DNA Germline WGS UMI

The DRAGEN recipe includes the recommended pipeline specific commands.

  
/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 
# Targeted caller 
--enable-targeted true                  #Targeted 
# Star allele 
--enable-star-allele true 
# PGX 
--enable-pgx true                       #PGX 
# Short tandem repeats 
--repeat-genotype-enable true 
# Variable Number Tandem Repeat (VNTR) Caller 
--enable-vntr true 
--sv-vntr-merge false                   #do not merge into the sv vcf 

Notes and additional options

Hashtable

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

See: Product Files

Input options

DRAGEN input sources include: fastq list, fastq, bam, or cram.

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 inputs reads required to generate a consensus read. Any family with insufficient supporting reads is discarded. The default is 2.
--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 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

Annotation

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

VNTR

Option	Description
--sv-vntr-merge false	Option to disabe automatic merging of VNTR calls into SV VCF.

For more details see: VNTR Calling

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).

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.