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DNA Somatic Tumor-Only Solid WES

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. This recipe includes the recommended commands for solid samples. These settings support fresh frozen samples, as well as some optional settings for FFPE 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. SSD /staging 
--output-file-prefix $PREFIX 
# Inputs 
--tumor-fastq-list $PATH                #see 'Input Options' for FQ, BAM or CRAM 
--tumor-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 
--vc-systematic-noise $PATH             #Required 
--vc-excluded-regions-bed $BED          #FFPE: optionally mask ALUs 
--vc-target-vaf $NUM                    #Default = 0.03 (>= 3% VAF) 
# SV 
--enable-sv true 
--sv-exome true 
--sv-target-bed $SV_TARGET_BED 
# CNV 
--enable-cnv true 
--cnv-population-b-allele-vcf $POP_VCF 
--cnv-target-bed $PATH 
--cnv-combined-counts $PATH             #CNV PON 
# HRD Scoring 
--enable-hrd true                       #requires CNV 
# Annotation 
--variant-annotation-data $NIRVANA_PATH 
--vc-enable-germline-tagging true 
# TMB 
--enable-tmb true 
--tmb-enable-proxi-filter true          #Optional for Tumor-Only 
# HLA genotyper 
--enable-hla true 
--hla-exome true                        #Set HLA output to 3 field resolution (coding sequence only) 
# Microsatellite Instability (MSI) 
--enable-msi true 
--msi-microsatellites-file $PATH 
--msi-ref-normal-input $PATH            #required

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. For BCL input, first create FASTQs using BCL conversion.

FQ list Input

FQ Input

BAM Input

CRAM Input

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.

Duplicate Marking

Option
Description

--enable-duplicate-marking true

By default, DRAGEN marks duplicate reads and exclude them from variant calling.

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.

SNV

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.

--vc-systematic-noise $PATH

Systematic noise file. This filter is recommended for removing systematic noise observed in normal samples (i.e. systematic alignment errors, sequencing errors, etc.).

--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-sq-filter-threshold $NUM

Threshold for sensitivity-specificity tradeoff using SQ score. The pipeline specific 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-systematic-noise-filter-threshold $INT

Threshold for sensitivity-specificity tradeoff using AQ score for non-hotspot variants. This is only used when supplying a systematic noise file. Pipeline specific default value = 60. Raise this value to improve specificity at the cost of sensitivity, or lower it to improve sensitivity at the cost of specificity.

--vc-systematic-noise-filter-threshold-in-hotspot $INT

Threshold for sensitivity-specificity tradeoff using AQ score for hotspot variants. This is only used when supplying a systematic noise file. Pipeline specific default value = 20. 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

Clinical applications require maximum confidence in variant calls to prevent false positive diagnoses. High-specificity mode reduces false positives with some loss in sensitivity.

High Specificity Option
Description

--vc-high-specificity true

Apply aggressive filters in the small variant caller to boost specificity, with some loss in sensitivity.

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).

--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. 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-population-b-allele-vcf $POP_VCF

Specify a population SNP VCF. This option can be used when a matched normal sample is not available and analysis must be performed in tumor-only mode.

--cnv-enable-cyto-output true

Enable Cytogenetics-compatible output (default false).

Annotation

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

TMB

The Tumor-Normal pipeline is more effective than the Tumor-Only pipeline at removing or tagging germline variants. The Tumor-Only may subsequently report somewhat elevated TMB values. The TMB proxi filter is an optional setting on top of the regular database germline filter. It will aggressively filter additional germline variants based on allele frequencies.

Option
Description

--tmb-vaf-threshold FLOAT

Variant minimum 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 and PON files can be downloaded here: Product Files.

For more information on MSI calling, see MSI

SV

Option
Description

--sv-target-bed

Specifies a BED file containing the set of regions to call. Optionally gzip or bgzip format.

--sv-exclusion-bed

(Optional) Specifies a BED file containing the set of regions to exclude for the SV calling. Optionally 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 WGS Tumor-Normal, but strongly recommended for WGS Tumor-Only. Has not been validated in WES, enrichment and amplicon panels.

--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-min-scored-variant-size $INT

100000

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.

DRAGEN has pre-built systematic noise files for WGS, WES and for Pillar Amplicons. For high sensitivity applications, including panels or clinical WES/WGS assays, it is recommended to create your own systematic noise file as described under Custom.

Prebuilt

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

This section describes how to generate systematic noise files from phenotypically normal (non-tumor) samples to optimize the performance of a specific assay. 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.

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.

This step generates a bed file containing mean and max noise estimates per position. This can be used directly during variant calling (argument --vc-systematic-noise). The distribution of noise per position can also be plotted to identify particularly noisy positions that could be troubleshooted (e.g. modify assay settings or DRAGEN settings) or blocklisted

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

SV systematic noise files have not been tested with WES, enrichment and amplicon panels. It is considered an experimental mode for these assays.

Custom

Custom systematic noise files can be generated for WES, Panels or Amplicon. 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.

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

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)

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

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

Step 1. Generate CNV target counts of individual normal samples.

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.

Step 2. CNV combined counts file generation.

$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.

MSI baseline file (PON)

It is recommended to use a baseline file that matches the sample type (FF/FFPE), assay type (WGS/WES/Panel) and genome build (hg19/hg38) of the samples being analyzed. If matched normals are available, it is recommended to include them in the baselines.

For MSI baseline generation, see Baseline microsatellite repeat distribution. It is required that non-tumor inputs are used for baseline generation.

Step 1. Generate MSI baselines for individual normal samples.

Step 2. Combine MSI baselines to a single file.

The combined MSI baseline files can then be used for each case sample run of DRAGEN MSI using --msi-ref-normal-input combined_msi_baseline.dist.

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