it is located in the user-specified output directory. In tumor-only mode the output is stored to `.hla.tumor.tsv` file. In tumor-normal mode, two output genotype files are generated from tumor and normal samples: `.hla.tumor.tsv` and `.hla.tsv`. In all cases, the genotype file contains a header row with one column for each of the class I and/or class II alleles and a body row with the HLA type of each allele at two-field resolution. The following is an example output file produced by DRAGEN class I and II HLA typing: A1 A2 B1 B2 C1 C2 DQA11 DQA12 DQB11 DQB12 DRB11 DRB12 A\*26:01 A\*29:02 B\*44:02 B\*44:03 C\*05:01 C\*16:01 DQA1\*01:03 DQA1\*01:02 DQB1\*06:03 DQB1\*06:02 DRB1\*15:01 DRB1\*15:01 The HLA Caller generates two additional HLA files. * `.hla_metrics.csv`—Contains the number of reads supporting each allele result (individual reads may support multiple alleles), and the total number of HLA reads analyzed. * `.hla_2field_EM.tsv`—Contains the maximal likelihood output from the Expectation-Maximization step: a list of candidate alleles at two-field resolution and corresponding intermediate posterior probability. Internal checks for sufficient coverage at each HLA locus will trigger a warning message when fewer than 50 reads support any given allele call, or when fewer than 300 HLA reads are detected overall. In both settings, an allele call will still be attempted, but the results may be unreliable. An empty genotype call at a given HLA locus is returned when there are no reads supporting that locus. In this scenario, a warning message will indicate missing coverage. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#known-limitations) Known Limitations --------------------------------------------------------------------------------------------------------------------------------------------------------- * Map-align must be enabled for HLA (see [Map-Align DRAGEN Requirement for HLA](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#map-align-dragen-requirement-for-hla) ). As such, tumor-normal paired file inputs from BAM are not currently supported for HLA calling. * No HLA genotype will be returned with single-end DNA read inputs. * By default, DRAGEN only genotypes HLA alleles that have full-nucleotide sequence data in IMGT/HLA v3.45 and that have also been reported on the Allele Frequency Net database. As such, no partial alleles are currently called using the supplied resource reference FASTA file `HLA_resource.v2.fasta`. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#examples) Examples --------------------------------------------------------------------------------------------------------------------------------------- The HLA Caller accepts standard input files in FASTQ or BAM format. The following example command line uses FASTQ file inputs. The following example command line uses BAM file inputs (with map-align enabled). NOTE: the `--hla-enable-class-2` enables class II HLA typing. The following example command line uses tumor-normal paired file inputs from FASTQ. The following example command line activates HLA typing in a TSO500-solid run from FASTQ input. A TSO500-compatible reference\_directory is one which uses the same reference genome as in TSO i.e. hg19. The following example command line activates HLA typing in a TSO500-liquid run from FASTQ input. A TSO500-compatible reference\_directory is one which uses the same reference genome as in TSO i.e. hg19. ¹Marsh SG, et al. Nomenclature for factors of the HLA system, 2010. Tissue Antigens. 2010 75:291-455. [PreviousQC Metrics Reportingchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/qc-metrics-reporting) [NextBiomarkerschevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/biomarkers) Last updated 7 months ago Was this helpful? * [HLA Workflow](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#hla-workflow) * [Reference Requirement for HLA](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#reference-requirement-for-hla) * [Building the HLA-Specific Reference Subdirectory](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#building-the-hla-specific-reference-subdirectory) * [HLA Resource FASTA](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#hla-resource-fasta) * [Using Custom HLA Reference Files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#using-custom-hla-reference-files) * [HLA Caller Pipeline Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#hla-caller-pipeline-options) * [Map-Align DRAGEN Requirement for HLA](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#map-align-dragen-requirement-for-hla) * [HLA Output Files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#hla-output-files) * [Known Limitations](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#known-limitations) * [Examples](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/hla-typing#examples) Was this helpful? Copy dragen \ --build-hash-table true \ --ht-build-hla-hashtable=true \ --output-directory={REF-DIR} \ --ht-reference {PATH-TO}primary_reference.fasta Copy >A*01:01:01:30-full TCCCCAGACGCCGAGGATGGCCGTCATGGCGCC... >A*01:01:01:47-full TCCCCAGACGCCGAGGATGGCCGTCATGGCGCC... >A*01:01:01:76-full TCCCATTGGGTGTCGGGTTTCCAGAGAAGCCAA... >A*01:01:01:91-full TCCCCAGACGCCGAGGATGGCCGTCATGGCGCC... ... Copy dragen \ --enable-hla=true \ --enable-map-align=true \ --enable-sort=true \ --output-directory={output_directory} \ --output-file-prefix={prefix} \ --ref-dir={reference_directory} \ --RGID={read_group_ID} \ --RGSM={read_group_sample} \ -1 {fq1} \ -2 {fq2} \ Copy dragen \ --enable-hla=true \ --hla-enable-class-2=true \ --enable-map-align=true \ --enable-sort=true \ --output-directory={output_directory} \ --output-file-prefix={prefix} \ --bam-input={bam} \ --ref-dir={reference_directory} \ Copy dragen \ --enable-hla=true \ --enable-map-align=true \ --enable-sort=true \ --output-directory={output_directory} \ --output-file-prefix={prefix} \ --ref-dir={reference_directory} \ --tumor-fastq1={tumor_fq1} \ --tumor-fastq2={tumor_fq2} \ --RGID-tumor={tumor_group_ID} \ --RGSM-tumor={tumor_group_sample} \ -1 {normal_fq1} \ -2 {normal_fq2} \ --RGID={normal_group_ID} \ --RGSM={normal_group_sample} \ Copy dragen \ --tso500-solid-umi=true \ --tso500-solid-hla=true \ --fastq-file1={tumor_fq1} \ --fastq-file2={tumor_fq2} \ --RGID={read_group_ID} \ --RGSM={read_group_sample} \ --ref-dir={TSO500-compatible reference_directory} \ --output-directory={output_directory} \ --output-file-prefix={prefix} Copy dragen \ --tso500-liquid=true \ --tso500-liquid-hla=true \ --fastq-file1={tumor_fq1} \ --fastq-file2={tumor_fq2} \ --RGID={read_group_ID} \ --RGSM={read_group_sample} \ --ref-dir={TSO500-compatible reference_directory} \ --output-directory={output_directory} \ --output-file-prefix={prefix} --- # CheckFingerprint | DRAGEN v4.3 | DRAGEN ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/checkfingerprint#check-sample-identity-with-checkfingerprint) Check Sample Identity with CheckFingerprint CheckFingerprint is broadly based on Picard CheckFingerprint. CheckFingerprint will output LOD score to indicate whether all the genetic data between two files from the same individual or not. If LOD score is positive, those two samples come from the same individual. Otherwise, those two samples come from different individuals. In general, the sign of LOD in summary file should be consistent with Picard CheckFingerprint summary file, but the exact values may be different. Validation were done on whole-genome sequencing (WGS) data, mixing WGS samples and whole exon sequencing data. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/checkfingerprint#usage) Usage #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/checkfingerprint#modes) Modes The checks can run in one of two modes: * Read comparison mode. Aligned reads are compared with the expected VCF * VCF comparison mode. Output VCF is compared with the expected VCF #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/checkfingerprint#options) Options To enable CheckFingerprint module, the following command-line options are required. * `--enable-checkfingerprint true` * `--checkfingerprint-expected-vcf [path_to_expected_sample_vcf]` Read comparison mode is enabled by default. Read comparison mode is recommended to use for small dataset or whole exon sequencing data. To switch to VCF comparison mode, use the following options * `--checkfingerprint-enable-vcf-comparison true` * `--enable-variant-caller true` Vcf comparison mode is recommended to use for larger samples, such as whole-genome sequencing data with average 30 coverage or whole exon sequencing data. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/checkfingerprint#command-line-examples) Command-line Examples Read mode. Input BAM/FASTQ/CRAM, examine the individual reads in input sample, and compare individual reads with expected VCF file. VCF mode. Input BAM/FASTQ/CRAM, generate a VCF file first, and compare the VCF file with expected VCF file VCF mode. Input an observed VCF file, and compare observed VCF file with expected VCF file #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/checkfingerprint#inputs) Inputs The input files used by DRAGEN CheckFingerprint are: a) haplotype map (configuration files), b) FASTQ/BAM/CRAM (user input) or observed VCF file (user input), c) expected VCF file (user input). **a) Haplotype Map** Haplotype maps for hg19, hg38 and chm13 are files that are packaged with DRAGEN and automatically selected by the software. The haplotype map is a set of SNPs grouped into haplotyp blocks (also known as linkage disequilibrium blocks). SNPs in haplotye map is used as fingerprinting. The following columns are of interest: Field Description **NAME** SNP identifier **MAF** minor allele frequency **ANCHOR\_SNP** refers to the NAME of a SNP. SNPs with the same ANCHOR\_SNP have high linkage disequilibrium with each other. **b) Sample Input** Samples are input from bam/cram/fastq or observed vcf files. The following command-line example uses FASTQ input: The following command-line example uses vcf input: **c) Expected Vcf Input** Vcf output from dragen is recommended. It can contains multiple samples. Multiple sample vcfs can combine together and input here `--checkfingerprint-expected-vcf` Checkfingerprint calculates LOD between input sample (bam/cram/fastq or vcf) and each sample in expected\_vcf file. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/checkfingerprint#outputs) Outputs There are two main output files: * \[output-file-prefix\].CheckFingerprint.summary.txt : contains LOD scores between input sample and expected sample * \[output-file-prefix\].CheckFingerprint.detail.txt : contains LOD scores between individual SNPs. **CheckFingerprint.detail.txt example** **CheckFingerprint.summary.txt example** LOD\_EXPECTED\_SAMPLE is the LOD score between two samples #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/checkfingerprint#method-of-operation) Method of Operation CheckFingerprint calculates the LOD score to identify whether two samples are from the same individual or not. A positive value indicates those two samples are from the same individual. A negative value indicates two samples are not match. LOD is in logarithmic scale (base 10). Thus, a LOD of 4 indicates it is 10,000 more likely that data matches the genotypes than not. A score that is close to 0 is inconclusive that can result from low coverage or missing informative genotypes. The identity check takes advantage of haplotype blocks defined in configuration file (hg38\_nochr.map,hg19\_nochr.map). It can improve statistic power for identity detection by checking SNPs in haplotype blocks. In VCF mode, CheckFingerprint uses PL to estimate genotype probabilities. **Limitaions:** Currently, Vcf mode is designed for whole genome sequencing samples with 30 coverage; Read mode is designed for whole exome sequencing. Larger datasets may encounter timeout errors. Vcf mode is recommended for general use. Read mode should be used in isolation without other components enabled and should only be used if Vcf mode does not provide sufficient accuracy. [PreviousHigh Coverage Analysischevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/high-coverage) [NextPopulation Haplotyping (Beta)chevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/population-haplotyping) Last updated 7 months ago Was this helpful? * [Check Sample Identity with CheckFingerprint](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/checkfingerprint#check-sample-identity-with-checkfingerprint) * [Usage](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/checkfingerprint#usage) Was this helpful? Copy ./bin/dragen -r /staging/human/reference/hg38_alt_aware/DRAGEN/8 -b [bam] --output-directory [output_dir] \ --output-file-prefix [output_prefix] --enable-checkfingerprint true --checkfingerprint-expected-vcf [input_expected_vcf] Copy ./bin/dragen -r /staging/human/reference/hg38_alt_aware/DRAGEN/8 -b [bam] --output-directory [output_dir] \ --output-file-prefix [output_prefix] --enable-checkfingerprint true --checkfingerprint-expected-vcf [input_expected_vcf] \ --checkfingerprint-enable-vcf-comparison true --enable-variant-caller true Copy ./bin/dragen -r /staging/human/reference/hg38_alt_aware/DRAGEN/8 -b [bam] --output-directory [output_dir] \ --output-file-prefix [output_prefix] --enable-checkfingerprint true --checkfingerprint-expected-vcf [input_expected_vcf] \ --checkfingerprint-observed-vcf [input_observed_vcf] Copy dragen \ -r /staging/human/reference/hg38_alt_aware/DRAGEN/8 \ --fastq-file1 /staging/test/data/NA12878_R1.fastq \ --fastq-file2 /staging/test/data/NA12878_R2.fastq \ --output-directory /staging/test/output \ --output-file-prefix NA12878_dragen \ --RGID DRAGEN_RGID \ --RGSM NA12878 \ --enable-checkfingerprint true \ --checkfingerprint-expected-vcf [input_expected_vcf] \ --checkfingerprint-enable-vcf-comparison true \ --enable-variant-caller true Copy dragen \ -r /staging/human/reference/hg38_alt_aware/DRAGEN/8 \ --output-directory /staging/test/output \ --output-file-prefix NA12878_dragen \ --enable-checkfingerprint true \ --checkfingerprint-expected-vcf [input_expected_vcf] \ --checkfingerprint-observed-vcf [input_observed_vcf] \ Copy READ_GROUP EXPECTED_SAMPLE SNP SNP_ALLELES CHROM POSITION EXPECTED_GENOTYPE OBSERVED_GENOTYPE LOD OBS_A OBS_B IGNORE hg002 chr1:274 AG 1 908025 AG AA 0.0799204 0 0 IGNORE hg002 chr1:308 GA 1 916119 GG GG 0.350172 0 0 IGNORE hg002 chr1:473 CT 1 984039 CT CC 0.39524 0 0 Copy READ_GROUP EXPECTED_SAMPLE LL_EXPECTED_SAMPLE LL_RANDOM_SAMPLE LOD_EXPECTED_SAMPLE HAPLOTYPES_WITH_GENOTYPES HAPLOTYPES_CONFIDENTLY_CHECKED HAPLOTYPES_CONFIDENTLY_MATCHING HET_AS_HOM HOM_AS_HET HOM_AS_OTHER_HOM IGNORE hg002 -568.153 -105.25 -462.903 12558 122 49 51 15 7 --- # DNA Mapping | DRAGEN v4.3 | DRAGEN ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align#dna-mapping) DNA Mapping #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align#seed-density-option) Seed Density Option The _seed-density_ option controls how many (normally overlapping) primary seeds from each read the mapper looks up in its hash table for exact matches. The maximum density value of 1.0 generates a seed starting at every position in the read, ie, (L-K+1) K-base seeds from an L-base read. Seed density must be between 0.0 and 1.0. Internally, an available seed pattern equal or close to the requested density is selected. The sparsest pattern is one seed per 32 positions, or density 0.03125. * **Accuracy Considerations**\--Generally, denser seed lookup patterns improve mapping accuracy. However, for modestly long reads (eg, 50 bp+) and low sequencer error rates, there is little to be gained beyond the default 50% seed lookup density. * **Speed Considerations**\--Denser seed lookup patterns generally slow down mapping, and sparser seed patterns speed it up. However, when the seed mapping stage can run faster than the aligning stage, a sparser seed pattern does not make the mapper much faster. **Relationship to Reference Seed Interval** Functionally, a denser or sparser seed lookup pattern has an impact very similar to a shorter or longer reference seed interval (build hash table option `--ht-ref-seed-interval`). Populating 100% of reference seed positions and looking up 50% of read seed positions has the same effect as populating 50% of reference seed positions and looking up 100% of read seed positions. Either way, the expected density of seed hits is 50%. More generally, the expected density of seed hits is the product of the reference seed density (the inverse of the reference seed interval) and the seed lookup density. For example, if 50% of reference seeds are populated and 33.3% (1/3) of read seed positions are looked up, then the expected seed hit density should be 16.7% (1/6). DRAGEN automatically adjusts its precise seed lookup pattern to ensure it does not systematically miss the seed positions populated from the reference. For example, the mapper does not look up seeds matching only odd positions in the reference when only even positions are populated in the hash table, even if the reference seed interval is 2 and seed-density is 0.5. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align#map-orientations-option) Map Orientations Option The `--Mapper.map-orientations` option is used in mapping reads for bisulfite methylation analysis. It is set automatically based on the value set for `‑‑methylation-protocol`. The `--Mapper.map-orientations` option can restrict the orientation of read mapping to only forward in the reference genome, or only reverse-complemented. The valid values for `--map-orientations` are as follows. * 0--Either orientation (default) * 1--Only forward mapping * 2--Only reverse-complemented mapping If mapping orientations are restricted and paired end reads are used, the expected pair orientation can only be FR, not FF or RF. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align#seed-editing-options) Seed-Editing Options Although DRAGEN primarily maps reads by finding exact reference matches to short seeds, it can also map seeds differing from the reference by one nucleotide by also looking up single-SNP edited seeds. Seed editing is usually not necessary with longer reads (100 bp+), because longer reads have a high probability of containing at least one exact seed match. This is especially true when paired ends are used, because a seed match from either mate can successfully align the pair. But seed editing can, for example, be useful to increase mapping accuracy for short single-ended reads, with some cost in increased mapping time. The following options control seed editing: Seed Editing Options Command-Line Option Name Configuration File Option Name `--Mappper.seed-density` seed-density `-Mapper.edit-mode` edit-mode `--Mapper.edit-seed-num` edit-seed-num `--Mapper.edit-read-len` edit-read-len `--Mapper.edit-chain-limit` edit-chain-limit **edit-mode and edit-chain-limit** The edit-mode and edit-chain-limit options control when seed editing is used. The following four edit-mode values are available: Mode Description 0 No editing (default) 1 Chain length test 2 Paired chain length test 3 Full seed editing Edit mode 0 requires all seeds to match exactly. Mode 3 is the most expensive because every seed that fails to match the reference exactly is edited. Modes 1 and 2 employ heuristics to look up edited seeds only for reads most likely to be salvaged to accurate mapping. The main heuristic in edit modes 1 and 2 is a seed chain length test. Exact seeds are mapped to the reference in a first pass over a given read, and the matching seeds are grouped into chains of similarly aligning seeds. If the longest seed chain (in the read) exceeds a threshold edit-chain-limit, the read is judged not to require seed editing, because there is already a promising mapping position. Edit mode 1 triggers seed editing for a given read using the seed chain length test. If no seed chain exceeds `edit-chain-limit` (including if no exact seeds match), then a second seed mapping pass is attempted using edited seeds. Edit mode 2 further optimizes the heuristic for paired-end reads. If either mate has an exact seed chain longer than `edit-chain-limit`, then seed editing is disabled for the pair, because a rescue scan is likely to recover the mate alignment based on seed matches from one read. Edit mode 2 is the same as mode 1 for single-ended reads. **edit-seed-num and edit-read-len** For edit modes 1 and 2, when the heuristic triggers seed editing, these options control how many seed positions are edited in the second pass over the read. Although exact seed mapping can use a densely overlapping seed pattern, such as seeds starting at 50% or 100% of read positions, most of the value of seed editing can be obtained by editing a much sparser pattern of seeds, even a nonoverlapping pattern. Generally, if a user application can afford to spend some additional amount of mapping time on seed editing, a greater increase in mapping accuracy can be obtained for the same time cost by editing seeds in sparse patterns for a large number of reads, than by editing seeds in dense patterns for a small number of reads. Whenever seed editing is triggered, these two options request edit-seed-num seed editing positions, distributed evenly over the first edit-read-len bases of the read. For example, with 21-base seeds, edit-seed-num=6 and edit-read-len=100, edited seeds can begin at offsets {0, 16, 32, 48, 64, 80} from the 5' end, consecutive seeds overlapping by 5 bases. Because sequencing technologies often yield better base qualities nearer the (5') beginning of each read, this can focus seed editing where it is most likely to succeed. When a particular read is shorter than `edit-read-len`, fewer seeds are edited. Seed editing is more expensive when the reference seed interval (build hash table option ‑-ht‑ref-seed-interval) is greater than 1. For edit modes 1 and 2, additional seed editing positions are automatically generated to avoid missing the populated reference seed positions. For edit mode 3, the time cost can increase dramatically because query seeds matching unpopulated reference positions typically miss and trigger editing. