DNA Germline Panel

The DRAGEN recipe includes the recommended pipeline specific commands.

  
/opt/dragen/$VERSION/bin/dragen         #DRAGEN install path 
--ref-dir $REF_DIR                      #path to DRAGEN pangenome 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 
--enable-duplicate-marking true         #default=true 
# Small variant caller 
--enable-variant-caller true 
--vc-target-bed $VC_TARGET_BED 
# Annotation 
--variant-annotation-data PATH 
--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               #if panel covers class II HLA regions 
--hla-as-filter-min-threshold 29.0      #panel specific setting 
--hla-as-filter-ratio-threshold 0.85    #panel specific setting 

Notes and additional options

Hashtable

For DRAGEN germline runs, it is recommended to use the pangenome 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.

Duplicate Marking

Option	Description
--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.

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 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.

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

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 pangenome 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 pangenome 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.