DNA Germline Amplicon

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.

  
/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. SSD /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 false        #default=false 
# Amplicon 
--enable-dna-amplicon true 
--amplicon-target-bed $PATH 
# Small variant caller 
--enable-variant-caller true 
--vc-target-bed $VC_TARGET_BED          #Optional. Auto-generated based on amplicon target bed. 
# Annotation 
--variant-annotation-data $NIRVANA_PATH 
--enable-variant-annotation true 
# SV 
--enable-sv true 
# CNV 
--enable-cnv true 
--cnv-combined-counts $PATH             #CNV PON. Required for amplicon CNV calling on CASE samples. 
--cnv-target-bed $PATH                  #Optional. Auto-generated based on amplicon target bed. 
--cnv-filter-qual $NUM                  #CNV filter quality. Adjust CNV filter quality thresholds according to the user’s validation study. 

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

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).

Duplicate Marking

Option	Description
--enable-duplicate-marking false	The Amplicon Pipeline disables duplicate marking. In amplicon assays, fragments originate from a limited number of unique start and end positions, making conventional duplicate detection inappropriate.

SNV

DRAGEN amplicon does not employ machine learning based variant recalibration (DRAGEN-ML).

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)

Annotation

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

CNV

Amplicon CNV requires PON input. In PON mode, the DRAGEN CNV Pipeline is broken down into two distinct stages. The target counts stage is performed on each sample (case and normals), to bin the alignments. The normalization and call detection stage is then performed with the case sample against the panel of normals to determine the events.

Option	Description
--cnv-segmentation-mode $SEG_MODE	Option to override the default segmentation algorithm. By default, bed is used for standard panels and hslm for Pillar panels with a pre-built PON.
--amplicon-cnv-use-default-pon false	We recommend including in-run normal samples—matched in sample type and library preparation—in the same sequencing run to serve as the PON. If generating a custom PON is not feasible, for Pillar panels, the pre-packaged panel-specific PON can be used as a fallback. To enable this, set the option to true.
--cnv-segmentation-bed $PATH	You can bypass segmentation by specifying a cnv-segmentation-bed and using cnv-segmentation-mode=bed. If bed segmentation mode is used, the segmentation bed is auto-generated from amplicon target bed by default
--cnv-filter-qual $NUM	QUAL value at which to hard filter CNV VCF. You can adjust CNV filter quality thresholds according to the your validation study

In-run PON

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

Step 1. Generate CNV target counts of individual samples from the sequencing run.

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 pangenome hashtable 
--output-directory $OUTPUT 
--intermediate-results-dir $PATH        #e.g. SSD /staging 
--output-file-prefix $PREFIX 
--fastq-list $PATH                      #see 'Input Options' for FQ, BAM or CRAM 
--fastq-list-sample-id $STRING 
# CNV 
--enable-cnv true 
--enable-dna-amplicon true 
--amplicon-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 pangenome 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.