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align#dna-aligning) DNA Aligning #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align#smith-waterman-alignment-scoring-settings) Smith-Waterman Alignment Scoring Settings The first stage of mapping is to generate seeds from the read and look for exact matches in the reference genome. These results are then refined by running full Smith-Waterman alignments on the locations with the highest density of seed matches. This well-documented algorithm works by comparing each position of the read against all the candidate positions of the reference. These comparisons correspond to a matrix of potential alignments between read and reference. For each of these candidate alignment positions, Smith-Waterman generates scores that are used to evaluate whether the best alignment passing through that matrix cell reaches it by a nucleotide match or mismatch (diagonal movement), a deletion (horizontal movement), or an insertion (vertical movement). A match between read and reference provides a bonus, on the score, and a mismatch or indel imposes a penalty. The overall highest scoring path through the matrix is the alignment chosen. The specific values chosen for scores in this algorithm indicate how to balance, for an alignment with multiple possible interpretations, the possibility of an indel as opposed to one or more SNPs, or the preference for an alignment without clipping. The default DRAGEN scoring values are reasonable for aligning moderate length reads to a whole human reference genome for variant calling applications. But any set of Smith-Waterman scoring parameters represents an imprecise model of genomic mutation and sequencing errors, and differently tuned alignment scoring values can be more appropriate for some applications. The following alignment options control Smith-Waterman Alignment: Command-Line Option Name Configuration File Option Name `--Aligner.global` `global` `--Aligner.match-score` `match-score` `--Aligner.match-n-score` `match-n-score` `--Aligner.mismatch-pen` `mismatch-pen` `--Aligner.gap-open-pen` `gap-open-pen` `--Aligner.gap-ext-pen` `gap-ext-pen` `--Aligner.unclip-score` `unclip-score` `--Aligner.no-unclip-score` `no-unclip-score` `--Aligner.aln-min-score` `aln-min-score` `--Aligner.min-score-coeff` `min-score-coeff` * **global** The `global` option (value can be 0 or 1) controls whether alignment is forced to be end-to-end in the read. When set to 1, alignments are always end-to-end, as in the Needleman-Wunsch global alignment algorithm (although not end-to-end in the reference), and alignment scores can be positive or negative. When set to 0, alignments can be clipped at either or both ends of the read, as in the Smith-Waterman local alignment algorithm, and alignment scores are nonnegative. Generally, `global=0` is preferred for longer reads, so significant read segments after a break of some kind (large indel, structural variant, chimeric read, and so forth) can be clipped without severely decreasing the alignment score. Setting global=1 might not have the desired effect with longer reads because insertions at or near the ends of a read can function as pseudoclipping. Also, with global=0, multiple (chimeric) alignments can be reported when various portions of a read match widely separated reference positions. Using `global=1` is sometimes preferable with short reads, which are unlikely to overlap structural breaks, unable to support chimeric alignments, and are suspected of incorrect mapping if they cannot align well end-to-end. Consider using the unclip-score option, or increasing it, instead ofsetting global=1, to make a soft preference for unclipped alignments. * **match-score** The `match-score` option specifies the score for a read nucleotide matching a reference nucleotide (A, C, G, or T), or matching a reference 2–3 nucleotide IUPAC-IUB code. Its value is an unsigned integer, from 0 to 15. match\_score=0 can only be used when global=1. A higher match score results in longer alignments, and fewer long insertions. * **match-n-score** The `match-n-score` option specifies the score for an aligned position where the read position and/or the reference position is an N code. This option is a signed integer, from -16 to 15. * **mismatch-pen** The `mismatch-pen` option is the penalty (negative score) for a read nucleotide mismatching any reference nucleotide or IUPAC-IUB code, except N. This option is an unsigned integer, from 0 to 63. A higher mismatch penalty results in alignments with more insertions, deletions, and clipping to avoid SNPs. * **gap-open-pen** The `gap-open-pen` option is the penalty (negative score) for opening a gap (ie, an insertion or deletion). This value is only for a 0-base gap. It is always added to the gap length times gap-ext-pen. This option is an unsigned integer, from 0 to 127. A higher gap open penalty causes fewer insertions and deletions of any length in alignment CIGARs, with clipping or alignment through SNPs used instead. * **gap-ext-pen** The `gap-ext-pen` option is the penalty (negative score) for extending a gap (ie, an insertion or deletion) by one base. This option is an unsigned integer, from 0 to 15. A higher gap extension penalty causes fewer long insertions and deletions in alignment CIGARs, with short indels, clipping, or alignment through SNPs used instead. * **unclip-score** The `unclip-score` option is the score bonus for an alignment reaching the beginning or end of the read. An end-to-end alignment receives twice this bonus. This option is an unsigned integer, from 0 to 127. A higher unclipped bonus causes alignment to reach the beginning and/or end of a read more often, where this can be done without too many SNPs or indels. A nonzero unclip-score is useful when global=0 to make a soft preference for unclipped alignments. Unclipped bonuses have little effect on alignments when global=1, because end-to-end alignments are forced anyway (although 2 × unclip-score does add to every alignment score unless no-unclip-score = 1). Note that, especially with longer reads, setting unclip-score much higher than gap-open-pen can have the undesirable effect of insertions at or near one end of a read being utilized as pseudoclipping, as happens with global=1 * **no-unclip-score** The `no-unclip-score` option can be 0 or 1. The default is 1. When no-unclip-score is set to 1, any unclipped bonus (unclip-score) contributing to an alignment is removed from the alignment score before further processing, such as comparison with aln-min-score, comparison with other alignment scores, and reporting in AS or XS tags. However, the unclipped bonus still affects the best-scoring alignment found by Smith-Waterman alignment to a given reference segment, biasing toward unclipped alignments When unclip-score > 0 causes a Smith-Waterman local alignment to extend out to one or both ends of the read, the alignment score stays the same or increases if no-unclip-score=0, whereas it stays the same or decreases if no-unclip-score=1. The default, no-unclip-score=1, is recommended when global=1, because every alignment is end-to-end, and there is no need to add the same bonus to every alignment. When changing no-unclip-score, consider whether aln-min-score should be adjusted. When no-unclip-score=0, unclipped bonuses are included in alignment scores compared to the aln-min-score floor, so the subset of alignments filtered out by aln-min-score can change significantly with no-unclip-score. * **aln-min-score** The `aln-min-score` option specifies a minimum acceptable alignment score. Any alignment results scoring lower are discarded. Increasing or decreasing aln-min-score can reduce or increase the percentage of reads mapped. This option is a signed integer (negative alignment scores are possible with global=0). aln-min-score also affects MAPQ estimates. The primary contributor to MAPQ calculation is the difference between the best and second-best alignment scores. A read's best alignment score is saved in the AS SAM tag, and the second-best score (if available) is saved in the XS tag. aln-min-score serves as the suboptimal alignment score if nothing higher was found except the best score. Therefore, increasing aln-min-score can decrease reported MAPQ for some low-scoring alignments. You can use the min-score-coeff option to adjust aln-min-score as a function of read length. * **min-score-coeff** The `min-score-coeff` option makes adjustments to `aln-min-score` per read base. When using the `min-score-coeff` and `aln-min-score` options together, you can define the minimum alignment score for each read as an affine function of read lengths. The minimum score for an N-base read is calculated as follows: `(min-score-coeff)\*N+(aln-min-score)` The `min-score-coeff` option is an integer ranging from –64 to 63.999. If the value is 0, then the minimum alignment score is fixed at aln-min-score for all read length. You can use positive values for `min-score-coeff` to allow shorter reads to match with lower alignment scores, but require longer reads to achieve higher scores. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align#paired-end-options) Paired-End Options DRAGEN can process paired-end data passed via a pair of FASTQ files or in a single interleaved FASTQ file. The hardware maps the two ends separately, and then determines a set of alignments that seem most likely to form a pair in the expected orientation and having roughly the expected insert size. The alignments for the two ends are evaluated for the quality of their pairing, with larger penalties for insert sizes far from the expected size. The following options control processing of paired-end data: * **Reorientation** The `pe-orientation` option specifies the expected paired-end orientation. Only pairs with this orientation can be flagged as proper pairs. Valid values are as follows: * 0--FR (default) * 1--RF * 2--FF * **unpaired-pen** For paired end reads, best mapping positions are determined jointly for each pair, according to the largest pair score found, considering the various combinations of alignments for each mate. A pair score is the sum of the two alignment scores minus a pairing penalty, which estimates the unlikelihood of insert lengths further from the mean insert than this aligned pair. The `unpaired-pen` option specifies how much alignment pair scores should be penalized when the two alignments are not in properly paired position or orientation. This option also serves as the maximum pairing penalty for properly paired alignments with extreme insert lengths. The unpaired-pen option is specified in Phred scale, according to its potential impact on MAPQ. Internally, it is scaled into alignment score space based on Smith-Waterman scoring parameters. * **pe-max-penalty** The `pe-max-penalty` option limits how much the estimated MAPQ for one read can increase because its mate aligned nearby. A paired alignment is never assigned MAPQ higher than the MAPQ that it would have received mapping single-ended, plus this value. By default, pe-max-penalty = mapq-max = 255, effectively disabling this limit. The key difference between `unpaired-pen` and `pe-max-penalty` is that `unpaired-pen` affects calculated pair scores and thus which alignments are selected and pe-max-penalty affects only reported MAPQ for paired alignments. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align#mean-insert-size-detection) Mean Insert Size Detection When working with paired-end data, DRAGEN must choose among the highest-quality alignments for the two ends to try to choose likely pairs. To make this choice, DRAGEN uses a skew normal insert model to evaluate the likelihood that a pair of alignments constitutes a pair. This model is based on the observation that common library preparation methods have insert-size distributions that are sometimes close to normal, but also sometimes clearly asymmetric, often skewing toward longer insert sizes. The skew normal insert model is used only for the DNA mode. If you know the statistics of your library prep for an input file (and the file consists of a single read group), you can specify the characteristics of the insert-length distribution: mean, standard deviation, shape (or skewness) and three quartiles. These characteristics can be specified with the `Aligner.pe-stat-mean-insert`, `Aligner.pe-stat-stddev-insert`, `Aligner.pe-stat-shape-insert`, `Aligner.pe-stat-quartiles-insert`, and `Aligner.pe-stat-mean-read-len` options. However, it is typically preferable to allow DRAGEN to detect these characteristics automatically. Dragen automatically samples the insert-length distribution. When the software starts execution, it runs a sample of up to 2,000,000 pairs through the aligner, calculates the distribution, and then uses the resulting statistics for evaluating all pairs in the input set. The DRAGEN host software reports the statistics in its stdout log in a report, as follows: Note that the `Mean`, `Standard deviation` and `Quartiles` reported above are the sample mean, standard deviation and quartiles calculated from the initial sample of up to 2,000,000 pairs, assuming a normal distribution. The sample mean and standard deviation are used to fit the parameters of a skew-normal distribution. A skew-normal distribution is defined by starting with an underlying normal distribution (whose mean we call `position` or `xi` and standard deviation we call `scale` or `omega`) and folding a varying portion of the probability mass from one side of the mean (e.g., left side) to the other (e.g., right) side. The portion folded varies smoothly, from 0% at the original mean, approaching 100% from the left tail to the right tail. A `shape` parameter which we call `alpha` controls how rapidly the folded fraction increases, and at `alpha=0` there is no folding and the distribution remains normal. In the standard output, we also include the command line options needed to reproduce the DRAGEN run with the same insert stat settings. Note that when specifying stats on the command line, the skew-normal `xi` value should be used for `Aligner.pe-stat-mean-insert`. The `omega` value should be used for `Aligner.pe-stat-stddev-insert`, and the `alpha` value should be used for `Aligner.pe-stat-shape-insert`. If `Aligner-pe-stat-shape-insert` is not specified on the command line, a default value of 0 is assumed. The insert length distribution for each sample is written to fragment\_length\_hist.csv. Each sample starts with the following lines These lines are followed by the histogram for the first ~2M read pairs for DNA (~100K read pairs for RNA). The histogram counts are aggregated across all read groups sharing the same sample id (`RGSM` field). When the number of sample pairs is very small, there is not enough information to characterize the distribution with high confidence. In this case, DRAGEN applies default statistics that specify a very wide insert distribution, which tends to admit pairs of alignments as proper pairs, even if they may lie tens of thousands of bases apart. In this situation, DRAGEN outputs a message, as follows: The small samples formula calculates standard deviation as follows: The default model is "standard deviation = 10000". If the first 2M reads are unmapped or if all pairs are improper pairs, then the standard deviation is set to 10000 and the mean and quartiles are set to 0. Note that the minimum value for standard deviation is 12, which is independent of the number of samples. Also, in the DNA mode when we have fewer than 1000 high quality alignments we revert to the normal distribution based insert model, because of insufficient number of samples to accurately estimate the parameters of the skew normal distribution. For RNA-Seq data, the insert size distribution is not normal due to pairs containing introns. The DRAGEN software estimates the distribution using a kernel density estimator to fit a long tail to the samples. This estimate leads to a more accurate mean and standard deviation for RNA-Seq data and proper pairing. DRAGEN writes detected paired-end stats into a tab-delimited log file in the output directory called .insert-stats.tab. This file contains the statistical distribution of detected insert sizes for each read group, including quartiles, mean, standard deviation, shape, minimum, and maximum. The information matches the standard-out report above. Additionally, the log file includes the minimum and maximum insert limits that DRAGEN applied for rescue scans. Note that the reported mean and standard deviation in this tab-limited log file are the `xi` and `omega` parameters of the skew-normal distribution. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align#rescue-scans) Rescue Scans For paired-end reads, where a seed hit is found for one mate but not the other, rescue scans hunt for missing mate alignments within a rescue radius of the mean insert length. Normally, the DRAGEN host software sets the rescue radius to 2.5 standard deviations of the empirical insert distribution. But in cases where the insert standard deviation is large compared to the read length, the rescue radius is restricted to limit mapping slowdowns. In this case, a warning message is displayed, as follows: Although the user can ignore this warning, or specify an intermediate rescue radius to maintain mapping speed, it is recommended to use 2.5 sigmas for the rescue radius to maintain mapping sensitivity. To disable rescue scanning, set max-rescues to 0. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align#output-options) Output Options DRAGEN can track multiple independent alignments for each read. These alignments include the optimal (primary) one, as well as those mapping different subsegments of the read, (chimeric/supplementary), and sub-optimal (secondary) mappings of the read to different areas of the reference. For DNA alignment by default, DRAGEN can emit one primary alignment for each read, up to three chimeric alignments (Aligner.supp-aligns=3), and no secondary alignments (Aligner.sec-aligns=0). The maximum user-specified value for supp-aligns or sec-aligns is 4095. You can use the following configuration options to control how many of each type of alignment to include in DRAGEN output. * **mapq-max** The `mapq-max` option specifies a ceiling on the estimated MAPQ that can be reported for any alignment, from 0 to 255. If the calculated MAPQ is higher, this value is reported instead. The default is 60. * **supp-aligns**, **sec-aligns** The `supp-aligns` and `sec-aligns` options restrict the maximum number of supplementary (ie, chimeric and SAM FLAG 0x800) alignments and secondary (ie, suboptimal and SAM FLAG 0x100) alignments, respectively, that can be reported for each read. A maximum of 4095 supplementary alignments and 4095 secondary alignments can be reported for any read, in addition to a primary alignment. High settings for these two options impact speed so it is advisable to increase only as needed. * **sec-phred-delta** The `sec-phred-delta` option controls which secondary alignments are emitted based on the alignment score relative to the primary reported alignment. Only secondary alignments with likelihood within this Phred value of the primary are reported. * **sec-aligns-hard** The `sec-aligns-hard` option suppresses the output of all secondary alignments if there are more secondary alignments than can be emitted. Set sec-aligns-hard to 1 to force the read to be unmapped when not all secondary alignments can be output. * **supp-as-sec** When the `supp-as-sec` option is set to 1, then supplementary (chimeric) alignments are reported with SAM FLAG 0x100 instead of 0x800. The default is 0. The supp-as-sec option provides compatibility with tools that do not support FLAG 0x800. * **hard-clips** The hard-clips option is used as a field of 3 bits, with values ranging from 0 to 7. The bits specify alignments, as follows: * Bit 0--primary alignments * Bit 1--supplementary alignments * Bit 2--secondary alignments Each bit determines whether local alignments of that type are reported with hard clipping (1) or soft clipping (0). The default is 6, meaning primary alignments use soft clipping and supplementary and secondary alignments use hard clipping. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align#mapping-with-alt-contigs) Mapping with ALT-contigs The GRCh38 human reference contains many more alternate haplotypes (ALT contigs) than previous versions of the reference. Generally, including ALT contigs in the mapping reference improves mapping and variant calling specificity, because misalignments are eliminated for reads matching an ALT contig but scoring poorly against the primary assembly. However, mapping with GRCh38's ALT contigs without special treatment can substantially degrade variant calling sensitivity in corresponding regions, because many reads align equally well to an ALT contig and to the corresponding position in the primary assembly. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align#masked-based-alt-awareness) Masked Based ALT-awareness The recomeneded and default approach for dealing with ALT-contigs in DRAGEN is masking regions of ALT contigs of high similarity to their corresponding primary contig. This approach is more accurate than liftover based ALT-awarness because there are many places where the "correct" or most useful liftover between a long ALT haplotype and the primary assembly is ambiguous. Incorrect liftover can produce dense clusters of mismapped reads and false variant calls. The base masking approach has the benefits of using ALT contigs without the negative consequences. Masked hash tables are built from a standard hg18 or hg38 FASTA that contains ALT contigs. The hash table builder will automatically mask regions of the ALT contigs with Ns. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align#liftover-based-alt-awarness) Liftover Based ALT-awarness With liftover based ALT-awareness, the mapper and aligner are aware of the liftover relationship between ALT contig positions and corresponding primary assembly positions. Seed matches within ALT contigs are used to obtain corresponding primary assembly alignments, even if the latter score poorly. Liftover groups are formed, each containing a primary assembly alignment candidate, and zero or more ALT alignment candidates that lift to the same location. Each liftover group is scored according to its best-matching alignments, taking properly paired alignments into account. The winning liftover group provides its primary assembly representative as the primary output alignment, with MAPQ calculated based on the score difference to the second-best liftover group. Emitting primary alignments within the primary assembly maintains normal aligned coverage and facilitates variant calling there. If the --Aligner.en-alt-hap-aln option is set to 1 and --Aligner.supp-aligns is greater than 0, then corresponding alternate haplotype alignments can also be output, flagged as supplementary alignments. DRAGEN requires ALT-Aware hash tables for any hg19 or GRCh38 reference where ALT contigs are detected. To disable this requirement in DRAGEN, set the --ht-alt-aware-validate option to false. The following is a comparison of alternative options for dealing with alternate haplotypes. * Mapping without ALT contigs in the reference: * False-positive variant calls result when reads matching an alternate haplotype misalign somewhere else. * Poor mapping and variant calling sensitivity where reads matching an ALT contig differ greatly from the primary assembly. * Mapping with ALT contigs but no ALT awareness: * False-positive variant calls from misaligned reads matching ALT contigs are eliminated. * Low or zero aligned coverage in primary assembly regions covered by alternate haplotypes, due to some reads mapping to ALT contigs. * Low or zero MAPQ in regions covered by alternate haplotypes, where they are similar or identical to the primary assembly. * Variant calling sensitivity is dramatically reduced throughout regions covered by alternate haplotypes. * Mapping with ALT contigs and ALT awareness: * False-positive variant calls from misaligned reads matching ALT contigs are eliminated. * Normal aligned coverage in regions covered by alternate haplotypes because primary alignments are to the primary assembly. * Normal MAPQs are assigned because alignment candidates in alternative haplotypes are not considered in competition. * Good mapping and variant calling sensitivity where reads matching an ALT contig differ greatly from the primary assembly. