DNA Somatic Tumor-Normal Solid WES UMI

The DRAGEN recipe includes the recommended pipeline specific commands.

New in DRAGEN V4.3.17:

The somatic tumor-normal pipeline now natively supports UMIs. UMIs can be present in either the tumor sample alone or in both the tumor and normal samples. It is no longer necessary to run a separate pre-processing step to generate BAMs. In addition, DRAGEN V4.3.17 now also supports starting from BAM or CRAM files with the --enable-map-align=true option enabled.

Important Note for Earlier V4.3.X Versions:

In these versions, UMI support is limited. You can only use UMIs in tumor-only mode or by first performing UMI collapsing separately on both the tumor and normal samples. After collapsing, the resulting BAM files can be used as input for the variant calling step.

  
/opt/dragen/$VERSION/bin/dragen         #DRAGEN install path 
--ref-dir $REF_DIR                      #path to DRAGEN linear hashtable 
--output-directory $OUTPUT 
--intermediate-results-dir $PATH        #e.g. SDD /staging 
--output-file-prefix $PREFIX 
# Inputs 
--tumor-fastq-list $PATH                #see 'Input Options' for FQ, BAM or CRAM 
--tumor-fastq-list-sample-id $STRING 
--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 
--tumor-normal-has-umi STRING           #Sample(s) containing UMI ['tumor', 'both']. 
--remove-duplicates false 
--umi-min-supporting-reads 1            #Default=2 
# Small variant caller 
--enable-variant-caller true 
--vc-target-bed $VC_TARGET_BED 
--vc-systematic-noise $PATH             #Optional 
--vc-enable-umi-solid true              #>= 1% VAF 
# SV 
--enable-sv true 
--sv-systematic-noise $PATH             #Optional 
--sv-exome true 
--sv-call-regions-bed $SV_TARGET_BED 
# CNV 
--enable-cnv true 
--cnv-use-somatic-vc-baf true 
--cnv-target-bed $PATH 
--cnv-combined-counts $PATH             #CNV PON 
# HRD Scoring 
--enable-hrd true                       #requires SNV 
# Annotation 
--variant-annotation-data PATH 
--variant-annotation-assembly GRCh37/8 
--enable-variant-annotation true 
# TMB 
--enable-tmb true 
# HLA genotyper 
--enable-hla true 
--hla-enable-class-2 true 
# Microsatellite Instability (MSI) 
--msi-command tumor-normal 
--msi-microsatellites-file $PATH 
--msi-coverage-threshold 40 

Notes and additional options

Hashtable

For DRAGEN somatic runs it is recommended to use the linear hashtable.

See: Product Files

Input options

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

FQ list Input

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

FQ Input

--tumor-fastq1 $PATH 
--tumor-fastq2 $PATH 
--RGSM-tumor $STRING 
--RGID-tumor $STRING 
--fastq-file1 $PATH 
--fastq-file2 $PATH 
--RGSM $STRING 
--RGID $STRING 

BAM Input

--tumor-bam-input $PATH 
--bam-input $PATH 

CRAM Input

--tumor-cram-input $PATH 
--cram-input $PATH 

Mapping and Aligning

Option	Description
--enable-map-align true	In the TN pipeline this must be set to false for BAM/CRAM input.
--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.

Fractional (Raw Reads) Downsampling

DRAGEN can subsample a random, fractional percentage of reads from an input file using the fractional downsampler. You can use downsampling to subsample data sets in order to simulate different amounts of sequencing. DRAGEN randomly subsamples reads from primary analysis without any modification (e.g. no trimming, no filtering, etc.).

Downsampling may be useful to reduce runtime on very deep samples. For Tumor-Normal analyses it is also recommended to use a normal sample with coverage that is less than the tumor sample. If the matched normal has deeper coverage than the tumor sample, then the fractional samples may be used to reduce coverage on the normal sample.

Option	Description
--enable-fractional-down-sampler	Set to true to enable fractional downsampling. The default value is false.
--down-sampler-normal-subsample	Specify the fraction of reads to keep as a subsample of normal input data. The default value is 1.0 (100%).
--down-sampler-tumor-subsample	Specify the fraction of reads to keep as a subsample of tumor input data. The default value is 1.0 (100%).
--down-sampler-random-seed	Specify the random seed for different runs of the same input data. The default value is 42.

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.
--tumor-normal-has-umi STRING	Specify if only the tumor, or if both the tumor and normal have UMIs. Options: 'both','tumor'.

For more information see: UMI Options.

