DNA Germline WES UMI

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.

  
/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 
# 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 
# Small variant caller 
--enable-variant-caller true 
--vc-target-bed $VC_TARGET_BED 
# Annotation 
--variant-annotation-data $NIRVANA_PATH 
--enable-variant-annotation true 
# SV 
--enable-sv true 
--sv-exome true 
--sv-target-bed $SV_TARGET_BED 
# CNV 
--enable-cnv true 
--cnv-population-b-allele-vcf $POP_VCF  #needed for AOH detection 
--cnv-target-bed $PATH 
--cnv-combined-counts $PATH             #CNV PON. See 'In-run PON' section below. 
# HLA genotyper 
--enable-hla true 
--hla-exome true                        #Set HLA output to 3 field resolution (coding sequence only) 
# Targeted caller (only if using the Illumina CS/PGx Custom Enrichment Research Panel) 
--enable-targeted true 
--targeted-pon $PATH                    #Targeted PON. See 'In-run PON' section below. 
--targeted-systematic-noise $PATH       #Targeted systematic noise file 

Notes and additional options

Hashtable

For DRAGEN germline runs, it is recommended to use the pangenome hashtable.

See: Product Files

Input options

DRAGEN input sources include: fastq list, fastq, bam, or cram. For BCL input, first create FASTQs using BCL conversion.

FQ list Input

--fastq-list $PATH 
--fastq-list-sample-id $STRING 

FQ Input

--fastq-file1 $PATH 
--fastq-file2 $PATH 
--RGSM $STRING 
--RGID $STRING 

BAM Input

--bam-input $PATH 

CRAM Input

--cram-input $PATH 

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.

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: <INSTALL_PATH>/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. Most pipelines perform better with a setting of 1. A setting of 2 may potentially be relevant for samples with ultra deep coverage (e.g. ctDNA). (Default since DRAGEN V4.5 is 1)
--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.
--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.

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 in base pairs (BP) over which phased variants will be combined. Set to 0 to disable. Valid range is [0; 15] BP (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.

Annotation

For instructions on how to download the Nirvana annotation database, please refer to Nirvana

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).
--hla-exome	Output HLA alleles at 3-field resolution (default 4-field resolution) for panels targeting only coding sequences or WES.

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	Specify a segmentation bed file to add pre-defined segments to be called.
--cnv-population-b-allele-vcf $POP_VCF	Specify a population SNP VCF to enable AOH detection through allele-specific CN calling (ASCN).
--cnv-enable-cyto-output true	Enable Cytogenetics-compatible output (default true).
--cnv-enable-mosaic-calling true	Enable MOSAIC-calling mode (default true).

In-run PON

For CNV PON requirements and generation options see CNV Preprocessing | Panel of Normals.

For Targeted Caller PON requirements and generation options see Targeted Caller | Exome calling.

CNV and Targeted Caller require separate PON files, but the intermediate counts files can be generated in the same DRAGEN command line invocation. Follow the steps below to generate the CNV and Targeted Caller PON files. Note that Targeted Caller is only supported with the Illumina CS/PGx Custom Enrichment Research Panel.

Step 1. Generate CNV target counts and Targeted exome counts of individual samples from the sequencing run.

Any samples that should not be included in the final PON file can be excluded from this step. Any options used for CNV target counts generation (BED file, GC Bias Correction, etc.) should be matched when processing the case samples.

  
/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 
--fastq-list $PATH                      #see 'Input Options' for FQ, BAM or CRAM 
--fastq-list-sample-id $STRING 
# CNV 
--enable-cnv true 
--cnv-target-bed $PATH 
# Targeted caller (only if using the Illumina CS/PGx Custom Enrichment Research Panel) 
--targeted-generate-exome-counts true 

Step 2. CNV combined counts file generation.

  
/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 
--enable-cnv true 
--cnv-generate-combined-counts true 
--cnv-normals-list $CNV_NORMALS_LIST 

$CNV_NORMALS_LIST is a text file with one line for each path to a CNV target counts file generated in step 1 (either <output-file-prefix>.target.counts.gz or <output-file-prefix>.target.counts.gc-corrected.gz). Individual target counts files are merged into a single <output-file-prefix>.combined.counts.txt.gz PON file in the output directory. The PON file is used for each case sample run of DRAGEN CNV using the --cnv-combined-counts option.

Step 3. Targeted Caller PON file generation.

  
/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 
--targeted-pon-counts-list $TARGETED_PON_COUNTS_LIST 

$TARGETED_PON_COUNTS_LIST is a text file with one line for each path to a Targeted Caller exome counts file generated in step 1 (<output-file-prefix>.targeted.exome.counts.json.gz). Individual exome counts files are merged into a single <output-file-prefix>.targeted.pon.json.gz PON file in the output directory. The PON file is used for each case sample run of DRAGEN Targeted Caller using the --targeted-pon option.

Targeted Caller

A systematic noise file corresponding to one of the pre-built pangenome references can be downloaded from the [DRAGEN Software Support Site page]https://support.illumina.com/sequencing/sequencing_software/dragen-bio-it-platform/product_files.html).