DNA Somatic Tumor-Normal Solid Panel
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
--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
--enable-duplicate-marking true #default=true
# Small variant caller
--enable-variant-caller true
--vc-target-bed $VC_TARGET_BED
--vc-systematic-noise $PATH #Recommended
--vc-excluded-regions-bed $BED #FFPE: optionally mask ALUs
--vc-target-vaf 0.03 #Default = 0.03 (>= 3% 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
# Annotation
--variant-annotation-data PATH
--enable-variant-annotation true
# TMB
--enable-tmb true
# HLA genotyper
--enable-hla true
--hla-as-filter-min-threshold 29.0 #panel specific setting
--hla-as-filter-ratio-threshold 0.85 #panel specific setting
# 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. For BCL input, first create FASTQs using BCL conversion.
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
--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.
Duplicate Marking
--enable-duplicate-marking true
By default, DRAGEN marks duplicate reads and exclude them from variant calling.
--enable-positional-collapsing true
Alternative to --enable-duplicate-marking=true. Instead of discarding duplicate reads, DRAGEN can optionally perform positional collapsing, merging them into higher-quality consensus reads. This is beneficial for small panels without UMIs and coverage between 300X and 1000X. However, it's slower than standard duplicate marking and less effective on samples with coverage lower than 300X. For very high coverage (1000X+), avoid it due to potential read collisions. For high-sensitivity panels with 1000X+ coverage, consider using UMIs.
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.
--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
--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-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.). When working with panels it is recommended that a custom systematic noise file be created for each assay.
--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 $NUM
Threshold for sensitivity-specificity tradeoff using SQ score. The pipeline specific default threshold is 17.5. 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-specificty tradeoff using AQ score for non-hotspot variants. This is only used when supplying a systematic noise file. Default value = 10. 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-specificty tradeoff using AQ score for hotspot variants. This is only used when supplying a systematic noise file. Default value = 10. 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.
--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
--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
--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
--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.
For panels it is recommended to post-process the file by intersecting the WES or WGS sites with the manifest. This will avoid using any off-target reads in the MSI analysis. For small panels it may be required to generate custom site files to ensure the panel covers at least 2000 sites. To generate custom MSI site files refer to the MSI Biomarker section in the user guide.
--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
--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.
--sv-enable-liquid-tumor-mode true
DRAGEN can account for Tumor-in-Normal (TiN) contamination by running liquid tumor mode.
--sv-tin-contam-tolerance $TIN_CONTAM_TOLERANCE
Set the Tumor-in-Normal (TiN) contamination tolerance level.
For more information, see Structural Variant Calling.
Resource Files
DRAGEN requires resource files for components such as SNV, SV, and CNV. The following notes provide references for downloading these files or generating them for custom workflows or assays.
SNV Systematic Noise
Systematic noise files are considered essential in Tumor-Only workflows. It is also recommended for Tumor-Normals workflows.
Prebuild
Prebuilt systematic noise BED files (WES and WGS) can be downloaded here: Product Files.
WGS_hg38_v2.0.0_systematic_noise.snv.bed.gz
For WGS FF
FFPE_WGS_hg38_v2.0.0_systematic_noise.snv.bed.gz
For WGS FFPE (only hg38)
WES_hg38_v2.0.0_systematic_noise.snv.bed.gz
For WES FF and FFPE
Custom
This section describes how to generate systematic noise files from phenotypically normal (non-tumor) samples to optimize the performance of a specific assay. For best accuracy, the normal samples should ideally closely match the sequencer, sample type, library prep, and coverage of the tumor samples of interest. It is typically recommended to use 30-70 normals when building a noise file, but fewer can be used.
Step 1. Run DRAGEN somatic tumor-only on each of approximately 30-70 normal samples.
