Germline CNV Calling (WGS/WES)
Last updated
Was this helpful?
Last updated
Was this helpful?
The DRAGEN CNV pipeline follows the workflow shown in the following figure.
DRAGEN CNV Pipeline Workflow
The DRAGEN CNV Pipeline uses many aspects of the DRAGEN secondary analysis available in other pipelines, such as hardware acceleration and efficient I/O processing. To enable CNV processing in the DRAGEN Host Software, set the --enable-cnv command line option to true.
The CNV pipeline has the following processing modules:
Calling / Genotyping --- Thresholding, scoring, qualifying, and filtering of putative events as copy number variants.
The following is an example command line for WGS input (using self-normalization):
The following is an example command line for WES input:
The following are the top-level options that are shared with the DRAGEN Host Software to control the CNV pipeline. You can input a BAM or CRAM file into the CNV pipeline. If you are using the DRAGEN mapper and aligner, you can use FASTQ files.
--bam-input --- The BAM file to be processed.
--cram-input --- The CRAM file to be processed.
--enable-cnv --- Enable or disable CNV processing. Set to true to enable
CNV processing.
--enable-map-align --- Enables the mapper and aligner module. The
default is true, so all input reads are remapped and aligned unless this
option is set to false.
--fastq-file1, --fastq-file2 --- FASTQ file or files to be processed.
--output-directory --- Output directory where all results are stored.
--output-file-prefix --- Output file prefix that will be prepended to
all result file names.
--ref-dir --- The DRAGEN reference genome hashtable directory.
The output and filtering options control the CNV output files.
--cnv-exclude-bed --- Specifies a BED file that indicates the intervals to exclude from the CNV analysis. If a target interval overlaps regions specified from exclude BED file more than cnv-exclude-bed-min-overlap, the target interval is suppressed.
--cnv-exclude-bed-min-overlap --- Specifies a fraction for filtering threshold of overlap amount between a target interval and the excluded region (0.5).
--cnv-enable-ref-calls --- Emit copy neutral (REF) calls in the output VCF file. The default is true for single WGS CNV analysis.
--cnv-enable-tracks --- Generate track files that can be imported into IGV for viewing. When this option is enabled, a *.gff file for the output variant calls is generated, as well as *.bw files for the tangent normalized signal. The default is true.
--cnv-filter-bin-support-ratio --- Filters out a candidate event if the span of supporting bins is less than the specified ratio with respect to the overall event length. This filter only applies to records with length greater than cnv-filter-bin-support-ratio-min-len. The default ratio is 0.2 (20% support). As an example, if an event is called and has a length of 100,000 bp, but the target interval bins that support the call only spans a total of 15,000 bp (15,000/100,000 = 0.15), then the interval is filtered out. If applied, the record will have cnvBinSupportRatio as a filter.
--cnv-filter-bin-support-ratio-min-len --- Minimum length of candidate event at which to apply cnv-filter-bin-support-ratio. Currently only applied to germline WGS workflows, with default value of 80,000 bp.
--cnv-filter-copy-ratio --- Specifies the minimum copy ratio (CR) threshold value centered about 1.0 at which a reported event is marked as PASS in the output VCF file. The default value is 0.2, which leads to calls with CR between 0.8 and 1.2 being filtered. If applied, the record will have cnvCopyRatio as a filter.
--cnv-filter-length --- Specifies the minimum event length in bases at which a reported event is marked as PASS in the output VCF file. The default is 10000. If applied, the record will have cnvLength as a filter.
--cnv-filter-qual --- Specifies the QUAL value at which a reported event is marked as PASS in the output VCF file. You should adjust the parameter value according to your own application data. If applied, the record will have cnvQual as a filter.
--cnv-min-qual --- Specifies the minimum reported QUAL. The default is 3.
--cnv-max-qual --- Specifies the maximum reported QUAL. The default is 200.
--cnv-qual-length-scale --- Specifies the bias weighting factor to adjust QUAL estimates for segments with longer lengths. This is an advanced option and should not need to be modified. The default is 0.9303 (2-0.1).
--cnv-qual-noise-scale --- Specifies the bias weighting factor to adjust QUAL estimates based on sample variance. This is an advanced option and should not need to be modified. The default is 1.0.
Quality scores are computed using a probabilistic model that uses a mixture of heavy tailed probability distributions (one per integer copy number) with a weighting for event length. Noise variance is estimated. The output VCF contains a Phred-scaled metric that measures confidence in called amplification (CN > 2 for diploid locus), deletion (CN < 2 for diploid locus), or copy neutral (CN=2 for diploid locus) events.
The scoring algorithm also calculates exact copy-number quality scores that are inputs to the DeNovo CNV detection pipeline.
When running the target counts stage or the normalization stage, the DRAGEN CNV pipeline also provides the following information about the samples in the run.
A correlation metric of the read count profile between the case sample and any panel of normals samples. A correlation metric greater than 0.90 is recommended for confident analysis, but there is no hard restriction enforced by the software.
The predicted sex of each sample in the run. The sex is predicted based on the read count information in the sex chromosomes and the autosomal chromosomes. The median value for the counts is printed to the screen for the autosomal chromosomes, the X chromosome, and the Y chromosome. This estimation requires a minimum of 300 target intervals on the sex chromosomes to proceed.
The results are printed to the screen when running the pipeline. For example:
The predicted sexes for samples in use are also printed to the *.cnv_metrics.csv
output file. For a panel of normals, the predicted sexes are used to determine which panel samples are leveraged for normalization on sex chromosomes. If the estimated sex of the sample is UNDETERMINED, the sex of the sample is set to FEMALE.
You can override the predicted sex of the sample with the --sample-sex option.
--- Binning of the read counts and other signals from alignments.
--- Correction of intrinsic system biases.
--- Detection of normal ploidy levels and normalization of the case sample. Optionally, a panel of normals (PoN) is used when a cohort or population samples are readily available.
--- Breakpoint detection via segmentation of the normalized signal.