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align#dragen-multigenome-mapper) DRAGEN Multigenome Mapper The Multigenome Mapper in DRAGEN significantly improves the accuracy of mapping Illumina reads, particularly in challenging regions such as segmental duplications and other difficult to map regions. This advanced method leverages population haplotypes from pangenome references to incorporate additional variant information, constructing alternative haplotype paths that improve reads mapping. By offering these alternate paths, the Multigenome Mapper enables reads containing population-specific variants to align directly to their most likely genomic locations, reducing mapping ambiguity. This improved mapping also results in improved variant calling accuracy. When given a set of population variants (VCF) or haplotypes, the pangenome reference modification is categorized in the following types: * Alternate contigs represent population haplotypes. Alt-contigs can have a single variant or a combination of nearby phased variants. * Ambiguous codes (IUPAC codes) to represent SNPs. To improve alignment, it edits the reference FASTA with isolated population SNPs. * Haplotype database. An additional haplotype database is built and used to augment the reference FASTA with population variants. A multigenome based mapper algorithm is used to score read alignment according to the variants in this database. The DRAGEN pangenome hashtables are available to download from the [DRAGEN Software Support Site pagearrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) . [PreviousDRAGEN DNA Pipelinechevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline) [NextRead Trimmingchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming) Last updated 7 months ago Was this helpful? * [DNA Mapping](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align#dna-mapping) * [DNA Aligning](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align#dna-aligning) * [Mapping with ALT-contigs](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align#mapping-with-alt-contigs) * [DRAGEN Multigenome Mapper](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align#dragen-multigenome-mapper) Was this helpful? Copy Initial paired-end statistics detected for read group RGID, based on 39042 high quality pairs for FR orientation Quartiles (25 50 75) = 398 409 420 Mean = 410.192 Standard deviation = 14.1254 NOTE: DRAGEN's insert estimates include corrections for clipping (so they are not identical to TLEN) Skew-normal insert distribution applied: Position (xi) = 424.084 Scale (omega) = 19.8719 Shape (alpha) = -1.88125 To rerun with identical insert stats, specify: --Aligner.pe-stat-mean-insert=424.084 --Aligner.pe-stat-stddev-insert=19.8719 --Aligner.pe-stat-shape-insert=-1.88125 --Aligner.pe-stat-quartiles-insert="398 409 420" --Aligner.pe-stat-mean-read-len=101 Copy #Sample: sample name FragmentLength,Count Copy WARNING: Less than 28 high quality pairs found - standard deviation is calculated from the small samples formula Copy if samples < 3 then      standard deviation = 10000 else if samples < 28 then     standard deviation = 25 * (standard deviation + 1) / (samples - 2) end if if standard deviation < 12 then      standard deviation = 12 end if Copy Rescue radius = 220 Effective rescue sigmas = 0.5       WARNING: Default rescue sigmas value of 2.5 was overridden by host software!       The user may wish to set rescue sigmas value explicitly with --Aligner.rescue-sigmas --- # Multi Caller | DRAGEN v4.3 | DRAGEN [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/multi-caller#overview) Overview ----------------------------------------------------------------------------------------------------------------------------------------- The DRAGEN offering encompasses a multitude of bioinformatics tools and allows for rapid end-to-end analysis of NGS data. The most common workflow is running FASTQ data through the DRAGEN map/align component and streaming directly to the small variant caller. This eliminates the need for a user to construct a workflow from off-the-shelf tools, dealing with interfaces, unfortunate incompability issues, and external library dependencies. In this section, we expand on the capabilities of DRAGEN to ease the workflow needs of common bioinformatics analyses. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/multi-caller#component-model) Component Model ------------------------------------------------------------------------------------------------------------------------------------------------------- Most components in DRAGEN can be enabled or disabled independently. These are controlled by `enable-` flags on the command line. Based on which components are enabled, DRAGEN will resolve any inconsistencies (if applicable) and construct the desired workflow. Where possible, DRAGEN will run components in parallel to save time and compute costs. Some examples of the top level options are listed here: * enable-map-align * enable-sort * enable-duplicate-marking * enable-variant-caller * enable-cnv * enable-sv Each component has its own set of options which are used to configure the behavior of the component. These options typically control specific input settings, internal algorithm parameters, or output files and filtering criteria. Refer to the individual component sections for more details. As an example, a different BED file may be provided separately for each caller: * cnv-target-bed * sv-call-regions-bed * vc-target-bed Additionally, some options are shared amongst callers, such as `output-directory` and `sample-sex`. Each variant caller will also produce its own set of VCFs and metric output files. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/multi-caller#input-formats) Input Formats --------------------------------------------------------------------------------------------------------------------------------------------------- DRAGEN accepts the following common standard NGS input formats: * FASTQ (`fastq-file1` and `fastq-file2`) * FASTQ List (`fastq-list`) * BAM (`bam-input`) * CRAM (`cram-input`) Somatic workflows can use tumor equivalent input files (eg, `tumor-bam-input`). When running from unaligned reads, the reads first go through the map/align component to produce alignments which continue downstream to the variant callers. When running from prealigned reads, the user has the choice to re-align with the DRAGEN map/align component or to use the existing alignments from the source input. It is common to run with `enable-map-align false` if you already have DRAGEN alignments available in BAM or CRAM format. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/multi-caller#multicaller-command-line) Multicaller Command Line ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- For most scenarios, simply creating the union of the command line options from the single caller scenarios will work. In this section we outline some best practices for doing so. * Configure the INPUT options * Configure the OUTPUT options * Configure MAP/ALIGN depending on if realignment is desired or not * Configure the VARIANT CALLERs based on the application * Build up the necessary options for each component separately, so that it can be re-used in the final command line. The following are partial templates that can be used as starting points. Adjust them accordingly for your specific use case. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/multi-caller#germline) Germline ----------------------------------------------------------------------------------------------------------------------------------------- The following table summarizes the support for different input formats and variant callers. GERMLINE FASTQ w/ Map/Align BAM/CRAM BAM/CRAM w/ Map/Align CNV+SNV Supported Supported Supported CNV+SV Supported Supported Supported SNV+SV Supported Supported Supported CNV+SNV+SV Supported Supported Supported For brevity, other features and callers are not listed in the table even though they may be supported. Examples include repeat genotyping, SMA, CYP2D6, and ploidy calling. DRAGEN can run all germline callers for WGS analysis in a single command line (CNV + SNV + SV + ...). Similar support also exists for WES analysis, if the component is supported in single caller mode and there is no conflict with the input configurations. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/multi-caller#somatic) Somatic --------------------------------------------------------------------------------------------------------------------------------------- The somatic workflows can be constructed in a similar manner by specifying tumor and normal inputs. The need for potentially two input files (tumor and matched normal) as well as the need for a matched normal SNV VCF for the Somatic CNV caller means extra care has to be taken. One recommended tumor/normal workflow first starts with running the matched normal through the Germline Workflow. 1. Run matched normal through Germline workflow (CNV + SNV + SV + ...). This is required to first generate the matched normal SNV VCF. See the Somatic CNV section for more details. 2. Run tumor and matched normal through Somatic workflow (CNV + SNV + SV + ...) Optionally, a full tumor/normal analysis can be done in a single execution if both the SNV and CNV modules are enabled, by leveraging the BAF information directly from the small variant caller. See the Somatic CNV section for more details. In brief, this requires the use of `--enable-variant-caller true` and `--cnv-use-somatic-vc-baf true`. The following table lists the various combinations that are supported under the tumor/normal mode of operation. TUMOR NORMAL FASTQ w/ Map/Align BAM/CRAM BAM/CRAM w/ Map/Align CNV+SNV Supported Supported Not Supported CNV+SV Supported Supported Not Supported SNV+SV Supported Supported Not Supported CNV+SNV+SV Supported Supported Not Supported Running in tumor only mode just requires removing the matched normal input from the `INPUT` options and configuring each individual caller to run in tumor only mode (for example, CNV uses a population B-allele VCF instead of the matched normal SNP VCF). The following table lists the combinations that are supported under the tumor only mode of operation. TUMOR ONLY FASTQ w/ Map/Align BAM/CRAM BAM/CRAM w/ Map/Align CNV+SNV Supported Supported Supported CNV+SV Supported Supported Supported SNV+SV Supported Supported Supported CNV+SNV+SV Supported Supported Supported These modes are for WGS analysis. Similar support also exists for WES analysis, if the mode is supported in single caller mode and there is no conflict in the input configurations. For WES analysis, note that CNV requires a panel of normals regardless of whether it is Tumor Normal or Tumor Only analysis. [PreviousPloidy Callerchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/ploidy-calling/ploidy-caller) [NextQC Metrics Reportingchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/qc-metrics-reporting) Last updated 7 months ago Was this helpful? * [Overview](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/multi-caller#overview) * [Component Model](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/multi-caller#component-model) * [Input Formats](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/multi-caller#input-formats) * [Multicaller Command Line](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/multi-caller#multicaller-command-line) * [Germline](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/multi-caller#germline) * [Somatic](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/multi-caller#somatic) Was this helpful? Copy #!/bin/bash set -euo pipefail DRAGEN_HASH_TABLE= FASTQ1= FASTQ2= RGSM= RGID= OUTPUT= PREFIX= INPUT_OPTIONS=" --ref-dir $DRAGEN_HASH_TABLE \ --fastq-file1 $FASTQ1 \ --fastq-file2 $FASTQ2 \ --RGSM $RGSM \ --RGID $RGID \ " OUTPUT_OPTIONS=" --output-directory $OUTPUT \ --output-file-prefix $PREFIX \ " MA_OPTIONS=" --enable-map-align true \ ... \ " CNV_OPTIONS=" --enable-cnv true \ ... \ " SNV_OPTIONS=" --enable-variant-caller true \ ... \ " SV_OPTIONS=" --enable-sv true \ ... \ " CMD=" dragen \ $INPUT_OPTIONS \ $OUTPUT_OPTIONS \ $MA_OPTIONS \ $CNV_OPTIONS \ $SNV_OPTIONS \ $SV_OPTIONS \ " echo $CMD bash -c $CMD Copy #!/bin/bash set -euo pipefail DRAGEN_HASH_TABLE= TUMOR_BAM= NORMAL_BAM= OUTPUT= PREFIX= INPUT_OPTIONS=" --ref-dir $DRAGEN_HASH_TABLE \ --tumor-bam-input $TUMOR_BAM \ --bam-input $NORMAL_BAM \ " OUTPUT_OPTIONS=" --output-directory $OUTPUT \ --output-file-prefix $PREFIX \ " MA_OPTIONS=" --enable-map-align false \ ... \ " CNV_OPTIONS=" --enable-cnv true \ ... \ " SNV_OPTIONS=" --enable-variant-caller true \ ... \ " SV_OPTIONS=" --enable-sv true \ ... \ " CMD=" dragen \ $INPUT_OPTIONS \ $OUTPUT_OPTIONS \ $MA_OPTIONS \ $CNV_OPTIONS \ $SNV_OPTIONS \ $SV_OPTIONS \ " echo $CMD bash -c $CMD --- # DRAGEN Recipes | DRAGEN v4.3 | DRAGEN [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes#overview) Overview ----------------------------------------------------------------------------------------------------------------------- The following sub-pages contain recommended command line options for specific DRAGEN pipelines. For an overview of DRAGEN command line parsing, also see [Multicaller Workflows](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/multi-caller) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes#germline-pipelines) Germline Pipelines * [DNA Germline Panel UMI](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi) * [DNA Germline Panel](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel) * [DNA Germline WES UMI](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wes-umi) * [DNA Germline WES](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wes) * [DNA Germline WGS UMI](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs-umi) * [DNA Germline WGS](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes#rna-and-scrna-pipelines) RNA and scRNA Pipelines * [RNA Panel](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-panel) * [RNA WTS](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-wts) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes#somatic-pipelines) Somatic Pipelines * [DNA Somatic Tumor-Normal Solid Panel UMI](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-somatic-tumor-normal-solid-panel-umi) * [DNA Somatic Tumor-Normal Solid Panel](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-somatic-tumor-normal-solid-panel) * [DNA Somatic Tumor-Normal Solid WES UMI](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-somatic-tumor-normal-solid-wes-umi) * [DNA Somatic Tumor-Normal Solid WES](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-somatic-tumor-normal-solid-wes) * [DNA Somatic Tumor-Normal Solid WGS UMI](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-somatic-tumor-normal-solid-wgs-umi) * [DNA Somatic Tumor-Normal Solid WGS](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-somatic-tumor-normal-solid-wgs) * [DNA Somatic Tumor-Only Heme WGS](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-somatic-tumor-only-heme-wgs) * [DNA Somatic Tumor-Only Solid Panel UMI](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-somatic-tumor-only-solid-panel-umi) * [DNA Somatic Tumor-Only Solid Panel](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-somatic-tumor-only-solid-panel) * [DNA Somatic Tumor-Only Solid WES UMI](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-somatic-tumor-only-solid-wes-umi) * [DNA Somatic Tumor-Only Solid WES](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-somatic-tumor-only-solid-wes) * [DNA Somatic Tumor-Only Solid WGS UMI](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-somatic-tumor-only-solid-wgs-umi) * [DNA Somatic Tumor-Only Solid WGS](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-somatic-tumor-only-solid-wgs) * [DNA Somatic Tumor-Only ctDNA Panel UMI](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-somatic-tumor-only-ctdna-panel-umi) [PreviousKmer Classifier Database Builderchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/pipeline/kmer-class-db-builder) [NextDNA Germline Panel UMIchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi) Last updated 7 months ago Was this helpful? * [Overview](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes#overview) * [Germline Pipelines](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes#germline-pipelines) * [RNA and scRNA Pipelines](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes#rna-and-scrna-pipelines) * [Somatic Pipelines](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes#somatic-pipelines) Was this helpful? --- # DRAGEN Secondary Analysis | DRAGEN The DRAGEN secondary analysis software utilizes a highly reconfigurable Field Programmable Gate Array (FPGA) card and is available on a preconfigured DRAGEN server that can be seamlessly integrated into bioinformatics workflows. The platform can be loaded with highly optimized algorithms for many different NGS secondary analysis pipelines, including the following: * Whole genome * Exome * RNA-Seq * Methylome * Cancer All user interaction is accomplished via DRAGEN software that runs on the host server and manages all communication with the FPGA card. This user guide summarizes the technical aspects of the system and provides detailed information for all DRAGEN command line options. If you are working with DRAGEN for the first time, Illumina recommends that you first read the _Getting Started section_, which provides a short introduction to DRAGEN, including running a test of the server, generating a reference genome, and running example commands. [hashtag](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-host-software/dragen-platform#dna-pipeline) DNA Pipeline ------------------------------------------------------------------------------------------------------------------------------------------ DRAGEN DNA Pipeline ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F25033470-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FG9szlFZupV6Q2DasL98y%252Fuploads%252Fgit-blob-2e515ef08d8202f152485297d4a5ec4d2ab1f5b0%252Fdragen-platform.DNA_Pipeline.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=1d39b493&sv=2) The DRAGEN DNA Pipeline massively accelerates the secondary analysis of NGS data. For example, the time taken to process an entire human genome at 30x coverage is reduced from approximately 10 hours (using the current industry standard, BWA-MEM+GATK-HC software) to approximately 20 minutes. Time scales linearly with coverage depth. These pipelines harness the tremendous power of the DRAGEN server and include highly optimized algorithms for mapping, aligning, sorting, duplicate marking, and haplotype variant calling. They also use platform features such as hardware-accelerated compression and optimized BCL conversion, together with the full set of platform tools. Unlike all other secondary analysis methods, DRAGEN DNA Applications do not reduce accuracy to achieve speed improvements. Accuracy for both SNPs and INDELs is improved over that of BWA-MEM+GATK-HC in side-by-side comparisons. In addition to haplotype variant calling, the pipeline supports calling of copy number and structural variants as well as detection of repeat expansions. [hashtag](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-host-software/dragen-platform#rna-pipeline) RNA Pipeline ------------------------------------------------------------------------------------------------------------------------------------------ DRAGEN secondary anaylsis includes an RNA-seq (splicing-aware) aligner, as well as RNA-specific analysis components for gene expression quantification and gene fusion detection. ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F25033470-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FG9szlFZupV6Q2DasL98y%252Fuploads%252Fgit-blob-b7f556bf8783fff0e54fd33c48677f50efe302dd%252Fdragen-platform.RNA_Pipeline.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=37cbd772&sv=2) The DRAGEN RNA Pipeline shares many components with the DNA Pipeline. Mapping of short seed sequences from RNA-Seq reads is performed similarly to mapping DNA reads. In addition, splice junctions (the joining of noncontiguous exons in RNA transcripts) near the mapped seeds are detected and incorporated into the full read alignments. DRAGEN secondary analysis uses hardware accelerated algorithms to map and align RNA-Seq--based reads faster and more accurately than popular software tools. For instance, it can align 100 million paired-end RNA-Seq--based reads in about three minutes. With simulated benchmark RNA-Seq data sets, its splice junction sensitivity and specificity are unsurpassed. [hashtag](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-host-software/dragen-platform#methylation-pipeline) Methylation Pipeline ---------------------------------------------------------------------------------------------------------------------------------------------------------- The DRAGEN Methylation Pipeline provides support for automating the processing of bisulfite sequencing data to generate a BAM with the tags required for methylation analysis and reports detailing the locations with methylated cytosines. [PreviousDRAGEN Host Softwarechevron-left](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-host-software) [NextDRAGEN Appschevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-apps) Last updated 21 days ago Was this helpful? * [DNA Pipeline](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-host-software/dragen-platform#dna-pipeline) * [RNA Pipeline](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-host-software/dragen-platform#rna-pipeline) * [Methylation Pipeline](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-host-software/dragen-platform#methylation-pipeline) Was this helpful? --- # Star Allele Caller | DRAGEN v4.3 | DRAGEN [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller#overview) Overview ----------------------------------------------------------------------------------------------------------------------------------------------- The Star Allele Caller identifies the genotypes and metabolism status of the following PGx genes that are included in [FDA's PGx recommendationsarrow-up-right](https://www.fda.gov/medical-devices/precision-medicine/table-pharmacogenetic-associations) or have [CPIC Level A designationarrow-up-right](https://cpicpgx.org/genes-drugs/) : CACNA1S, CFTR, CYP2C19, CYP2C9, CYP3A4, CYP3A5, CYP4F2, IFNL3, RYR1, NUDT15, SLCO1B1, TPMT, UGT1A1, VKORC1, DPYD, G6PD, MT-RNR1, BCHE, ABCG2, NAT2, F5 and UGT2B17. It finds optimal genotypes for the above genes, based on star allele definitions from resources listed below. It calls metabolism status based on a PharmCAT resource file that provides mappings between genotypes and phenotypes. The file is [herearrow-up-right](https://github.com/PharmGKB/PharmCAT/blob/aeecfe5f787e95dfb31ede62884e287affef45b3/src/main/resources/org/pharmgkb/pharmcat/definition/gene_phenotypes.json) . The primary support for the Star Allele Caller is for human reference hg38 for which it supports the above mentioned genes. Additionally, it also supports the following genes on references hg19 and GRCh37 : CACNA1S, CYP2C19, CYP2C9, CYP3A4, CYP3A5, CYP4F2, IFNL3, NUDT15, SLCO1B1, VKORC1, DPYD, ABCG2, F5. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller#star-allele-definition-resources-for-hg38) Star allele definition resources for hg38 ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- For genes CACNA1S, CFTR, CYP2C19, CYP2C9, CYP3A4, CYP3A5, CYP4F2, IFNL3, RYR1, NUDT15, SLCO1B1, TPMT, UGT1A1, VKORC1, DPYD, G6PD, MT-RNR1, ABCG2 the allele definitions are sourced from PharmGKB which are found [herearrow-up-right](https://www.pharmgkb.org/page/pgxGeneRef) . For BCHE and NAT2, the alleles are sourced from [thisarrow-up-right](https://www.dovepress.com/getfile.php?fileID=61995) paper and [thisarrow-up-right](https://api.pharmgkb.org/v1/download/submission/1447964753) website, respectively. For UGT2B17, the star alleles are defined [herearrow-up-right](https://www.pharmacogenomics.pha.ulaval.ca/wp-content/uploads/2015/04/HAP-UGT2B17.htm) . Note that since BCHE does not have defined star alleles, the Star Allele Caller checks if a sample is positive for any of the variants that are reported in the paper. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller#star-allele-definition-resources-for-hg19-grch37) Star allele definition resources for hg19/GRCh37 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- For genes CYP2C19, CYP2C9, CYP3A4, CYP3A5, CYP4F2, NUDT15, SLCO1B1, DPYD, the definitions are sourced from PharmVAR and can be found [herearrow-up-right](https://www.pharmvar.org/genes) . For the remaining hg19/GRCh37 genes, i.e., ABCG2, CACNA1S, IFNL3, F5 and VKORC1 - the allele definitions have been lifted from their corresponding definitions for hg38 (which are sourced from PharmGKB as noted above). [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller#functionality) Functionality --------------------------------------------------------------------------------------------------------------------------------------------------------- The Star Allele Caller has the following features. * It calls star allele genotypes from different types of genomic data like FASTQ, BAM, gVCF, VCF. * It provides additional details about the genotype call, including a confidence score. * It assumes genotypes for missing positions to be ref - these positions are listed in the output. * It assumes filtered genotype calls to be ref - these records are also listed in the output. * If multiple optimal diplotypes are satisfied, then it lists them all. * It supports different versions of the human reference hg38, hg19 and GRCh37. * For the genes UGT2B17 and CYP2C19, the caller analyzes CNV calls to detect star alleles. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller#input-files-and-command-line-examples) Input files and command line examples --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The Star Allele Caller can accept as input, different forms of sequence data such as FASTQs files, BAM/CRAM files or gVCF/VCF files. If small variant VCF/gVCF and CNV-VCF files are used as input, they should meet the following specifications. * Must be aligned to the same human reference that is passed through the -r option. * Variants should follow a parsimonious left aligned variant representation format. * Complex variants - for example, representing closely located, independent variants, in a single record - are NOT supported. Note that VCF/gVCF files can also be substituted with, a compressed GZ file (i.e. `.vcf.gz` or `.gvcf.gz`). For running the caller, the human reference needs to be always passed as a command line option. The Star Allele Caller detects the reference version (i.e., hg19, GRCh37 or hg38) and accordingly reads in the correct allele definitions. The Star allele caller can be enabled in parallel with other components as part of a WGS germline analysis workflow using the option `--enable-pgx` (see [DRAGEN Recipe - Germline WGS](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs) ) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller#command-line-with-gvcf-input) Command line with gVCF input In the simplest case, the caller takes DRAGEN gVCF and DRAGEN CNV-VCF files as input. The following is an example of the command line for the basic use case. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller#command-line-with-vcf-input) Command line with VCF input Contrary to a variant-only VCF file, a DRAGEN gVCF file contains the genotypes for all positions in a genome. Although the gVCF format is the preferred format for the caller, it can also accept a standard variant-only VCF file as input. The command line for this case will be the same as above, with the VCF file passed instead of a gVCF file. Also, the CNV-VCF file is optional - in this case the Star Allele Caller will not call star alleles that are detected through CNV analysis. An example of this use case, with only a variant only VCF file as input, is as follows. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller#command-line-with-bam-input) Command line with BAM input For running the Star Allele Caller from a BAM input, the variant caller also needs to be enabled. Optionally, the CNV caller should also be preferably enabled for analyzing CNV star alleles. An example of the command line for this use case is as follows. **Note that the Star Allele Caller supports force genotyping option of the variant caller (set by** `**--vc-forcegt-vcf**`**) but other variant caller options, such as combining phased variants (set using** `**--vc-combine-phased-variants-distance**`**), is NOT supported at this time.** ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller#command-line-with-fastq-input) Command line with FASTQ input If a FASTQ file is used as input, additional options, `--RGID` and `--RGSM` need to be set in the command line. An example of the command line for this use case as follows. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller#output-files) Output files ------------------------------------------------------------------------------------------------------------------------------------------------------- Following completion of the DRAGEN Star Allele Caller run, the following output files are produced. 1. When the Star Allele Caller is run with small variant calling, or directly from genome VCF input, then the main output file, `.targeted.json` contains the complete and detailed results for all genes. This is an example output for one gene `DPYD` and for one sample `NA19374`. The fields in the json file are as follows. * "genomeBuild": Reference version being used * "softwareVersion": Version of DRAGEN being run * "sampleId": Sample name * "phenotypeDatabaseSources": Resources used for calling metabolism status (phenotype) * "starAlleleDatabaseSources": Resources used for identifying star alleles (genotype) * "locusAnnotations": List of star allele caller results, one for each gene * "gene": Gene name * "geneId": HGNC or Ensembl id of the gene that is static * "starAlleleDatabaseSource": Resource for the star allele definitions file * "genotype": The detected star allele diplotype (or haplotype for haploid gene) * "genotypeQuality": Phred scaled quality score for the genotype * "phenotypeDatabaseAnnotation": Metabolism status corresponding to the genotype called * "supportingVariants": List of star alleles that are satisfied by found variants. The id field denotes the name of the star allele. Each non-ref star allele has a list of supportingVariants which displays the variant details (same as from the small variant vcf file. The quality field denotes the gq field from the vcf record) * "missingVariantSites": List of relevant gene sites for which vcf records are missing or filtered * "variantStarAllelesFound": List of star allele haplotypes that are satisfied by the found variants Each Star allele genotype contains one or two haplotypes (a haplotype for chrM gene MT-RNR1 and chrX gene G6PD for male samples, and a diplotype for all other genes) separated by a slash (e.g. `*1/*2`). Each haplotype is a pre-defined star allele and the definitions can be found under the allele definitions URL. Note that there may be some variance to star allele definitions and notations based on the resource and when it was last updated. When the Star Allele Caller cannot identify an optimal genotype for a gene, a no-call (`./.` or `.`) is made. In certain cases, more than one genotype is optimally satisfied, in that case all satisfied genotypes are listed, separated by a semi-colon (e.g. `*1/*2;*3/*4`). 1. Tsv and json files (`.star_allele.tsv` and `.star_allele.json`, respectively) are produced when the Star Allele Caller is run stand-alone from a gvcf or vcf file or if the option `--targeted-enable-legacy-output` is set. The json file has the same format as `.targeted.json` (shown above) while the tsv file contains summarized star allele calls for each gene. This is an example for one gene from the tsv output. The fields are gene name and genotype. [PreviousIndel Re-aligner (Beta)chevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/indel-realigner) [NextHigh Coverage Analysischevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/high-coverage) Last updated 7 months ago Was this helpful? * [Overview](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller#overview) * [Star allele definition resources for hg38](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller#star-allele-definition-resources-for-hg38) * [Star allele definition resources for hg19/GRCh37](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller#star-allele-definition-resources-for-hg19-grch37) * [Functionality](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller#functionality) * [Input files and command line examples](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller#input-files-and-command-line-examples) * [Command line with gVCF input](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller#command-line-with-gvcf-input) * [Command line with VCF input](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller#command-line-with-vcf-input) * [Command line with BAM input](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller#command-line-with-bam-input) * [Command line with FASTQ input](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller#command-line-with-fastq-input) * [Output files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/star-allele-caller#output-files) Was this helpful? Copy dragen \ -r /staging/human/reference/hg38_alt_aware/DRAGEN/${HASH_TABLE_VERSION} \ --star-allele-gvcf /staging/test/data/NA12878.gvcf \ --star-allele-cnv-vcf /staging/test/data/NA12878.cnv.vcf.gz \ --output-directory /staging/test/output \ --output-file-prefix NA12878_dragen \ --enable-star-allele true Copy dragen \ -r /staging/human/reference/hg38_alt_aware+cnv+hla+rna_v2/DRAGEN/${HASH_TABLE_VERSION} \ --star-allele-gvcf /staging/test/data/NA12878.vcf \ --output-directory /staging/test/output \ --output-file-prefix NA12878_dragen \ --enable-star-allele true Copy dragen \ -r /staging/human/reference/hg38_alt_aware/DRAGEN/${HASH_TABLE_VERSION} \ --bam-input /staging/test/data/NA12878.bam \ --output-directory /staging/test/output \ --output-file-prefix NA12878_dragen \ --enable-map-align false \ --enable-star-allele true \ --enable-variant-caller true \ --vc-emit-ref-confidence gvcf \ --enable-cnv true \ --cnv-enable-self-normalization true Copy dragen \ -r /staging/human/reference/hg38_alt_aware+cnv+hla+rna_v2/DRAGEN/${HASH_TABLE_VERSION} \ -1 /scratch/NA11829.fq1.gz \ -2 /scratch/NA11829.fq2.gz \ --RGID DRAGEN_RGID \ --RGSM DRAGEN_RGSM \ --enable-map-align true \ --output-directory /staging/test/output \ --output-file-prefix NA11829 \ --enable-star-allele true \ --enable-variant-caller true \ --vc-emit-ref-confidence gvcf \ --enable-cnv true \ --cnv-enable-self-normalization true Copy { "genomeBuild": "hg38", "softwareVersion": "dragen v4.4.0-52-g09190b26", "sampleId": "HG00236", "phenotypeDatabaseSources": [\ "PharmCAT Phenotypes Version: Snapshot-2022.09.15"\ ], "starAlleleDatabaseSources": [\ "PharmGKB Database Version: Snapshot-2022.01.01",\ "PharmGKB Database Version: Snapshot-2022.03.01",\ "UGT Nomenclature Committee Version: Snapshot-01.01.2023",\ "Zhu et al. 2020, PMID: 33061533"\ ], "locusAnnotations": [\ {\ "gene": "CYP3A5",\ "geneId": "HGNC:2638",\ "starAlleleDatabaseSource": "PharmGKB Database Version: Snapshot-2022.01.01",\ "genotype": "*3/*3",\ "genotypeQuality": 43,\ "phenotypeDatabaseAnnotation": "Poor Metabolizer",\ "supportingVariants": [\ {\ "alleleId": "*3",\ "chrom": "chr7",\ "pos": 99672916,\ "ref": "T",\ "alt": "C,",\ "gt": "1/1",\ "quality": 43\ }\ ],\ "variantStarAllelesFound": "*3",\ "missingVariantSites": []\ }\ {\ "gene": "F5",\ "geneId": "HGNC:3542",\ "starAlleleDatabaseSource": "PharmGKB Database Version: Snapshot-2022.01.01",\ "genotype": "rs6025reference(C)/rs6025reference(C)",\ "genotypeQuality": 0,\ "phenotypeDatabaseAnnotation": null,\ "supportingVariants": [],\ "variantStarAllelesFound": "",\ "missingVariantSites": [\ {\ "id": "169549811:C:T",\ "alleleIds": "rs6025variant(T)"\ }\ ]\ },\ \ Copy\ \ UGT1A1 *36/*80+*37 --- # Read Trimming | DRAGEN v4.3 | DRAGEN DRAGEN can remove artifacts from reads using hardware accelerated read trimming. Hardware accelerated read trimming is available on U200 and cloud systems, as part of the DRAGEN mapper and adds no additional run time. DRAGEN provides multiple independent trimming filters that target different types of artifacts or use cases. You can enable and configure the artifacts or use cases independently to tailor the read-trimming to your analysis. Read trimming uses two different modes, hard-trimming and soft-trimming. To enable hard-trimming mode, use `--read-trimmers`. In hard-trimming mode, potential artifacts are removed from input reads. Reads that are trimmed to fewer than 20 bases are filtered and replaced with a placeholder read that uses 10 N bases. DRAGEN assigns the filtered reads a 0x200 flag set. DRAGEN contains a novel lossless soft-trimming mode. In soft-trimming mode, reads are mapped as though they had been trimmed, but no bases are removed. To enable the trimmer in soft mode, use `--soft-read-trimmers`. Soft-trimming suppresses systematic mismapping of reads that contain trimmable artifacts, without actually losing the trimmed bases in aligned output. Soft-trimming prevents reads with trimmable artifacts, such as Poly-G artifacts, from being mapped to reference G homopolymers, or prevents adapter sequences from being mapped to the matching reference loci. Soft-trimming might map reads to different positions in the reference than they would have been if not using soft-trimming. When using soft-trimmed, DRAGEN does not filter reads and does not map reads with bases that would have been trimmed entirely. Soft-trimming for Poly-G artifacts is enabled by default on supported systems. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#read-trimming-tools) Read Trimming Tools ---------------------------------------------------------------------------------------------------------------------------------------------------------------- ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#fixed-length-trimming) Fixed-Length Trimming Fixed-length trimming removes a fixed number of bases from the 5' end of each read. If you are analyzing sequencing data from an amplicon of fixed size and expect the read-length to consistently exceed the length of quality sequence data, you can use the expected number in fixed-length trimming. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#poly-g-trimming) Poly-G Trimming Poly-G artifacts appear on two-channel sequencing systems when the dark base G is called after synthesis has terminated. As a result, DRAGEN calls several erroneous high-confidence G bases on the ends of affected reads. For contaminated samples, many affected reads can be mapped to reference regions with high G content. The affected reads can cause problems for processing downstream. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#quality-trimming) Quality Trimming Base quality can degrade over the length of a read toward the 5' end and separate from any artifacts from early termination of synthesis. The lower quality bases can affect mapping and alignment results, and might lead to incorrect variant or methylation calls downstream. The quality trimming tool calculates a rolling average of the base quality inward from the 5' end and removes the minimum number of bases, so the average number of bases is above the threshold specified using `--trim-min-quality`. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#adapter-trimming) Adapter Trimming Problems during library preparation, or libraries with smaller inserts can result in the synthesis of high quality reads containing sequence from the adapters used. If not removed before analysis, noninsert bases can reduce mapping efficiency and downstream accuracy. The adapter trimming tool uses the adapter sequences from the input FASTA file, and then removes all hits greater than a specified size. Adapter trimming allows for a 10% mismatch. For 3' adapters, trimming is from the first matching adapter base to the end of the read. For 5' adapters, trimming is from the first (3') matching adapter base to the beginning (5') of the read. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#ambiguous-base-trimming) Ambiguous Base Trimming If quality trimming is not feasible due to reduced yield or other limitations, an alternative option is to remove only explicitly ambiguous bases from the ends of read. If enabled the ambiguous base trimmer applies a simple exact-match search to both ends of all processed reads, regardless of mate-pair status. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#minimum-length-trimming) Minimum Length Trimming You can maximize trimmer sensitivity, by using the minimum length trimming tool to remove a fixed number of bases from each read after the trimmer tools above have run. For example, if you would like to remove 5 bp from each read, a 7 bp adapter hit could be missed if five of the bases are removed first. To mitigate this issue, DRAGEN provides an optional minimum trim-length filter. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#maximum-length-trimming) Maximum Length Trimming If using libraries of fixed-size inserts, such as small PCR amplicons, it is more convenient to specify a length that all reads should be trimmed to rather than the number of bases to remove. You can use the maximum length trimming tool. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#polya-tail-trimming) PolyA Tail Trimming If using RNA libraries, reads overlapping the poly-A tail of the transcripts may contain long poly-A/poly-T sequences at the end of the reads which may result in incorrect alignment. The poly-A trimmer mitigates this by trimming the poly-A tail from the end of the read. See additional description in [RNA alignment](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#polya-trimming) section. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#read-trimming-metrics) Read Trimming Metrics -------------------------------------------------------------------------------------------------------------------------------------------------------------------- The trimmer generates a metrics file titled `\.trimmer_metrics.csv`. Metrics are available on an aggregate level over all input data. The metrics units are in reads or bases. * **Total input reads** Total number of reads in the input files. * **Total input bases** Total number of bases in the input reads. * **Total input bases R1** Total number of bases in R1 reads. * **Total input bases R2** Total number of bases in R2 reads. * **Average input read length** Total number of input bases divided by the number of input reads. * **Total trimmed reads** Total number of reads trimmed by at least one base, not including soft-trimming. * **Total trimmed bases** Total number of bases trimmed, not including soft-trimming. * **Average bases trimmed per read** The number of trimmed bases divided by the number of input reads. * **Average bases trimmed per trimmed read** The number of trimmed bases divided by the number of trimmed reads. * **Remaining poly-G K-mers R1 3prime** The number of R1 3' read ends that contain likely Poly-G artifacts after trimming. * **Remaining poly-G K-mers R2 3prime** The number of R2 3' read ends that contain likely Poly-G artifacts after trimming. * **Total filtered reads** The number of reads that were filtered out during trimming. * **Reads filtered for minimum read length R1** The number of R1 reads that were filtered due to being trimmed below the minimum read length. * **Reads filtered for minimum read length R2** The number of R2 reads that were filtered due to being trimmed below the minimum read length. * ** trimmed reads** The number of reads with at least one base trimmed by TRIMMER. DRAGEN reports the metric for both R1 and R2 mates and the filtering status (unfiltered or filtered) of the trimmed read. The metric includes reads that were trimmed during soft-trimming. Each trimming tool above produces the metric. * ** trimmed bases** The number of bases trimmed by TRIMMER. The metric is produced for both R1 and R2 mates and the filtering status (unfiltered or filtered) of the trimmed read. The metric includes bases from reads that were trimmed during soft trimming. Each trimming tool above produces the metric. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#read-trimming-settings) Read Trimming Settings ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#read-trimmer) Read trimmer Option Description `--read-trimmers` To enable trimming filters in hard-trimming mode, set to a comma-separated list of the trimmer tools you would like to use (in the order of execution). To disable trimming, set to `none`. During mapping, artifacts are removed from all reads. The following are valid trimmer names: * `fixed-len`—Fixed-length trimming * `polyg`—Poly-G trimming * `quality`—Quality trimming * `adapter`—Adapter trimming * `n`—Ambiguous base trimming * `min-len`—Minimum length trimming * `cut-end`—Maximum length trimming * `polya`—RNA Poly-A tail trimming. See additional description in [RNA alignment](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#polya-trimming) section * `bisulfite`—Bisulfite trimming Read trimming is disabled by default (default: "none"). `--soft-read-trimmers` To enable trimming filters in soft-trimming mode, set to a comma-separated list of the trimmer tools you would like to use (in the order of execution). To disable soft trimming, set to `none`. During mapping, reads are aligned as if trimmed, and bases are not removed from the reads. The following are the valid trimmer names. * `fixed-len`—Fixed-length trimming * `polyg`—Poly-G trimming * `quality`—Quality trimming * `adapter`—Adapter trimming * `n`—Ambiguous base trimming * `min-len`—Minimum length trimming * `cut-end`—Maximum length trimming * `polya`—RNA Poly-A tail trimming. See additional description in [RNA alignment](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-rna-pipeline/rna-alignment#polya-trimming) section * `bisulfite`—Bisulfite trimming Soft-trimming is enabled for the `polyg` filter by default (default: "polyg"). `--trimming-only` Disables mapping and alignment to run read-trimming only. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#filtering-after-the-trimmers-execution) Filtering after the trimmer's execution Option Description `--trim-min-length` Specify a minimum read length allowed after the trimmer execution. DRAGEN filters any reads with a length less than the value after all read-trimming steps are completed (default: 20). `--trim-min-len-read1` Specify a minimum read length allowed for read1 after the trimmer execution. DRAGEN filters any reads with a length of read1 less than the value after all read-trimming steps are completed (default: 20). `--trim-min-len-read2` Specify a minimum read length allowed for read2 after the trimmer execution. DRAGEN filters any reads with a length of read2 less than the value after all read-trimming steps are completed (default: 20). `--trim-filter-dummy-len` Specify the number of N bases in dummy reads that replace filtered reads (default: 10). `--trim-filter-set-flag` If enabled, dummy reads will have their 0x200 SAM flag set (default: true). ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#fixed-length-trimming-1) Fixed-length trimming Option Description `--trim-r1-5prime` Specify a fixed number of bases to trim from the 5' end of Read 1 (default: 0). `--trim-r1-3prime` Specify a fixed number of bases to trim from the 3' end of Read 1 (default: 0). `--trim-r2-5prime` Specify a fixed number of bases to trim from the 5' end of Read 2 (default: 0). `--trim-r2-3prime` Specify a fixed number of bases to trim from the 3' end of Read 2 (default: 0). ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#quality-trimming-1) Quality trimming Option Description `--trim-min-quality` Specify the minimum read quality. DRAGEN trims bases from the 3' end of reads with a quality below the value. `--trim-quality-r1-5prime` Specify the quality cutoff below which to trim from the 5' end of read 1. `--trim-quality-r1-3prime` Specify the quality cutoff below which to trim from the 3' end of read 1. `--trim-quality-r2-5prime` Specify the quality cutoff below which to trim from the 5' end of read 2. `--trim-quality-r2-3prime` Specify the quality cutoff below which to trim from the 3' end of read 2. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#adapter-trimming-1) Adapter trimming Option Description `--trim-adapter-read1` Specify the FASTA file that contains adapter sequences to trim from the 3' end of Read 1. `--trim-adapter-read2` Specify the FASTA file that contains adapter sequences to trim from the 3' end of Read 2. `--trim-adapter-r1-5prime` Specify the FASTA file that contains adapter sequences to trim from the 5' end of Read 1. NB: the sequences should be in reverse order (with respect to their appearance in the FASTQ) but not complemented. `--trim-adapter-r2-5prime` Specify the FASTA file that contains adapter sequences to trim from the 5' end of Read 2. NB: the sequences should be in reverse order (with respect to their appearance in the FASTQ) but not complemented. `--trim-adapter-stringency` Specify the minimum number of adapter bases required for trimming (default: 4). ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#bisulfite-trimming) Bisulfite trimming Option Description `--trim-bisulfite-ends` Enable both 5-Prime and 3-Prime bisulfite trimming. `--trim-bisulfite-5prime` If a 3' adapter was trimmed, trim an additional 2bp from the 3' end, unless the 5' end matches 'CAA' or 'CGA'". `--trim-bisulfite-3prime` If the 5' end matches 'CAA' or 'CGA', trim the first two of these 5' bases. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#minimum-length-trimming-1) Minimum-length trimming Option Description `--trim-min-r1-5prime` Specify the minimum number of bases to trim from the 5' end of Read 1 (default: 0). `--trim-min-r1-3prime` Specify the minimum number of bases to trim from the 3' end of Read 1 (default: 0). `--trim-min-r2-5prime` Specify the minimum number of bases to trim from the 5' end of Read 2 (default: 0). `--trim-min-r2-3prime` Specify the minimum number of bases to trim from the 3' end of Read 2 (default: 0). ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#maximum-length-trimming-1) Maximum-length trimming Option Description `--trim-max-length` Specify the maximum number of bases that can be trimmed from the sequences of both reads. `--trim-max-len-read1` Specify the maximum number of bases that can be trimmed from the sequences of read1. `--trim-max-len-read2` Specify the maximum number of bases that can be trimmed from the sequences of read2. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#polya-trimming) PolyA trimming Option Description `--trim-polya-min-trim` The minimum number of poly-As required for polya trimming (default: 20). ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#polyg-trimming) PolyG trimming Option Description `--trim-polyg-kmer-len` How many bases to check at each read end for poly-G artifact detection (default: 25). `--trim-polyg-kmer-non-g` The maximum number of non-G bases in the K-mer for poly-G artifact detection (default: 2). `--trim-polyg-g-score-r1-5prime` The score for G bases on the 5' end of read 1 (default: 0). `--trim-polyg-g-score-r1-3prime` The score for G bases on the 3' end of read 1 (default: 15). `--trim-polyg-g-score-r2-5prime` The score for G bases on the 5' end of read 2 (default: 0). `--trim-polyg-g-score-r2-3prime` The score for G bases on the 3' end of read 2 (default: 15). `--trim-polyg-min-trim-r1-5prime` The minimum number of G's to trim from the 5' end of read 1 (default: 6). `--trim-polyg-min-trim-r1-3prime` The minimum number of G's to trim from the 3' end of read 1 (default: 6). `--trim-polyg-min-trim-r2-5prime` The minimum number of G's to trim from the 5' end of read 2 (default: 6). `--trim-polyg-min-trim-r2-3prime` The minimum number of G's to trim from the 3' end of read 2 (default: 6). `--trim-polyg-early-exit-threshold` The signed score threshold for poly-G trimming to exit early (default: -500). ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#polyx-trimming) PolyX trimming Option Description `--trim-polyx-bases-r1-5prime` The bases to trim for polyX trimming from the 5' end of read 1 (default: empty string "" ). `--trim-polyx-bases-r1-3prime` The bases to trim for polyX trimming from the 3' end of read 1 (default: empty string "" ). `--trim-polyx-bases-r2-5prime` The bases to trim for polyX trimming from the 5' end of read 2 (default: empty string "" ). `--trim-polyx-bases-r2-3prime` The bases to trim for polyX trimming from the 3' end of read 2 (default: empty string "" ). `--trim-polyx-min-trim-r1-5prime` The minimum number of X's to trim from the 5' end of read 1 (default: 20). `--trim-polyx-min-trim-r1-3prime` The minimum number of X's to trim from the 3' end of read 1 (default: 20). `--trim-polyx-min-trim-r2-5prime` The minimum number of X's to trim from the 5' end of read 2 (default: 20). `--trim-polyx-min-trim-r2-3prime` The minimum number of X's to trim from the 3' end of read 2 (default: 20). [PreviousDNA Mappingchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dna-map-align) [NextDRAGEN FASTQCchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/dragen-fastqc) Last updated 7 months ago Was this helpful? * [Read Trimming Tools](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#read-trimming-tools) * [Fixed-Length Trimming](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#fixed-length-trimming) * [Poly-G Trimming](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#poly-g-trimming) * [Quality Trimming](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#quality-trimming) * [Adapter Trimming](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#adapter-trimming) * [Ambiguous Base Trimming](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#ambiguous-base-trimming) * [Minimum Length Trimming](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#minimum-length-trimming) * [Maximum Length Trimming](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#maximum-length-trimming) * [PolyA Tail Trimming](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#polya-tail-trimming) * [Read Trimming Metrics](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#read-trimming-metrics) * [Read Trimming Settings](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#read-trimming-settings) * [Read trimmer](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#read-trimmer) * [Filtering after the trimmer's execution](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#filtering-after-the-trimmers-execution) * [Fixed-length trimming](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#fixed-length-trimming-1) * [Quality trimming](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#quality-trimming-1) * [Adapter trimming](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#adapter-trimming-1) * [Bisulfite trimming](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#bisulfite-trimming) * [Minimum-length trimming](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#minimum-length-trimming-1) * [Maximum-length trimming](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#maximum-length-trimming-1) * [PolyA trimming](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#polya-trimming) * [PolyG trimming](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#polyg-trimming) * [PolyX trimming](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/read-trimming#polyx-trimming) Was this helpful? --- # DNA Germline WGS | DRAGEN v4.3 | DRAGEN The DRAGEN recipe includes the recommended pipeline specific commands. A DRAGEN recipe is a predefined set of analysis parameters and workflow settings tailored for a specific type of genomic analysis. Some default parameters are included for clarity and are marked with comments. Copy /opt/dragen/$VERSION/bin/dragen #DRAGEN install path --ref-dir $REF_DIR #path to DRAGEN graph hashtable --output-directory $OUTPUT --intermediate-results-dir $PATH #e.g. SDD /staging --output-file-prefix $PREFIX # Inputs --fastq-list $PATH #see 'Input Options' for FQ, BAM or CRAM --fastq-list-sample-id $STRING # Mapper --enable-map-align true #optional with BAM/CRAM input --enable-map-align-output true #optionally save the output BAM --enable-sort true #default=true --enable-duplicate-marking true #default=true # Small variant caller --enable-variant-caller true # Annotation --variant-annotation-data PATH --variant-annotation-assembly GRCh37/8 --enable-variant-annotation true # SV --enable-sv true # CNV --enable-cnv true --cnv-enable-self-normalization true # HLA genotyper --enable-hla true --hla-enable-class-2 true #optional if assay covers class II HLA regions # Targeted caller --enable-targeted true #Targeted # Star allele --enable-star-allele true # PGX --enable-pgx true #PGX # Short tandem repeats --repeat-genotype-enable true [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs#notes-and-additional-options) Notes and additional options -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs#hashtable) Hashtable For DRAGEN germline runs, it is recommended to use the graph hashtable. See: [Product Filesarrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs#input-options) Input options DRAGEN input sources include: fastq list, fastq, bam, or cram. FQ list Input FQ Input BAM Input CRAM Input ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs#mapping-and-aligning) Mapping and Aligning Option Description `--enable-map-align true` Optionally disable map & align (default=true). `--enable-map-align-output true` Optionally save the output BAM (default=false). `--Aligner.clip-pe-overhang 2` Clean up any unwanted UMI indexes. Only use when reads contain UMIs, but UMI collapsing was not run. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs#duplicate-marking) Duplicate Marking Option Description `--enable-duplicate-marking true` By default, DRAGEN marks duplicate reads and exclude them from variant calling. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs#snv) SNV DRAGEN SNV VC employs machine learning based variant recalibration (DRAGEN-ML). It processes read and other contextual evidence to remove false positives, recover false negatives and reduce zygosity errors. No additional setup is required. DRAGEN-ML is enabled by default as needed, when running the germline SNV VC on hg19 or hg38. Note that we do not recommend changing the default QUAL thresholds of 3 for DRAGEN-ML and 10 for DRAGEN without ML. These values differ from each other because DRAGEN-ML improves the calibration of QUAL scores, leading to a change in the scoring range. Option Description `--vc-target-bed` Limit variant calling to region of interest. `--vc-combine-phased-variants-distance INT` Maximum distance over which phased variants will be combined. Set to 0 to disable. Valid range is \[0; 15\] (Default=2) `--vc-emit-ref-confidence GVCF` To enable gVCF output. `--vc-enable-vcf-output` To enable VCF file output during a gVCF run, set to true. The default value is false. For more detail on the small variant caller in somatic mode please refer to [Somatic Mode](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/small-variant-calling/somatic-mode) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs#annotation) Annotation For instructions on how to download the Nirvana annotation database, please refer to [Nirvana](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/nirvana) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs#hla) HLA Option Description `--enable-hla` Enable HLA typer (this setting by default will only genotype class 1 genes) `--hla-as-filter-min-threshold` Internal option to set min alignment score threshold. The default is 59 and works for WES and WGS. Set to 29 for panels. `--hla-as-filter-ratio-threshold` Minimum Alignment score of a read mate to be considered. The default is 0.67 and works for WES and WES. Set to 0.85 for panels. `--hla-enable-class-2` Extend genotyping to HLA class 2 genes (default=true). ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs#cnv) CNV Option Description `--cnv-enable-gcbias-correction true` Enable or disable GC bias correction when generating target counts. `--cnv-segmentation-mode $SEG_MODE` Option to override the default segmentation algorithm. Defaults include `slm` for germline WGS, `aslm` for somatic WGS, and `hslm` for targeted analysis. For more information, see [CNV Calling](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/cnv-calling) . [PreviousDNA Germline WGS UMIchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs-umi) [NextDNA Somatic Tumor-Normal Solid Panel UMIchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-somatic-tumor-normal-solid-panel-umi) Last updated 7 months ago Was this helpful? * [Notes and additional options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs#notes-and-additional-options) * [Hashtable](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs#hashtable) * [Input options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs#input-options) * [Mapping and Aligning](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs#mapping-and-aligning) * [Duplicate Marking](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs#duplicate-marking) * [SNV](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs#snv) * [Annotation](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs#annotation) * [HLA](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs#hla) * [CNV](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs#cnv) Was this helpful? Copy --fastq-list $PATH --fastq-list-sample-id $STRING Copy --fastq-file1 $PATH --fastq-file2 $PATH --RGSM $STRING --RGID $STRING Copy --bam-input $PATH Copy --cram-input $PATH --- # RNA WTS | DRAGEN v4.3 | DRAGEN The DRAGEN recipe includes the recommended pipeline specific commands. A DRAGEN recipe is a predefined set of analysis parameters and workflow settings tailored for a specific type of genomic analysis. Some default parameters are included for clarity and are marked with comments. Copy /opt/dragen/$VERSION/bin/dragen #DRAGEN install path --ref-dir $REF_DIR #path to DRAGEN graph hashtable --output-directory $OUTPUT --intermediate-results-dir $PATH #e.g. SDD /staging --output-file-prefix $PREFIX # Inputs --fastq-list $PATH #see 'Input Options' for FQ, BAM or CRAM --fastq-list-sample-id $STRING # Mapper --enable-rna true --annotation-file $GTF #GTF or GFF3 format --enable-map-align true #required for RNA --enable-map-align-output true #optionally save the output BAM --enable-sort true #default=true --enable-duplicate-marking true #default=true # Small variant caller --enable-variant-caller true --vc-target-bed $VC_TARGET_BED # RNA Quantification --enable-rna-quantification true --rna-library-type A #see 'RNA Quant' --rna-quantification-gc-bias true # RNA Gene Fusions --enable-rna-gene-fusion true [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-wts#notes-and-additional-options) Notes and additional options ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-wts#hashtable) Hashtable For DRAGEN RNA runs, it is recommended to use the linear hashtable. See: [Product Filesarrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-wts#input-options) Input options DRAGEN input sources include: fastq list, fastq, bam, or cram. FQ list Input FQ Input BAM Input CRAM Input ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-wts#mapping-and-aligning) Mapping and Aligning Option Description `--enable-map-align true` Optionally disable map & align (default=true). `--enable-map-align-output true` Optionally save the output BAM (default=false). ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-wts#duplicate-marking) Duplicate Marking Option Description `--enable-duplicate-marking true` By default, DRAGEN marks duplicate reads and exclude them from variant calling. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-wts#rna-variant-calling) RNA Variant Calling Option Description `--vc-target-bed $PATH` Restrict the variants called to a target bed. For WTS, a bed file specifying the gene-coding regions should be provided to avoid calling erroneous variants in non-coding regions due to noisy reads. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-wts#rna-quant) RNA Quant Option Description `--rna-library-type` Set the library according to the read orientations. Set to 'A' to auto detect the correct read orientation. Alternatively select 'IU', 'ISR', 'ISF', 'U', 'SR', or 'SF'. [PreviousRNA Panelchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-panel) [NextBCL conversionchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/bcl-conversion) Last updated 7 months ago Was this helpful? * [Notes and additional options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-wts#notes-and-additional-options) * [Hashtable](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-wts#hashtable) * [Input options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-wts#input-options) * [Mapping and Aligning](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-wts#mapping-and-aligning) * [Duplicate Marking](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-wts#duplicate-marking) * [RNA Variant Calling](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-wts#rna-variant-calling) * [RNA Quant](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-wts#rna-quant) Was this helpful? Copy --fastq-list $PATH --fastq-list-sample-id $STRING Copy --fastq-file1 $PATH --fastq-file2 $PATH --RGSM $STRING --RGID $STRING Copy --bam-input $PATH Copy --cram-input $PATH --- # Clinical Research Workflows | DRAGEN DRAGEN v4.4 introduces support for DRAGEN server apps. These apps, comprised of Docker images, Nextflow workflows, a CLI shell script, and packaged resource bundles, can be downloaded and installed on the on-premises server. The packaged resource bundles include all the resource files required to run the application, such as the hash table(s), various noise baseline files, bed files. Server apps make it easy to run complex workflows such as Tumor Normal somatic analysis by simplifying the management of external resources and applying the correct command line parameters for the selected analysis type. The DRAGEN server can support multiple installed server apps and DRAGEN on-prem for command line use at the same time. [PreviousDRAGEN Appschevron-left](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-apps) [NextCommon Product Featureschevron-right](https://help.dragen.illumina.com/product-guides/dragen-v4.5/dragen-apps/dragen-apps/common) Last updated 7 months ago Was this helpful? Was this helpful? --- # VNTR Calling | DRAGEN v4.3 | DRAGEN The DRAGEN Variable Number Tandem Repeat (VNTR) Caller detects expansions and contractions in tandem repeat (TR) regions. For specified TR regions in the genome, the DRAGEN VNTR Caller estimates the size of the haplotypes in each region and provides variant calls, including the number of copies of the repeat for the sample in question. The DRAGEN VNTR Caller only considers TR regions included in a pre-specified VNTR catalog file. For each region in the VNTR catalog, the VNTR Caller performs the following steps: 1. Read fragment collection, including wrap-around alignment and read classification; 2. Genotyping, including the scoring of candidate haplotype lengths using a Bayesian likelihood model. The output of the VNTR Caller is the total length of sequence present in each TR region for the sample in question, resolved for each haplotype if possible; the copy number for each region is calculated from the length. Calls are reported in a VNTR output VCF file following the VCFv4.4 spec. The DRAGEN VNTR Caller can be enabled by setting the `--enable-vntr` option to `true`. The VNTR Caller requires whole-genome sequencing (WGS) data aligned to a human reference genome with at least 30x coverage. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#dragen-vntr-caller-overview) DRAGEN VNTR Caller Overview ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3660538983-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FpFn0gLe5V859SXejQNAW%252Fuploads%252Fgit-blob-3a0f4f0b2e8b312c67380119e2675958bfe18636%252Fvntr-pipeline.svg%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=6776b8e&sv=2) image This diagram illustrates the overall workflow of the VNTR Caller. The VNTR Caller takes as input a set of aligned reads from the sample in question (either from the DRAGEN mapper or from an input BAM/CRAM) and a VNTR catalog file. The VNTR catalog is a bed file specifying the TR regions for the VNTR Caller to act upon. Each region in the bed file is expected to be the start and the end of a tandem repeat sequence with no additional buffer sequence. The catalog also includes a unique TR ID for each region and the sequence of the repeat unit/pattern (see below for more details on the VNTR catalog file format). ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#fragment-collection) Fragment Collection The VNTR Caller processes each TR region in parallel, starting with read fragment collection. The VNTR Caller considers read fragments (i.e. paired-end reads as a single unit) rather than individual reads. To obtain all of the relevant read fragments for each region, all of the reads that overlap the region are found, and then all of their mates are collected as well. Due to the repetitive nature of TR regions, existing read-alignments may be unreliable. Therefore spanning reads, unmapped reads, and reads with soft-clips undergo a specialized wrap-around alignment algorithm, which allows for a read to align to the same pattern sequence multiple times without penalty (mirroring the structure of the tandem repeat). This algorithm produces more reliable alignments of the read fragments to the TR region. Additionally, another rule to virtually extend the boundaries of the repetitive region into the flanks is applied to resolve some alignment ambiguities arising from the wrap-around alignment. Once reliable alignments of the read fragments have been obtained, the next step is to classify each read. Reads are classified as non-overlapping, flanking, spanning, and contained relative to the TR region based on the following figure. ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3660538983-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FpFn0gLe5V859SXejQNAW%252Fuploads%252Fgit-blob-7242d1ed7d9671ff1f0a9637fa2c2a3fdc8c446b%252Fvntr-overlaps.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=6c6d518e&sv=2) image The output of fragment collection is the set of all read fragments in each TR region, re-aligned as necessary, with each read given a classification. This collection of read fragments is referred to as a pileup and acts as the input to the genotyper. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#genotyping) Genotyping The genotyper determines the top-scoring haplotypes based on the read fragment evidence for each TR region. Given a pileup, the genotyper further classifies each read fragment into fragment classes. ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3660538983-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FpFn0gLe5V859SXejQNAW%252Fuploads%252Fgit-blob-6d444b473b47d9ce4801859a964eb4e60b6ae2b7%252Fvntr-classes.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=6321742e&sv=2) image The number of fragments in each class acts as evidence for the haplotype lengths of the TR region. A Bayesian likelihood model is used to evaluate what pair of haplotypes have the highest likelihood of generating the observation of these fragment class counts. A set of candidate haplotype lengths is generated based on fractional increments of the repeat pattern length, and each pair of haplotype lengths is evaluated as a candidate diploid genotype. If the caller detects that individual haplotype lengths cannot be resolved, the total length is considered as a candidate genotype instead (referred to as a total call). In subsequent steps, these total call candidates are assessed as if they were homozygous diploid genotypes. For a Bayesian model, the posterior probability of each candidate diploid genotype must be considered. The posterior probability is made up of two parts: the genotype prior and the pileup likelihood. Three types of priors are currently supported: * No prior (all alleles weighted equally, referred to as model 0) * Het/hom priors (four classes with different weights: homozygous reference, ref/alt, homozygous alt, and alt1/alt2, referred to as model 1) * Population haplotype frequencies (region-specific haplotype frequencies over high-quality population data sets, referred to as model 3 and used by default) The priors model can be chosen by setting the option `--vntr-priors-model` to `0`, `1`, or `3` (the default being `3`). The pileup likelihood is calculated as the likelihood of observing the fragment class counts given the candidate diploid genotype (based on an underlying model for how fragments are generated from a TR region haplotype of a given length). With the prior and the pileup likelihood, the posterior probability of each candidate diploid genotype can be computed. The diploid genotype with the highest posterior probability is chosen as the resulting call for each region. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#running-the-dragen-vntr-caller) Running the DRAGEN VNTR Caller ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- The VNTR Caller is disabled by default. To enable the VNTR Caller, set `--enable-vntr` to `true`. The VNTR Caller can run directly from FASTQ input with the mapper or from prealigned BAM/CRAM input. You can also enable the VNTR Caller in parallel with any other germline variant callers as part of a WGS germline analysis workflow. For more information on other variant callers, see the DRAGEN DNA Pipeline. FASTQ input example: BAM input example: Additional Options: * The number of threads used for the DRAGEN VNTR caller can be adjusted using the `--vntr-num-threads ` option. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#vntr-catalog) VNTR Catalog ------------------------------------------------------------------------------------------------------------------------------------------------- The VNTR catalog is a bed file with the following required fields: * chromosome (or contig) * start position (0-based inclusive) * end position (0-based exclusive = 1-based inclusive) * TR ID (unique ID for TR region) * repeat unit sequence (sequence of repeat pattern/motif) The reference haplotype length is calculated by subtracting the start position from the end position, and the number of repeat units in the reference can be found by dividing the reference haplotype length by the length of the repeat unit. When using a standard reference (`hg38`, `hg19`, or `GRCh37`), DRAGEN will automatically use a matching pre-packaged catalog by default. A custom catalog can be provided by adding in the option, `--vntr-catalog-bed `. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#custom-references-normalization-regions-and-priors) Custom References, Normalization Regions, and Priors ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- For references other than the ones mentioned above, a catalog must be provided. Furthermore the caller requires a set of normalization regions (`--vntr-normalization-regions-bed `. These regions should be well-behaved and free of any VNTRs or other large variants. We recommend using a few thousand regions of at least 2kb each. These two files are enough to run the genotyper without priors or the aforementioned flat priors model (`--vntr-priors-model 0` or `1`). To enable population priors `3`, one additional file has to be provided: `--vntr-priors-file `. The json file contains data obtained from a population analysis, structured like the following example with one entry per catalog region: [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#output) Output ------------------------------------------------------------------------------------------------------------------------------------- The output of the DRAGEN VNTR Caller includes a VNTR VCF file, a table output TSV file, and a VNTR metrics file. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#vntr-vcf-file) VNTR VCF File The VNTR VCF file follows [version 4.4 of the VCF specarrow-up-right](https://samtools.github.io/hts-specs/VCFv4.4.pdf) . The VCF includes a call for every TR region provided in the VNTR catalog unless it was hard-filtered in the fragment collection or the genotyping (the filter annotation can be found in the table output file). Each call is an estimate of the lengths of the haplotypes present in that region for the sample in question. If the individual haplotypes lengths can be distinguished, then a diploid call is reported including the lengths and copy number of each haplotype (in the INFO RB and RUC fields, respectively). Otherwise, a total call is made, only reporting the total length and total copy number for the region (in the FORMAT TOTALRB and TOTALRUC fields). For total calls, `GT = ./.`. If the length of a haplotype is within a certain threshold of the reference array length for the region, then it is reported as a reference allele (the default reference threshold is 10%). If both haplotypes are reference alleles or if the total length of a total call is within the total reference threshold, then a reference call is reported in the VCF, with `HomRef` in the `FILTER` field and `GT = 0/0`. A symbolic `` is reported in the `ALT` field for each non-reference allele in the call. The following fields are included for each VCF entry: * `INFO:SVTYPE`: set to "CNV" for all VNTR calls * `INFO:SVLEN`: set to the reference array length * `INFO:EVENTTYPE`: set to "VNTR" * `INFO:RUS`: the sequence of the repeat unit (i.e. the repeat pattern or motif) * `INFO:RUL`: the length of the repeat unit * `INFO:REFRUC`: the number of copies of the repeat in the reference haplotype * `INFO:RB`: the length of each ALT haplotype being reported * `INFO:CN`: the copy number per ALT haplotype relative to the reference (equal to `RB / SVLEN`) * `INFO:CNVTRLEN`: the change in length of each ALT haplotype compared to the reference (equal to `RB - SVLEN`) * `INFO:RUC`: the number of repeat unit copies for each ALT haplotype being reported (equal to `RB / RUL`) * `FORMAT:SVFT`: any filters that will be applied only in the merged SV + VNTR VCF * `FORMAT:GQ`: Genotype quality score * `FORMAT:CN`: the total copy number relative to the reference (equal to `TOTALRB / SVLEN`) * `FORMAT:TOTALRB`: the total length of all haplotypes (including reference haplotypes if present) * `FORMAT:TOTALCNVTRLEN`: the total change in length of all haplotypes relative to the reference (equal to `TOTALRB - 2*SVLEN`) * `FORMAT:TOTALRUC`: the total number of repeat unit copies of all haplotypes (including reference haplotypes if present; equal to `TOTALRB / RUL`) Additional fields: * `INFO:RUCCHANGE`: the change in the RUC compared to the reference for each ALT haplotype (equal to `RUC - REFRUC`) * `INFO:LOGPROB`: the log probability of the called alleles from the genotyper * `INFO:VNTRCLASSFIT`: the score of how well the fragment classes fit the expected distribution * `INFO:TOTALFRAGCOUNT`: the number of fragments used to make the call ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#table-output-tsv-file) Table Output TSV File The table output file provides a simple summary of the VNTR Caller output. Every single region included in the VNTR catalog bed will also be included in the table output file; regions where a hard-filter was applied in the fragment collection or the genotyping will still be included with the reason for the filter annotated (these regions will not appear in the VCF file). For each region, the following information is provided: * `trId`: the unique ID of the TR region * `patternSize`: the length of the repeat unit (i.e. the repeat pattern/motif length) * `refArraySize`: the length of the reference haplotype for this region * `Hap1Size`: the length of the first haplotype of the call (`Hap1Size <= Hap2Size`; `NA` for total calls) * `Hap2Size`: the length of the second haplotype of the call (`NA` for total calls) * `TotalSize`: the total length of all haplotypes in the call (equal to `Hap1Size + Hap2Size` for diploid calls) * `Likelihood`: the log likelihood of the called alleles from the genotyper (equal to `INFO:LOGPROB` in the VCF) * `QUAL`: QUAL score (matches QUAL field in the VCF) * `GQScore`: Genotype quality score (equal to `FORMAT:GQ` in the VCF) * `ClassDistributionFit`: the score of how well the fragment classes fit the expected distribution (equal to `INFO:VNTRCLASSFIT` in the VCF) * `FragmentCount`: the number of fragments used to make the call (equal to `INFO:TOTALFRAGCOUNT` in the VCF) * `Flags`: the flags and filters applied to the call ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#vntr-metrics-file) VNTR Metrics File The VNTR metrics file reports summary statistics for the VNTR caller including region counts, read class counts, and call counts. Region counts include the number of normalization regions, the number of prior regions, the number of TR regions with nonzero coverage, and the total number of TR regions. Read class counts include the total number of reads in each class (strictly left, left-flanking, spanning, contained, right-flanking, strictly right, and unmapped). Call counts include the total number of uncalled TR regions, as well as the total number of deletion, insertion, and reference calls for diploid and total calls (note that for a region where a diploid call is made, two calls are reported, but for a total call region, only one call is reported). [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#merging-with-sv-vcf) Merging with SV VCF --------------------------------------------------------------------------------------------------------------------------------------------------------------- DRAGEN supports the automatic merger of the VNTR VCF calls with the DRAGEN Structural Variant (SV) Caller output VCF. By default, if both the DRAGEN VNTR Caller and the DRAGEN SV Caller are enabled (with the options `--enable-vntr true` and `--enable-sv true`, respectively), then calls made by the VNTR Caller will also be included in the DRAGEN SV VCF (`.sv.vcf.gz`). This behavior can be disabled by adding the option `--sv-vntr-merge false`. The VNTR VCF does not change even if DRAGEN SV is enabled. When VNTR calls are added to the SV VCF, the following changes are applied: * VNTR diploid calls with `GT = 1/2` are split into two separate VCF entries, each of which is reported as `GT = 0/1`. * A `lt50bp` filter is applied to all VNTR calls with `INFO:CNVTRLEN < 50` (the min SV length parameter is set to 50 bp by default). For total calls with no `INFO:CNVTRLEN`, `FORMAT:TOTALCNVTRLEN` is used instead. * A `TotalCall` filter is applied to all VNTR total calls (calls with `GT = ./.`). This behavior can be disabled by adding the option `--sv-vntr-filter-total-calls false`. * A `LowPopulationVariance` filter is applied to all VNTR calls with the `FORMAT:SVFT` field equal to `LowPopulationVariance`. The filter indicates that there are few population samples with an SV variant for this region. * An `OverlapsVNTR` filter is applied to any SV call that overlaps with a VNTR call (even with a HomRef filter) UNLESS the VNTR call has a `TotalCall` or a `LowPopulationVariance` filter. [PreviousStructural Variant De Novo Quality Scoringchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/sv-calling/sv-denovo-quality-scoring) [NextFilter Duplicate Variantschevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/variant-deduplication) Last updated 7 months ago Was this helpful? * [DRAGEN VNTR Caller Overview](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#dragen-vntr-caller-overview) * [Fragment Collection](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#fragment-collection) * [Genotyping](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#genotyping) * [Running the DRAGEN VNTR Caller](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#running-the-dragen-vntr-caller) * [VNTR Catalog](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#vntr-catalog) * [Custom References, Normalization Regions, and Priors](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#custom-references-normalization-regions-and-priors) * [Output](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#output) * [VNTR VCF File](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#vntr-vcf-file) * [Table Output TSV File](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#table-output-tsv-file) * [VNTR Metrics File](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#vntr-metrics-file) * [Merging with SV VCF](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/vntr-calling#merging-with-sv-vcf) Was this helpful? Copy dragen \ -r \ --fastq-file1 \ --fastq-file2 \ --RGID \ --RGSM \ --output-directory \ --output-file-prefix \ --enable-map-align true \ --enable-vntr true \ Copy dragen \ -r \ --bam-input \ --output-directory \ --output-file-prefix \ --enable-map-align false \ --enable-vntr true \ Copy {"vntr_00000016": { "reference allele size": 834, // length of the region in the catalog "reference allele bias": 0.2, // a small bump for the reference size "unobserved allele prior": 1e-07, // the prior given to any allele not observed in the population "allele observations": [\ {"size": 834, "count": 340}, // size and number of occurences of a certain haplotype\ {"size": 1071, "count": 78},\ ], "total size observations": [\ {"size": 1668, "count": 98}, // size and number of occurences of the sum of \ {"size": 1905, "count": 12}, // the two haplotypes in a sample\ {"size": 1695, "count": 7},\ ] } --- # RNA Panel | DRAGEN v4.3 | DRAGEN The DRAGEN recipe includes the recommended pipeline specific commands. A DRAGEN recipe is a predefined set of analysis parameters and workflow settings tailored for a specific type of genomic analysis. Some default parameters are included for clarity and are marked with comments. Copy /opt/dragen/$VERSION/bin/dragen #DRAGEN install path --ref-dir $REF_DIR #path to DRAGEN graph hashtable --output-directory $OUTPUT --intermediate-results-dir $PATH #e.g. SDD /staging --output-file-prefix $PREFIX # Inputs --fastq-list $PATH #see 'Input Options' for FQ, BAM or CRAM --fastq-list-sample-id $STRING # Mapper --enable-rna true --annotation-file $GTF #GTF or GFF3 format --enable-map-align true #required for RNA --enable-map-align-output true #optionally save the output BAM --enable-sort true #default=true --enable-duplicate-marking true #default=true # Small variant caller --enable-variant-caller true --vc-target-bed $VC_TARGET_BED # RNA Quantification --enable-rna-quantification true --rna-library-type A #see 'RNA Quant' --rna-quantification-gc-bias true # RNA Gene Fusions --enable-rna-gene-fusion true --rna-gf-enriched-regions $PATH #see 'RNA Fusion' [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-panel#notes-and-additional-options) Notes and additional options ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-panel#hashtable) Hashtable For DRAGEN RNA runs, it is recommended to use the linear hashtable. See: [Product Filesarrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-panel#input-options) Input options DRAGEN input sources include: fastq list, fastq, bam, or cram. FQ list Input FQ Input BAM Input CRAM Input ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-panel#mapping-and-aligning) Mapping and Aligning Option Description `--enable-map-align true` Optionally disable map & align (default=true). `--enable-map-align-output true` Optionally save the output BAM (default=false). ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-panel#duplicate-marking) Duplicate Marking Option Description `--enable-duplicate-marking true` By default, DRAGEN marks duplicate reads and exclude them from variant calling. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-panel#rna-variant-calling) RNA Variant Calling Option Description `--vc-target-bed $PATH` Restrict the variants called to a target bed. For WTS, a bed file specifying the gene-coding regions should be provided to avoid calling erroneous variants in non-coding regions due to noisy reads. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-panel#rna-quant) RNA Quant Option Description `--rna-library-type` Set the library according to the read orientations. Set to 'A' to auto detect the correct read orientation. Alternatively select 'IU', 'ISR', 'ISF', 'U', 'SR', or 'SF'. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-panel#rna-amplicon) RNA Amplicon To enable RNA amplicon, set: * `--enable-rna-amplicon true`, and * `--amplicon-target-bed $PATH`. If RNA amplicon mode is enabled and the amplicon bed file already includes the gene name, then it is not required to set the ENRICH options option, since DRAGEN will read the enriched genes names from the amplicon BED file (fifth column). [PreviousDNA Somatic Tumor-Only ctDNA Panel UMIchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-somatic-tumor-only-ctdna-panel-umi) [NextRNA WTSchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-wts) Last updated 7 months ago Was this helpful? * [Notes and additional options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-panel#notes-and-additional-options) * [Hashtable](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-panel#hashtable) * [Input options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-panel#input-options) * [Mapping and Aligning](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-panel#mapping-and-aligning) * [Duplicate Marking](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-panel#duplicate-marking) * [RNA Variant Calling](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-panel#rna-variant-calling) * [RNA Quant](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-panel#rna-quant) * [RNA Amplicon](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/rna-panel#rna-amplicon) Was this helpful? Copy --fastq-list $PATH --fastq-list-sample-id $STRING Copy --fastq-file1 $PATH --fastq-file2 $PATH --RGSM $STRING --RGID $STRING Copy --bam-input $PATH Copy --cram-input $PATH --- # DNA Germline Panel | DRAGEN v4.3 | DRAGEN The DRAGEN recipe includes the recommended pipeline specific commands. A DRAGEN recipe is a predefined set of analysis parameters and workflow settings tailored for a specific type of genomic analysis. Some default parameters are included for clarity and are marked with comments. Copy /opt/dragen/$VERSION/bin/dragen #DRAGEN install path --ref-dir $REF_DIR #path to DRAGEN graph hashtable --output-directory $OUTPUT --intermediate-results-dir $PATH #e.g. SDD /staging --output-file-prefix $PREFIX # Inputs --fastq-list $PATH #see 'Input Options' for FQ, BAM or CRAM --fastq-list-sample-id $STRING # Mapper --enable-map-align true #optional with BAM/CRAM input --enable-map-align-output true #optionally save the output BAM --enable-sort true #default=true --enable-duplicate-marking true #default=true # Small variant caller --enable-variant-caller true --vc-target-bed $VC_TARGET_BED # Annotation --variant-annotation-data PATH --variant-annotation-assembly GRCh37/8 --enable-variant-annotation true # SV --enable-sv true --sv-exome true --sv-call-regions-bed $SV_TARGET_BED # CNV --enable-cnv true --cnv-target-bed $PATH --cnv-combined-counts $PATH #CNV PON # HLA genotyper --enable-hla true --hla-enable-class-2 true #optional if assay covers class II HLA regions --hla-as-filter-min-threshold 29.0 #panel specific setting --hla-as-filter-ratio-threshold 0.85 #panel specific setting [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel#notes-and-additional-options) Notes and additional options ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel#hashtable) Hashtable For DRAGEN germline runs, it is recommended to use the graph hashtable. See: [Product Filesarrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel#input-options) Input options DRAGEN input sources include: fastq list, fastq, bam, or cram. FQ list Input FQ Input BAM Input CRAM Input ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel#mapping-and-aligning) Mapping and Aligning Option Description `--enable-map-align true` Optionally disable map & align (default=true). `--enable-map-align-output true` Optionally save the output BAM (default=false). `--Aligner.clip-pe-overhang 2` Clean up any unwanted UMI indexes. Only use when reads contain UMIs, but UMI collapsing was not run. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel#duplicate-marking) Duplicate Marking Option Description `--enable-duplicate-marking true` By default, DRAGEN marks duplicate reads and exclude them from variant calling. