DNA Germline WES UMI
The DRAGEN recipe includes the recommended pipeline specific commands.
Notes and additional options
Hashtable
For DRAGEN germline runs, it is recommended to use the pangenome hashtable.
Input options
DRAGEN input sources include: fastq list, fastq, bam, or cram.
FQ list Input
FQ Input
BAM Input
CRAM Input
Mapping and Aligning
--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
--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.
--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.
Annotation
VNTR
--sv-vntr-merge false
Option to disable automatic merging of VNTR calls into SV VCF.
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 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.
Step 2. Combined counts generation.
Individual PON counts can be merged into a single file as a <prefix>.combined.counts.txt.gz
file.
$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.
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.
Targeted Caller
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