SNV

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-systematic-noise $PATH	Systematic noise file. This filter is recommended for removing systematic noise observed in normal samples.
--vc-somatic-hotspots $PATH	DRAGEN has a default set of hotspot variants (positions and alleles) where it will assign an increased prior probability. Use this option to override with a custom hotspots file.
--vc-enable-liquid-tumor-mode true	Tumor-in-normal contamination. Only use if there is some tumor leakage in the normal control.
--vc-override-tumor-pcr-params-with-normal false	Mixed sample preparation. Only use if the tumor and normal samples exhibit different PCR (indel) noise patterns, e.g., due to using different sample preparation.
--vc-sq-filter-threshold $INT	Threshold for sensitivity-specificity tradeoff. The default threshold is 3. Raise this value to improve specificity at the cost of sensitivity, or lower it to improve sensitivity at the cost of specificity.
--vc-excluded-regions-bed $BED	Hard filter variants that overlap with this region. ALU regions comprise approximately 11% of the genome, and are often exceptionally noisy regions in FFPE samples. Optionally filter out ALU regions using the DRAGEN excluded regions filter. ALU bed files can be downloaded as part of the Bed File Collection: Bed File Collection

High-coverage sequencing panels allow for the detection of low-frequency alleles. DRAGEN supports 3 main settings for improved sensitivity on low VAF variant calls.

High Sensitivity Option	Description
--vc-target-vaf FLOAT	The default is 0.03 (3%). Set to e.g. 0.01 to improve SNV sensitivity on 1% VAF variants (assuming sufficient coverage).
--vc-enable-umi-solid true	Optimized for 1% and higher VAFs on UMI (or read position collapsed) samples with approx 300-1000X coverage.
--vc-enable-umi-liquid true	Optimized for 0.1% and higher VAFs on UMI samples with 1000X or higher coverage as expected in liquid biopsies.

For more detail on the small variant caller in somatic mode please refer to Somatic Mode

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.
--cnv-normal-cnv-vcf $CNV_NORMAL_VCF	Specify germline CNVs from the matched normal sample. Germline-aware Mode.

For more information, see CNV Calling.

Annotation

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

TMB

Option	Description
--tmb-vaf-threshold FLOAT	Variant mininum allele frequency for usable variants (default=0.05)
--vc-callability-tumor-thresh INT	Required read coverage to use a site (default=50).
--tmb-enable-proxi-filter BOOL	Use variant vaf information to increase germline filtering. Recommended for TO, but not for TN. May be overly aggressive at tagging variants as germline (default=false).

See the user guide: TMB Germline Variants.

MSI

Microsatellite sites file can be downloaded here: Product Files.

Option	Description and recommended setting
--msi-coverage-threshold INT	Minimum coverage for a microsatellite: 60 (default)
--msi-distance-threshold FLOAT	Minimum Jensen-Shannon distance between tumor and normal for a microsatellite: 0.1 (default)

SV

Option	Description
--sv-call-regions-bed	Specifies a BED file containing the set of regions to call. Optionally gzip or bgzip format.
--sv-exclusion-bed	Specifies a BED file containing the set of regions to exclude for the SV calling. Optionally, you can compress the file in gzip or bgzip format.
--enable-variant-deduplication true	Relevant when both SV and SNV callers are enabled in somatic workflows. Can increase sensitivity and prevent the occurrence of replicated variants within genes such as FLT3 and KMT2A. Filter all small indels in the structural variant VCF that appear and are passing in the small variant VCF. DRAGEN will create a new VCF 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. DRAGEN normalizes variants by trimming and left shifting by up to 500 bases.
--sv-systematic-noise $BEDPE	Systematic noise BEDPE file containing the set of noisy paired regions (optionally gzip or bzip compressed). Optional for Tumor-Normal, but strongly recommended for Tumor-Only.
--sv-somatic-ins-tandup-hotspot-regions-bed $BED	Specify a custom BED of ITD hotspot regions to increase sensitivity for calling ITDs in somatic variant analysis. The default file includes FLT3, ARHGEF7, KMT2A, and UBTF exonic regions with some padding on both sides (300 bps)
--sv-min-candidate-variant-size	Run SV caller and report all SVs/indels at or above this size. The default value is set to 10.
--sv-min-scored-variant-size	After candidate identification, only score and report SVs/indels at or above this size. The default value is set to 50. This parameter doesn't affect the somatic hotspot region.

Option	Recommended Value for Liquid Tumors (e.g. AML/MLL)
--sv-enable-liquid-tumor-mode true	DRAGEN can account for Tumor-in-Normal (TiN) contamination by running liquid tumor mode.
--sv-tin-contam-tolerance $TIN_CONTAM_TOLERANCE	Set the Tumor-in-Normal (TiN) contamination tolerance level.

For more information, see Structural Variant Calling.

Resource Files

DRAGEN requires resource files for components such as SNV, SV, and CNV. The following notes provide references for downloading these files or generating them for custom workflows or assays.

SNV Systematic Noise

Systematic noise files are considered essential in Tumor-Only workflows. It is also recommended for Tumor-Normals workflows.

Prebuild

Prebuilt systematic noise BED files (WES and WGS) can be downloaded here: Product Files.

