DNA Germline WES 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 
--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 
# Targeted caller (only if exome panel includes spike-in for DRAGEN Targeted Calling) 
--enable-targeted true                  #Targeted 

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

--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.

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.

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.

  
/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 

Step 2. Combined counts generation.

Individual PON counts can be merged into a single file as a <prefix>.combined.counts.txt.gz file.

  
/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 

$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.

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 BaseSpace or DRAGEN Germline Enrichment on ICA.

CNV PONs can also be built in the cloud using the DRAGEN Baseline Builder App on BaseSpace or the DRAGEN Systematic Noise File Builder Pipeline on ICA.

Targeted Caller

Analysis of a full batch of germline samples with an automatically generated in-run PON can be performed using DRAGEN Enrichment on BaseSpace or DRAGEN Germline Enrichment on ICA.

A systematic noise file and corresponding pre-built pangenome reference can be downloaded from the DRAGEN Software Support Site page.

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