/opt/dragen/$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
--tumor-fastq-list $PATH #see 'Input Options' for FQ, BAM or CRAM
--tumor-fastq-list-sample-id $STRING
--vc-detect-systematic-noise=true
--vc-target-bed $VC_TARGET_BED #Region assessed in assay
--vc-target-bed-padding 500
--vc-emit-ref-confidence BP_RESOLUTION
--vc-enable-vcf-output true
--vc-enable-germline-tagging=true
--variant-annotation-data $PATH
--intermediate-results-dir $PATH
--output-directory $PATH
--output-file-prefix $STRING For panels we create GVCF files. Gather the full paths to the small variant caller hard filtered GVCFs (not VCFs) from step 1 and create an input file ${GVCF_LIST} by specifying 1 file per line.
Step 2. Generate the final noise file.
This step generates a bed file containing mean and max noise estimates per position. This can be used directly during variant calling (argument --vc-systematic-noise). The distribution of noise per position can also be plotted to identify particularly noisy positions that could be troubleshooted (e.g. modify assay settings or DRAGEN settings) or blocklisted
/opt/dragen/$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
--build-sys-noise-vcfs-list ${GVCF_LIST} The SNV systematic noise files can also be built in the cloud using the DRAGEN Baseline Builder App on BaseSpace or the DRAGEN Systematic Noise File Builder Pipeline on ICA.
SV Systematic Noise
Systematic noise files are also recommended for Tumor-Normals workflows, but are considered essential for reducing FP calls in Tumor-Only workflows.
Prebuilt
Prebuilt SV systematic noise files can be downloaded here: Product Files.
WGS_hg38_v3.0.0_systematic_noise.sv.bedpe.gz
For WGS/WES FF/FFPE
IDPF_WGS_hg38_v3.0.0_systematic_noise.sv.bedpe.gz
For HEME
Custom
It is considered optional to build a custom systematic noise file for WES or WGS applications, but for high sensitivity applications like panels it is strongly recommended. For best accuracy the normal samples should ideally closely match the sequencer, sample type, library prep and coverage of the tumor samples of interest. It is typically recommended to use 30 - 100 normals when building a noise file, but fewer can be used.
Step 1. Run DRAGEN somatic tumor-only on normal samples with --sv-detect-systematic-noise set to true to generate VCF output per normal sample.
/opt/dragen/$VERSION/bin/dragen #DRAGEN install path
--ref-dir $REF_DIR #path to DRAGEN linear hashtable
--output-directory $OUTPUT
--intermediate-results-dir $PATH #e.g. SSD /staging
--output-file-prefix $PREFIX
--tumor-fastq-list $PATH #see 'Input Options' for FQ, BAM or CRAM
--tumor-fastq-list-sample-id $STRING
--sv-detect-systematic-noise true Step 2. Build the BEDPE file using input VCFs from previous step.
/opt/dragen/$VERSION/bin/dragen #DRAGEN install path
--ref-dir $REF_DIR #path to DRAGEN linear hashtable
--output-directory $OUTPUT
--intermediate-results-dir $PATH #e.g. SSD /staging
--output-file-prefix $PREFIX
--sv-build-systematic-noise-vcfs-list $VCF_LIST#one VCF per line. Systematic noise BEDPE files can also be built in the cloud using the DRAGEN Baseline Builder App on BaseSpace or the DRAGEN Systematic Noise File Builder Pipeline on ICA.
CNV Panel of Normals (PON)
For CNV PON requirements and generation options see CNV Preprocessing | Panel of Normals.
If a matched normal is available it is recommended to include it in the PON.
Step 1. Generate CNV target counts of individual normal samples.
Any samples that should not be included in the final PON file can be excluded from this step. Any options used for CNV target counts generation (BED file, GC Bias Correction, etc.) should be matched when processing the case samples.
/opt/dragen/$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
--tumor-fastq-list $PATH #see 'Input Options' for FQ, BAM or CRAM
--tumor-fastq-list-sample-id $STRING
# CNV
--enable-cnv true
--cnv-target-bed $PATH Step 2. CNV combined counts file generation.
/opt/dragen/$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
--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.
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