Prebuilt WES/WGS noise files	Description
WGS_hg38_v2.0.0_systematic_noise.snv.bed.gz	For WGS FF
FFPE_WGS_hg38_v2.0.0_systematic_noise.snv.bed.gz	For WGS FFPE (only hg38)
WES_hg38_v2.0.0_systematic_noise.snv.bed.gz	For WES FF and FFPE

Custom

Prebuilt systematic noise files are available for WES or WGS applications. For these applications, it is considered optional to build custom noise files. For high-sensitivity applications, including panels, it is required to build custom noise files. For best accuracy, the normal samples should ideally closely match the sequencer, sample type, library prep, and coverage of the tumor samples of interest. It is typically recommended to use 30–70 normals when building a noise file, but fewer can be used.

Step 1. Run DRAGEN somatic tumor-only on each of approximately 30-70 normal samples.

  
/opt/dragen/$VERSION/bin/dragen         #DRAGEN install path 
--ref-dir $REF_DIR                      #path to DRAGEN linear hashtable 
--output-directory $OUTPUT 
--intermediate-results-dir $PATH        #e.g. SDD /staging 
--output-file-prefix $PREFIX 
--tumor-fastq-list $PATH                #see 'Input Options' for FQ, BAM or CRAM 
--tumor-fastq-list-sample-id $STRING 
--umi-enable true 
--umi-source STRING                     #default='qname' 
--umi-library-type STRING               #see 'UMI' 
--vc-detect-systematic-noise=true 
--vc-target-bed-padding 500 
--vc-enable-germline-tagging=true 
--enable-variant-annotation=true 
--variant-annotation-assembly=GRCh37/8 
--variant-annotation-data $PATH 
--intermediate-results-dir $PATH 
--output-directory $PATH 
--output-file-prefix $STRING 

For WES and WGS pipelines gather the full paths to the small variant hard filtered VCFs (not GVCFs) from step 1 and create a lines file ${VCF_LIST} by specifying 1 file per line.

Step 2. Generate the final noise file.

  
/opt/dragen/$VERSION/bin/dragen         #DRAGEN install path 
--ref-dir $REF_DIR                      #path to DRAGEN linear hashtable 
--output-directory $OUTPUT 
--intermediate-results-dir $PATH        #e.g. SDD /staging 
--output-file-prefix $PREFIX 
--build-sys-noise-vcfs-list ${VCF_LIST} 

The SNV systematic noise files 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.

SV Systematic Noise

Systematic noise files are also recommended for Tumor-Normals workflows, but are considered essential for reducing FP calls in Tumor-Only workflows.

Prebuilt

Prebuilt SV systematic noise files can be downloaded here: Product Files.

Prebuilt WES/WGS noise files	Description
WGS_hg38_v3.0.0_systematic_noise.sv.bedpe.gz	For WGS/WES FF/FFPE
IDPF_WGS_hg38_v3.0.0_systematic_noise.sv.bedpe.gz	For HEME

Custom

It is considered optional to build a custom systematic noise file for WES or WGS applications, but for high sensitivity applications like panels it is strongly recommended. For best accuracy the normal samples should ideally closely match the sequencer, sample type, library prep and coverage of the tumor samples of interest. It is typically recommended to use 30 - 100 normals when building a noise file, but fewer can be used.

Step 1. Run DRAGEN somatic tumor-only on normal samples with --sv-detect-systematic-noise set to true to generate VCF output per normal sample.

  
/opt/dragen/$VERSION/bin/dragen         #DRAGEN install path 
--ref-dir $REF_DIR                      #path to DRAGEN linear hashtable 
--output-directory $OUTPUT 
--intermediate-results-dir $PATH        #e.g. SDD /staging 
--output-file-prefix $PREFIX 
--tumor-fastq-list $PATH                #see 'Input Options' for FQ, BAM or CRAM 
--tumor-fastq-list-sample-id $STRING 
--umi-enable true 
--umi-source STRING                     #default='qname' 
--umi-library-type STRING               #see 'UMI' 
--tumor-normal-has-umi STRING           #Sample(s) containing UMI ['tumor', 'both']. 
--sv-detect-systematic-noise true 

Step 2. Build the BEDPE file using input VCFs from previous step.

  
/opt/dragen/$VERSION/bin/dragen         #DRAGEN install path 
--ref-dir $REF_DIR                      #path to DRAGEN linear hashtable 
--output-directory $OUTPUT 
--intermediate-results-dir $PATH        #e.g. SDD /staging 
--output-file-prefix $PREFIX 
--sv-build-systematic-noise-vcfs-list $VCF_LIST#one VCF per line. 

Systematic noise BEDPE files 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.

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.

If a matched normal is available it is recommended to include it in the PON.

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 linear hashtable 
--output-directory $OUTPUT 
--intermediate-results-dir $PATH        #e.g. SDD /staging 
--output-file-prefix $PREFIX 
--tumor-fastq-list $PATH                #see 'Input Options' for FQ, BAM or CRAM 
--tumor-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 linear 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.

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.