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel#snv) SNV DRAGEN SNV VC employs machine learning based variant recalibration (DRAGEN-ML). It processes read and other contextual evidence to remove false positives, recover false negatives and reduce zygosity errors. No additional setup is required. DRAGEN-ML is enabled by default as needed, when running the germline SNV VC on hg19 or hg38. Note that we do not recommend changing the default QUAL thresholds of 3 for DRAGEN-ML and 10 for DRAGEN without ML. These values differ from each other because DRAGEN-ML improves the calibration of QUAL scores, leading to a change in the scoring range. Option Description `--vc-target-bed` Limit variant calling to region of interest. `--vc-combine-phased-variants-distance INT` Maximum distance over which phased variants will be combined. Set to 0 to disable. Valid range is \[0; 15\] (Default=2) `--vc-emit-ref-confidence GVCF` To enable gVCF output. `--vc-enable-vcf-output` To enable VCF file output during a gVCF run, set to true. The default value is false. For more detail on the small variant caller in somatic mode please refer to [Somatic Mode](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/small-variant-calling/somatic-mode) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel#annotation) Annotation For instructions on how to download the Nirvana annotation database, please refer to [Nirvana](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/nirvana) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel#hla) HLA Option Description `--enable-hla` Enable HLA typer (this setting by default will only genotype class 1 genes) `--hla-as-filter-min-threshold` Internal option to set min alignment score threshold. The default is 59 and works for WES and WGS. Set to 29 for panels. `--hla-as-filter-ratio-threshold` Minimum Alignment score of a read mate to be considered. The default is 0.67 and works for WES and WES. Set to 0.85 for panels. `--hla-enable-class-2` Extend genotyping to HLA class 2 genes (default=true). ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel#cnv) CNV Option Description `--cnv-enable-gcbias-correction true` Enable or disable GC bias correction when generating target counts. `--cnv-segmentation-mode $SEG_MODE` Option to override the default segmentation algorithm. Defaults include `slm` for germline WGS, `aslm` for somatic WGS, and `hslm` for targeted analysis. `--cnv-segmentation-bed $PATH` If you are using somatic targeted panels with a set of genes supplied with the capture kit, then you can bypass segmentation by specifying a cnv-segmentation-bed and using cnv-segmentation-mode=bed. For more information, see [CNV Calling](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/cnv-calling) . ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel#cnv-panel-of-normals-pon) CNV Panel of Normals (PON) The panel of normals mode uses a set of matched normal samples to determine the baseline level from which to call CNV events. These matched normal samples should be derived from the same library prep and sequencing workflow that was used for the case sample. CNV requires PON files for all targeted analyses (including panels, exomes, germline, tumor-only and tumor-normal workflows). It is recommended to use 30-100 normal samples when building the PON, but fewer may be used. If sample coverage noise is relatively stable, as few as 5 PON samples may yield acceptable results. Follow the two steps below to generate CNV PON: **Step 1. Generate target counts of individual normal samples.** Any options used for panel of normals generation (BED file, GC Bias Correction, etc) should be matched when processing the case sample. **Step 2. Combined counts generation.** Individual PON counts can be merged into a single file as a `.combined.counts.txt.gz` file. `$CNV_NORMALS_LIST` is a single lines file with paths to each target counts file generated by step1 (either `.target.counts.gz` or `.target.counts.gc-corrected.gz`). Output will have a PON file with suffix `.combined.counts.txt.gz` file. Use the PON file in case sample runs of DRAGEN CNV with `--cnv-combined-counts` option. For more information, see [Panel of Normals](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/cnv-calling) . In some cases, an in-run PON containing germline samples from the same batch (i.e. sample source, DNA extraction, library prep and sequencing run) may provide superior normalization. Analysis of a full batch of germline samples with an automatically generated in-run PON can be performed using [DRAGEN Enrichment on BaseSpacearrow-up-right](https://www.illumina.com/products/by-type/informatics-products/basespace-sequence-hub/apps/dragen-enrichment.html) or DRAGEN Germline Enrichment on [ICAarrow-up-right](https://www.illumina.com/products/by-type/informatics-products/connected-analytics.html) . CNV PONs can also be built in the cloud using the [DRAGEN Baseline Builder App on BaseSpacearrow-up-right](https://www.illumina.com/products/by-type/informatics-products/basespace-sequence-hub/apps/dragen-baseline-builder.html) or the DRAGEN Systematic Noise File Builder Pipeline on [ICAarrow-up-right](https://www.illumina.com/products/by-type/informatics-products/connected-analytics.html) . [PreviousDNA Germline Panel UMIchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi) [NextDNA Germline WES UMIchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wes-umi) Last updated 7 months ago Was this helpful? * [Notes and additional options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel#notes-and-additional-options) * [Hashtable](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel#hashtable) * [Input options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel#input-options) * [Mapping and Aligning](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel#mapping-and-aligning) * [Duplicate Marking](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel#duplicate-marking) * [SNV](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel#snv) * [Annotation](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel#annotation) * [HLA](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel#hla) * [CNV](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel#cnv) * [CNV Panel of Normals (PON)](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel#cnv-panel-of-normals-pon) Was this helpful? Copy --fastq-list $PATH --fastq-list-sample-id $STRING Copy --fastq-file1 $PATH --fastq-file2 $PATH --RGSM $STRING --RGID $STRING Copy --bam-input $PATH Copy --cram-input $PATH Copy /opt/dragen/$VERSION/bin/dragen #DRAGEN install path --ref-dir $REF_DIR #path to DRAGEN graph hashtable --output-directory $OUTPUT --intermediate-results-dir $PATH #e.g. SDD /staging --output-file-prefix $PREFIX --fastq-list $PATH #see 'Input Options' for FQ, BAM or CRAM --fastq-list-sample-id $STRING --enable-cnv true --cnv-target-bed $PATH Copy /opt/dragen/$VERSION/bin/dragen #DRAGEN install path --ref-dir $REF_DIR #path to DRAGEN graph hashtable --output-directory $OUTPUT --intermediate-results-dir $PATH #e.g. SDD /staging --output-file-prefix $PREFIX --enable-cnv true --cnv-generate-combined-counts true --cnv-normals-list $CNV_NORMALS_LIST --- # Targeted Caller | DRAGEN v4.3 | DRAGEN Repetitive regions in the human genome pose a challenge for general variant calling approaches which typically cannot make use of potentially misplaced MAPQ0 reads. Furthermore, high sequence homology of some genes with a pseudogene paralog can lead to a wide variety of common structural variants (SVs) in the population, requiring specialized targeted calling approaches. DRAGEN supports targeted calling for a number of genes/targets as described in subsequent target-specific sections. The targeted caller can be enabled using the command line option `--enable-targeted=true` or a subset of targets can be enabled by providing a space-separated list of target names. The supported target names are: `cyp2b6`, `cyp2d6`, `cyp21a2`, `gba`, `hba`, `lpa`, `rh`, and `smn`. For a list of all supported targeted caller options along with their default values, see [Targeted Caller Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/command-line-options#targeted-caller-options) . The targeted caller produces a `.targeted.json` file containing a summary of the variant caller results for each target. Additional detail of individual variant calls are reported in VCF format in the `.targeted.vcf.gz` output file. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#input-data) Input Data ------------------------------------------------------------------------------------------------------------------------------------------------ The targeted caller requires WGS data aligned to a human reference genome with at least 30x coverage. The caller may be less reliable at lower coverage. Human reference genome builds based on `hg19`, `hs37d5` (including `GRCh37`), or `hg38` are supported. The targeted caller should not be enabled with low-coverage, exome or enrichment sequencing data. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#output-files) Output Files ---------------------------------------------------------------------------------------------------------------------------------------------------- ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#targeted-json-file) Targeted JSON File The targeted caller generates a `.targeted.json` file in the output directory. The output file is a JSON formatted file containing the fields below. Fields in JSON Explanation Type and Possible Values Present sampleId The sample name. string always softwareVersion The version of DRAGEN. string always phenotypeDatabaseSources Resources used for calling metabolism status (phenotype). json array of strings CYP2B6 or CYP2D6 is enabled cyp2b6 The CYP2B6 caller fields. dictionary CYP2B6 caller is enabled cyp2d6 The CYP2D6 caller fields. dictionary CYP2D6 caller is enabled cyp21a2 The CYP21A2 caller fields. dictionary CYP21A2 caller is enabled gba The GBA caller fields. dictionary GBA caller is enabled hba The HBA caller fields. dictionary HBA caller is enabled lpa The LPA caller fields. dictionary LPA caller is enabled rh The RH caller fields. dictionary RH caller is enabled smn The SMN caller fields. dictionary SMN caller is enabled ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#targeted-vcf-file) Targeted VCF File The targeted caller generates a `.targeted.vcf.gz` file in the output directory. The output file is a `VCFv4.2` formatted file. The targets that have VCF output are: cyp21a2, gba, hba, lpa, rh, and smn. Small variants, structural variants, and copy number variants are reported in the same VCF file. The `.targeted.vcf.gz` file includes the following `source` header line: For lpa, rh and smn targets, the `EVENT` and `EVENTTYPE` INFO fields are used to identify the called variants. The `EVENT` and `EVENTTYPE` INFO fields are formally introduced in `VCFv4.4` to enable the representation of complex rearrangements. This is achieved using the `EVENT` field to group all the related VCF records together, and the `EVENTTYPE` to classify the event. The corresponding header lines are the following. However, the use of `EVENT` is not limited to complex rearrangements and can be used to associate nonsymbolic alleles, for example in cases of variant position ambiguity in high homology regions. Since the `EVENTTYPE` values are implementation-defined, custom `EVENTTYPE` header lines are included to describe each `EVENTTYPE`. For cyp21a2, gba, and hba targets, the `ALLELE_ID` INFO field is used to identify the called variant alleles. The missing value `.` is used when no identifier is available (e.g. a wild type allele) or applicable (e.g. allele index 0 for a structural variant record). #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#nonrecombinant-like-variants-in-high-homology-regions) Nonrecombinant-like Variants In High Homology Regions In the case of target variants in a high homology region, each variant is reported ambiguously at all corresponding homologous positions (i.e. in both the pseudogene and in the target gene). Additional analysis for these variants can be performed if absolute certainty that these variants are located in the target gene (e.g. in gba or cyp21a2) is required. For lpa and smn the ploidy of the called genotype (`FORMAT/GT` field) corresponds to the combined copy number from all the homologous positions. For cyp21a2, gba and hba, this "joint" genotype from all the homologous positions is instead reported in a separate `FORMAT/JGT` field which is then collapsed into a diploid genotype and reported in the `FORMAT/GT` field. The following fields are reported for "joint" calls: Note that the `FORMAT/GQ` and `FORMAT/JGQ` fields contain the unconditional genotype quality, unlike the VCF spec where `FORMAT/GQ` is defined as the genotype quality conditioned on the site being variant. ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3660538983-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FpFn0gLe5V859SXejQNAW%252Fuploads%252Fgit-blob-e43ba60ee72d294f505e348e92d78d494d8508ad%252Fhigh-homology-region-variants.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=11851f1f&sv=2) High Homology Region Variant Example In the depicted example there are two genes A and B that include a high homology region. The usual process to call variants in this regions is to make a joint pileup of the reads aligning in both genes A and B and call the variants using a model with a ploidy proportional to the total copy number of the regions. This generates divergent possible genotypes that are equally likely since the variant cannot be confidently placed in either gene A or gene B. For lpa and smn the variant would be reported as follows: Given the unconventional ploidy of the `FORMAT/GT` field used in this representation, a `TargetedRepeatConflict` filter is applied to these records. The header line for the filter is the following. For cyp21a2, gba and hba, a conventional diploid `FORMAT/GT` is reported and so no `TargetedRepeatConflict` filter is applied. Due to the ambiguity in placing target variants in high homology regions, the corresponding `QUAL` and `FORMAT/GQ` fields can be much lower than conventional small variant calls (i.e. Phred 3 for a single variant allele copy across two homologous diploid positions). Therefore, instead of filtering on `QUAL` and `FORMAT/GQ` for these records, the records are filtered based on the `FORMAT/JVQL` and `FORMAT/JGQ` fields: Since the wild type alleles at homologous positions may be different from each other or different from the reference alleles, an additional filter is applied when only wild type alleles are detected across the homologous positions. This avoids making ambiguous variant calls when no target variant of interest is detected. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#rh-gene-conversion-events) Rh Gene Conversion Events In the case of an identified gene conversion even in rh, a small variant is reported at each differentiating site in the acceptor region. ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3660538983-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FpFn0gLe5V859SXejQNAW%252Fuploads%252Fgit-blob-6d3761fadb3a2a51842ce18c1a458e744160a52b%252Fgene-conversion.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=66375886&sv=2) Gene Conversion Example In the depicted example there are two genes A and B and gene A is the acceptor of a gene conversion from gene B (green box in the figure). Gene conversion are identified by observing variations in copy number at differentiating sites (blue and pink bars in the figure) in consecutive regions. Copy number variations between regions define the breakends of the gene conversion. An equivalent VCF representation for gene conversion would be using CNV and SV entries with breakends corresponding to the donor/acceptor regions, however, only the small variant representation is currently supported. In the case of a detected gene conversion event, there may be differentiating sites with a genotype that is inconsistent with that gene conversion event. In these cases the `RecombinantConflict` filter is applied. The `RecombinantConflict` is defined by the following header line. In the example, the resulting representation is as follows. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#nonallelic-homologous-recombination) Nonallelic Homologous Recombination For cyp21a2 and gba, nonallelic homologous recombination can result in gene deletion or duplication in the case of reciprocal recombination or gene conversion in the case of nonreciprocal recombination. Both gene deletion and gene conversion can introduce loss-of-function variants and in both cases the targeted caller will report these variants in the target gene. In the case of gene deletion, the differentiating sites at the nontarget (i.e. pseudogene) positions will contain the overlapping deletion allele `*` while the differentiating sites in the target will contain any variant alleles. Although an equivalent VCF representation would be to simply report the deletion with a single structural variant VCF record, reporting small variant VCF records in the target gene allows for identification of the specific mutations that may occur in a gene transcript and matches well with annotation using HGVS nomenclature. Similarly, for gene conversions, variants are reported at differentiating sites in the target gene, rather than as pairs of structural variant breakends. Calls at differentiating sites within the recombinant variant calling region will contain the same "joint" fields as are reported for nonrecombinant-like variants in high homology regions (see [Nonrecombinant-like Variants In High Homology Regions](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#nonrecombinant-like-variants-in-high-homology-regions) ). However, the collapsed diploid `FORMAT/GT` will be based on any detected recombination events. Because detected recombinant variants are placed in the target gene, these records are filtered differently than the ambiguously placed, nonrecombinant-like variants in high homology regions. The `INFO/Recombinant` flag is added to calls derived from recombinant variant calling to distinguish them from nonrecombinant-like variant calls in high homology regions. The `FORMAT/VQL` field is used to apply the `RecombinantLowVQL` filter for low quality recombinant variants and the `RecombinantREF` filter is applied when the collapsed diploid `FORMAT/GT` contains only reference alleles. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#overlapping-structural-variant-representation) Overlapping Structural Variant Representation The use of `GT=0` for symbolic structural variant alleles is formally disambiguated in `VCFv4.4`, specifying that _"GT=0 indicates the absence of any of the ALT symbolic structural variants defined in the record"_. With this convention we can report compound overlapping heterozygous structural variants. ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3660538983-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FpFn0gLe5V859SXejQNAW%252Fuploads%252Fgit-blob-010a80b182e07a93942d96e23dc48334979be703%252Foverlapping-variants-representation.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=9cd727a6&sv=2) Overlapping Variants Representation Example In the hba genotype depicted above, two overlapping SVs can be represented as follows: The relevant header lines for the VCF records above are: #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#variable-number-tandem-repeat-representation) Variable Number Tandem Repeat Representation ![](https://help.dragen.illumina.com/~gitbook/image?url=https%3A%2F%2F3660538983-files.gitbook.io%2F%7E%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FpFn0gLe5V859SXejQNAW%252Fuploads%252Fgit-blob-2f52b64a69c3e05051df1e1ceef1868be94bba49%252Fvntr.png%3Falt%3Dmedia&width=768&dpr=3&quality=100&sign=20d63601&sv=2) VNTR Example In the depicted example there is a Variable Number Tandem Repeat (VNTR) region composed of three repeat units in the reference. The `CN` INFO field is used to report the allele copy number, the `CN` FORMAT field to is used report the region total copy number given by the sum of the allele copy numbers, and the `REPCN` FORMAT field is used to report the repeat unit copy number equal to the allele copy number multiplied by the number of repeat units in the reference. This VNTR can be represented as follows: The `REPCN` and `CN` header lines are: #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#additional-filters) Additional Filters For lpa, rh and smn, the `TargetedLowQual` filter is applied if the `QUAL` of a target variant is less than `3.00`. Similarly, for cyp21a2 and gba the `TargetedLowVQL` filter is applied if the `VQL` of a target variant in low-homology region is less than `3.00`. The `TargetedLowGQ` filter is applied if the targeted variant has `GQ` smaller than `3`. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#merging-targeted-calls-in-the-hard-filtered-files) Merging Targeted Calls In The `hard-filtered` Files When the small variant caller is enabled, the targeted small variant VCF calls can be merged into the `.hard-filtered.vcf.gz` and `.hard-filtered.gvcf.gz` files, briefly `hard-filtered` files. The `--targeted-merge-vc` command line option can be used to control which targets will have their small variant VCF records merged into the `hard-filtered` files. For example, `--targeted-merge-vc rh` will enable merging of the calls from the `rh` caller into the `hard-filtered` files and `--targeted-merge-vc rh hba` will enable merging of the calls from the `rh` and `hba` targets into the `hard-filtered` files. The `true` value will merge all calls from all supported targets into the `hard-filtered` files, while the `false` value will merge no calls into the `hard-filtered` files. The targeted calls merged into the `hard-filtered` files are marked with a `TARGETED` INFO flag. When enabled, targeted small variants are merged into the `hard-filtered` files regardless of any regions that may be provided using the `--vc-target-bed` option. #### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#merging-strategy) Merging Strategy The merging strategy for targeted small variant calls is to prioritize the targeted calls over small variant calls from the germline small variant caller. When a germline small variant call overlaps a targeted caller call, then the small variant call is filtered with a `TargetedConflict` filter if any of the following holds: * The targeted caller call is `PASS`. * The small variant call and targeted caller call have incompatible genotypes and the targeted caller call is not filtered with the `TargetedLowGQ` filter. The strategy is summarized in the following examples. 1. The `TARGETED` call is `PASS`. 1. The `TARGETED` call and the small variant call are not overlapping 1. The `TARGETED` call is filtered with `TargetedLowQual` and has a discordant variant representation with the overlapping small variant call. 1. The `TARGETED` call is filtered with `TargetedLowQual` and has a discordant genotype with the overlapping small variant call. 1. The `TARGETED` call is filtered with `TargetedLowGQ` and has a discordant genotype with the overlapping small variant call. [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#command-line-examples) Command-Line Examples ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- The targeted caller can be enabled in parallel with other components as part of a human WGS germline analysis workflow (see [DRAGEN Recipe - Germline WGS](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs) ). ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#fastq-input-example) FASTQ Input Example The following command-line example runs the targeted caller from FASTQ input: ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#prealigned-bam-input-example) Prealigned BAM Input Example The following command-line example runs cyp21a2 only using BAM input without realignment: [PreviousDe Novo Repeat Expansion Detectionchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/repeat-expansions/de-novo-str-detection) [NextCYPDB6 Callerchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller/cyp2b6-calling) Last updated 7 months ago Was this helpful? * [Input Data](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#input-data) * [Output Files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#output-files) * [Targeted JSON File](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#targeted-json-file) * [Targeted VCF File](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#targeted-vcf-file) * [Merging Targeted Calls In The hard-filtered Files](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#merging-targeted-calls-in-the-hard-filtered-files) * [Command-Line Examples](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#command-line-examples) * [FASTQ Input Example](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#fastq-input-example) * [Prealigned BAM Input Example](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/targeted-caller#prealigned-bam-input-example) Was this helpful? Copy ##source=DRAGEN_TARGETED Copy ##INFO= ##INFO= Copy ##EVENTTYPE= ##EVENTTYPE= ##EVENTTYPE= Copy ##INFO= Copy ##INFO= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= ##FORMAT= Copy chr1 100 . A T . TargetedRepeatConflict EVENT=GeneA-B:50A>T;EVENTTYPE=VARIANT_IN_HOMOLOGY_REGION GT 0/0/0/1 chr1 200 . A T . TargetedRepeatConflict EVENT=GeneA-B:50A>T;EVENTTYPE=VARIANT_IN_HOMOLOGY_REGION GT 0/0/0/1 Copy ##FILTER= Copy ##FILTER= ##FILTER= Copy ##FILTER= Copy chr1 121 . A T . PASS EVENT=GC_AB;EVENTTYPE=GENE_CONVERSION; GT:PS 0|1:121 ... chr1 280 . G A . PASS EVENT=GC_AB;EVENTTYPE=GENE_CONVERSION; GT:PS 0|1:121 Copy ##FILTER= Copy chr1 121 . A T . PASS EVENT=GC_AB;EVENTTYPE=GENE_CONVERSION; GT:PS 0|1:121 ... chr1 144 . C T . RecombinantConflict EVENT=GC_AB;EVENTTYPE=GENE_CONVERSION; GT:PS 1|1:121 chr1 153 . A G . RecombinantConflict EVENT=GC_AB;EVENTTYPE=GENE_CONVERSION; GT 0/0 ... chr1 280 . G A . PASS EVENT=GC_AB;EVENTTYPE=GENE_CONVERSION; GT:PS 0|1:121 Copy ##FORMAT= ##FILTER= ##FILTER= Copy chr16 170262 . G , . . END=174517;IMPRECISE;SVLEN=4255,4255;SVCLAIM=DJ,DJ;ALLELE_ID=.,-a4.2,aaa4.2 GT 0/2 chr16 173301 . A , . . END=177104;IMPRECISE;SVLEN=3804,3804;SVCLAIM=DJ,DJ;ALLELE_ID=.,-a3.7,aaa3.7 GT 0/1 Copy ##INFO= ##INFO= ##INFO= ##INFO= Copy chr1 100 . A , . . END=400;EVENT=A;EVENTTYPE=VNTR;SVCLAIM=D;SVLEN=300;CN=2.6,4.3 GT:CN:REPCN 1|2:6.9:8|13 Copy ##FORMAT= ##INFO= ##FORMAT= Copy ##FILTER= Copy ##FORMAT= ##FILTER= Copy ##FILTER= Copy chr1 100 . A C . TargetedConflict . GT 0/1 chr1 100 . A C . PASS TARGETED GT 1/1 Copy chr1 110 . T TCA . PASS . GT 0/1 chr1 111 . G A . PASS TARGETED GT 0/1 Copy chr1 120 . ATTC A . TargetedConflict . GT 0/1 chr1 121 . T A . TargetedLowQual TARGETED GT 0/1 chr1 125 . TCAC T . TargetedLowQual TARGETED GT 0/1 chr1 126 . C G . TargetedConflict . GT 0/1 Copy chr1 130 . C G . TargetedConflict . GT 0/1 chr1 130 . C G . TargetedLowQual TARGETED GT 1/1 Copy chr1 140 . AC A . PASS . GT:GQ 0/1:5 chr1 140 . A T . TargetedLowGQ TARGETED GT:GQ 1/1:2 Copy dragen \ -r /staging/human/reference/hg38_alt_aware/DRAGEN/${HASH_TABLE_VERSION} \ --fastq-file1 /staging/test/data/NA12878_R1.fastq \ --fastq-file2 /staging/test/data/NA12878_R2.fastq \ --output-directory /staging/test/output \ --output-file-prefix NA12878_dragen \ --RGID DRAGEN_RGID \ --RGSM NA12878 \ --enable-targeted=true Copy dragen \ -r /staging/human/reference/hg38_alt_aware/DRAGEN/${HASH_TABLE_VERSION} \ --bam-input /staging/test/data/NA12878.bam \ --output-directory /staging/test/output \ --output-file-prefix NA12878_dragen \ --enable-map-align=false \ --enable-targeted=cyp21a2 --- # DNA Germline Panel UMI | DRAGEN v4.3 | DRAGEN The DRAGEN recipe includes the recommended pipeline specific commands. A DRAGEN recipe is a predefined set of analysis parameters and workflow settings tailored for a specific type of genomic analysis. Some default parameters are included for clarity and are marked with comments. Copy /opt/dragen/$VERSION/bin/dragen #DRAGEN install path --ref-dir $REF_DIR #path to DRAGEN graph hashtable --output-directory $OUTPUT --intermediate-results-dir $PATH #e.g. SDD /staging --output-file-prefix $PREFIX # Inputs --fastq-list $PATH #see 'Input Options' for FQ, BAM or CRAM --fastq-list-sample-id $STRING # Mapper --enable-map-align true #optional with BAM/CRAM input --enable-map-align-output true #optionally save the output BAM --enable-sort true #default=true # UMI --umi-enable true --umi-source STRING #Default='qname' --umi-library-type STRING #e.g. random-duplex --umi-metrics-interval-file $BED --remove-duplicates false --umi-min-supporting-reads 1 #Default=2 # Small variant caller --enable-variant-caller true --vc-target-bed $VC_TARGET_BED # Annotation --variant-annotation-data PATH --variant-annotation-assembly GRCh37/8 --enable-variant-annotation true # SV --enable-sv true --sv-exome true --sv-call-regions-bed $SV_TARGET_BED # CNV --enable-cnv true --cnv-target-bed $PATH --cnv-combined-counts $PATH #CNV PON # HLA genotyper --enable-hla true --hla-enable-class-2 true #optional if assay covers class II HLA regions --hla-as-filter-min-threshold 29.0 #panel specific setting --hla-as-filter-ratio-threshold 0.85 #panel specific setting [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi#notes-and-additional-options) Notes and additional options -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi#hashtable) Hashtable For DRAGEN germline runs, it is recommended to use the graph hashtable. See: [Product Filesarrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi#input-options) Input options DRAGEN input sources include: fastq list, fastq, bam, or cram. FQ list Input FQ Input BAM Input CRAM Input ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi#mapping-and-aligning) Mapping and Aligning Option Description `--enable-map-align true` Optionally disable map & align (default=true). `--enable-map-align-output true` Optionally save the output BAM (default=false). `--Aligner.clip-pe-overhang 2` Clean up any unwanted UMI indexes. Only use when reads contain UMIs, but UMI collapsing was not run. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi#umi) UMI Option Description `--umi-source STRING` Specify the input type for the UMI sequence. Options: `qname`, `fastq`, `bamtag`. `--umi-library-type STRING` Set the batch option for different UMIs correction. Options: `random-duplex`, `random-simplex`, `nonrandom-duplex`. `--umi-nonrandom-whitelist $PATH` If UMI is nonrandom, either a whitelist or correction table is required. The whitelist includes a valid UMI sequence per line. `--umi-correction-table $PATH` If UMI is nonrandom, either a whitelist or correction table is required. The correction table defaults to the table used by TruSight Oncology: /resources/umi/umi\_correction\_table.txt.gz. `--umi-min-supporting-reads INT` Specify the number of matching UMI input reads required to generate a consensus read. Any family with insufficient supporting reads is discarded. The default is 2, but most pipelines perform better with this setting set to 1. A setting of 2 may potentially be relevant for samples with ultra deep coverage (e.g. ctDNA). `--umi-metrics-interval-file $BED` Target region in BED format. `--umi-emit-multiplicity both` Set the consensus sequence type to output. DRAGEN UMI allows collapsing duplex sequences from the two strands of the original molecules. For more information, see [Merge Duplex UMIs](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/unique-molecular-identifiers#merge-duplex-umis) . `--umi-start-mask-length INT` Number of additional bases to ignore from start of read. The default is 0. To reduce FP optionally set to 1. `--umi-end-mask-length INT` Number of additional bases to ignore from end of read. The default is 0. To reduce FP optionally set to 3. For more information see: [UMI Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/unique-molecular-identifiers#umi-options) . ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi#snv) SNV DRAGEN SNV VC employs machine learning based variant recalibration (DRAGEN-ML). It processes read and other contextual evidence to remove false positives, recover false negatives and reduce zygosity errors. No additional setup is required. DRAGEN-ML is enabled by default as needed, when running the germline SNV VC on hg19 or hg38. Note that we do not recommend changing the default QUAL thresholds of 3 for DRAGEN-ML and 10 for DRAGEN without ML. These values differ from each other because DRAGEN-ML improves the calibration of QUAL scores, leading to a change in the scoring range. Option Description `--vc-target-bed` Limit variant calling to region of interest. `--vc-combine-phased-variants-distance INT` Maximum distance over which phased variants will be combined. Set to 0 to disable. Valid range is \[0; 15\] (Default=2) `--vc-emit-ref-confidence GVCF` To enable gVCF output. `--vc-enable-vcf-output` To enable VCF file output during a gVCF run, set to true. The default value is false. For more detail on the small variant caller in somatic mode please refer to [Somatic Mode](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/small-variant-calling/somatic-mode) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi#annotation) Annotation For instructions on how to download the Nirvana annotation database, please refer to [Nirvana](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/nirvana) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi#hla) HLA Option Description `--enable-hla` Enable HLA typer (this setting by default will only genotype class 1 genes) `--hla-as-filter-min-threshold` Internal option to set min alignment score threshold. The default is 59 and works for WES and WGS. Set to 29 for panels. `--hla-as-filter-ratio-threshold` Minimum Alignment score of a read mate to be considered. The default is 0.67 and works for WES and WES. Set to 0.85 for panels. `--hla-enable-class-2` Extend genotyping to HLA class 2 genes (default=true). ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi#cnv) CNV Option Description `--cnv-enable-gcbias-correction true` Enable or disable GC bias correction when generating target counts. `--cnv-segmentation-mode $SEG_MODE` Option to override the default segmentation algorithm. Defaults include `slm` for germline WGS, `aslm` for somatic WGS, and `hslm` for targeted analysis. `--cnv-segmentation-bed $PATH` If you are using somatic targeted panels with a set of genes supplied with the capture kit, then you can bypass segmentation by specifying a cnv-segmentation-bed and using cnv-segmentation-mode=bed. For more information, see [CNV Calling](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/cnv-calling) . ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi#cnv-panel-of-normals-pon) CNV Panel of Normals (PON) The panel of normals mode uses a set of matched normal samples to determine the baseline level from which to call CNV events. These matched normal samples should be derived from the same library prep and sequencing workflow that was used for the case sample. CNV requires PON files for all targeted analyses (including panels, exomes, germline, tumor-only and tumor-normal workflows). It is recommended to use 30-100 normal samples when building the PON, but fewer may be used. If sample coverage noise is relatively stable, as few as 5 PON samples may yield acceptable results. Follow the two steps below to generate CNV PON: **Step 1. Generate target counts of individual normal samples.** Any options used for panel of normals generation (BED file, GC Bias Correction, etc) should be matched when processing the case sample. **Step 2. Combined counts generation.** Individual PON counts can be merged into a single file as a `.combined.counts.txt.gz` file. `$CNV_NORMALS_LIST` is a single lines file with paths to each target counts file generated by step1 (either `.target.counts.gz` or `.target.counts.gc-corrected.gz`). Output will have a PON file with suffix `.combined.counts.txt.gz` file. Use the PON file in case sample runs of DRAGEN CNV with `--cnv-combined-counts` option. For more information, see [Panel of Normals](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/cnv-calling) . In some cases, an in-run PON containing germline samples from the same batch (i.e. sample source, DNA extraction, library prep and sequencing run) may provide superior normalization. Analysis of a full batch of germline samples with an automatically generated in-run PON can be performed using [DRAGEN Enrichment on BaseSpacearrow-up-right](https://www.illumina.com/products/by-type/informatics-products/basespace-sequence-hub/apps/dragen-enrichment.html) or DRAGEN Germline Enrichment on [ICAarrow-up-right](https://www.illumina.com/products/by-type/informatics-products/connected-analytics.html) . CNV PONs can also be built in the cloud using the [DRAGEN Baseline Builder App on BaseSpacearrow-up-right](https://www.illumina.com/products/by-type/informatics-products/basespace-sequence-hub/apps/dragen-baseline-builder.html) or the DRAGEN Systematic Noise File Builder Pipeline on [ICAarrow-up-right](https://www.illumina.com/products/by-type/informatics-products/connected-analytics.html) . [PreviousDRAGEN Recipeschevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes) [NextDNA Germline Panelchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel) Last updated 7 months ago Was this helpful? * [Notes and additional options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi#notes-and-additional-options) * [Hashtable](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi#hashtable) * [Input options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi#input-options) * [Mapping and Aligning](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi#mapping-and-aligning) * [UMI](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi#umi) * [SNV](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi#snv) * [Annotation](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi#annotation) * [HLA](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi#hla) * [CNV](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi#cnv) * [CNV Panel of Normals (PON)](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-panel-umi#cnv-panel-of-normals-pon) Was this helpful? Copy --fastq-list $PATH --fastq-list-sample-id $STRING Copy --fastq-file1 $PATH --fastq-file2 $PATH --RGSM $STRING --RGID $STRING Copy --bam-input $PATH Copy --cram-input $PATH Copy /opt/dragen/$VERSION/bin/dragen #DRAGEN install path --ref-dir $REF_DIR #path to DRAGEN graph hashtable --output-directory $OUTPUT --intermediate-results-dir $PATH #e.g. SDD /staging --output-file-prefix $PREFIX --fastq-list $PATH #see 'Input Options' for FQ, BAM or CRAM --fastq-list-sample-id $STRING --enable-cnv true --cnv-target-bed $PATH Copy /opt/dragen/$VERSION/bin/dragen #DRAGEN install path --ref-dir $REF_DIR #path to DRAGEN graph hashtable --output-directory $OUTPUT --intermediate-results-dir $PATH #e.g. SDD /staging --output-file-prefix $PREFIX --enable-cnv true --cnv-generate-combined-counts true --cnv-normals-list $CNV_NORMALS_LIST --- # DNA Germline WGS UMI | DRAGEN v4.3 | DRAGEN The DRAGEN recipe includes the recommended pipeline specific commands. A DRAGEN recipe is a predefined set of analysis parameters and workflow settings tailored for a specific type of genomic analysis. Some default parameters are included for clarity and are marked with comments. Copy /opt/dragen/$VERSION/bin/dragen #DRAGEN install path --ref-dir $REF_DIR #path to DRAGEN graph hashtable --output-directory $OUTPUT --intermediate-results-dir $PATH #e.g. SDD /staging --output-file-prefix $PREFIX # Inputs --fastq-list $PATH #see 'Input Options' for FQ, BAM or CRAM --fastq-list-sample-id $STRING # Mapper --enable-map-align true #optional with BAM/CRAM input --enable-map-align-output true #optionally save the output BAM --enable-sort true #default=true # UMI --umi-enable true --umi-source STRING #Default='qname' --umi-library-type STRING #e.g. random-duplex --umi-metrics-interval-file $BED --remove-duplicates false --umi-min-supporting-reads 1 #Default=2 # Small variant caller --enable-variant-caller true # Annotation --variant-annotation-data PATH --variant-annotation-assembly GRCh37/8 --enable-variant-annotation true # SV --enable-sv true # CNV --enable-cnv true --cnv-enable-self-normalization true # HLA genotyper --enable-hla true --hla-enable-class-2 true #optional if assay covers class II HLA regions # Targeted caller --enable-targeted true #Targeted # Star allele --enable-star-allele true # PGX --enable-pgx true #PGX # Short tandem repeats --repeat-genotype-enable true [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs-umi#notes-and-additional-options) Notes and additional options ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs-umi#hashtable) Hashtable For DRAGEN germline runs, it is recommended to use the graph hashtable. See: [Product Filesarrow-up-right](https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs-umi#input-options) Input options DRAGEN input sources include: fastq list, fastq, bam, or cram. FQ list Input FQ Input BAM Input CRAM Input ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs-umi#mapping-and-aligning) Mapping and Aligning Option Description `--enable-map-align true` Optionally disable map & align (default=true). `--enable-map-align-output true` Optionally save the output BAM (default=false). `--Aligner.clip-pe-overhang 2` Clean up any unwanted UMI indexes. Only use when reads contain UMIs, but UMI collapsing was not run. ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs-umi#umi) UMI Option Description `--umi-source STRING` Specify the input type for the UMI sequence. Options: `qname`, `fastq`, `bamtag`. `--umi-library-type STRING` Set the batch option for different UMIs correction. Options: `random-duplex`, `random-simplex`, `nonrandom-duplex`. `--umi-nonrandom-whitelist $PATH` If UMI is nonrandom, either a whitelist or correction table is required. The whitelist includes a valid UMI sequence per line. `--umi-correction-table $PATH` If UMI is nonrandom, either a whitelist or correction table is required. The correction table defaults to the table used by TruSight Oncology: /resources/umi/umi\_correction\_table.txt.gz. `--umi-min-supporting-reads INT` Specify the number of matching UMI input reads required to generate a consensus read. Any family with insufficient supporting reads is discarded. The default is 2, but most pipelines perform better with this setting set to 1. A setting of 2 may potentially be relevant for samples with ultra deep coverage (e.g. ctDNA). `--umi-metrics-interval-file $BED` Target region in BED format. `--umi-emit-multiplicity both` Set the consensus sequence type to output. DRAGEN UMI allows collapsing duplex sequences from the two strands of the original molecules. For more information, see [Merge Duplex UMIs](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/unique-molecular-identifiers#merge-duplex-umis) . `--umi-start-mask-length INT` Number of additional bases to ignore from start of read. The default is 0. To reduce FP optionally set to 1. `--umi-end-mask-length INT` Number of additional bases to ignore from end of read. The default is 0. To reduce FP optionally set to 3. For more information see: [UMI Options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/unique-molecular-identifiers#umi-options) . ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs-umi#snv) SNV DRAGEN SNV VC employs machine learning based variant recalibration (DRAGEN-ML). It processes read and other contextual evidence to remove false positives, recover false negatives and reduce zygosity errors. No additional setup is required. DRAGEN-ML is enabled by default as needed, when running the germline SNV VC on hg19 or hg38. Note that we do not recommend changing the default QUAL thresholds of 3 for DRAGEN-ML and 10 for DRAGEN without ML. These values differ from each other because DRAGEN-ML improves the calibration of QUAL scores, leading to a change in the scoring range. Option Description `--vc-target-bed` Limit variant calling to region of interest. `--vc-combine-phased-variants-distance INT` Maximum distance over which phased variants will be combined. Set to 0 to disable. Valid range is \[0; 15\] (Default=2) `--vc-emit-ref-confidence GVCF` To enable gVCF output. `--vc-enable-vcf-output` To enable VCF file output during a gVCF run, set to true. The default value is false. For more detail on the small variant caller in somatic mode please refer to [Somatic Mode](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/small-variant-calling/somatic-mode) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs-umi#annotation) Annotation For instructions on how to download the Nirvana annotation database, please refer to [Nirvana](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/nirvana) ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs-umi#hla) HLA Option Description `--enable-hla` Enable HLA typer (this setting by default will only genotype class 1 genes) `--hla-as-filter-min-threshold` Internal option to set min alignment score threshold. The default is 59 and works for WES and WGS. Set to 29 for panels. `--hla-as-filter-ratio-threshold` Minimum Alignment score of a read mate to be considered. The default is 0.67 and works for WES and WES. Set to 0.85 for panels. `--hla-enable-class-2` Extend genotyping to HLA class 2 genes (default=true). ### [hashtag](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs-umi#cnv) CNV Option Description `--cnv-enable-gcbias-correction true` Enable or disable GC bias correction when generating target counts. `--cnv-segmentation-mode $SEG_MODE` Option to override the default segmentation algorithm. Defaults include `slm` for germline WGS, `aslm` for somatic WGS, and `hslm` for targeted analysis. For more information, see [CNV Calling](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-dna-pipeline/cnv-calling) . [PreviousDNA Germline WESchevron-left](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wes) [NextDNA Germline WGSchevron-right](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs) Last updated 7 months ago Was this helpful? * [Notes and additional options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs-umi#notes-and-additional-options) * [Hashtable](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs-umi#hashtable) * [Input options](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs-umi#input-options) * [Mapping and Aligning](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs-umi#mapping-and-aligning) * [UMI](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs-umi#umi) * [SNV](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs-umi#snv) * [Annotation](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs-umi#annotation) * [HLA](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs-umi#hla) * [CNV](https://help.dragen.illumina.com/dragen-v4.3/product-guide/dragen-v4.3/dragen-recipes/dna-germline-wgs-umi#cnv) Was this helpful? Copy --fastq-list $PATH --fastq-list-sample-id $STRING Copy --fastq-file1 $PATH --fastq-file2 $PATH --RGSM $STRING --RGID $STRING Copy --bam-input $PATH Copy --cram-input $PATH ---