Somatic WES CNV Calling
For somatic whole-exome sequencing (WES) and somatic targeted panels, you can use a panel of normals as the reference baseline to provide insight into copy number variants. The reported events are based solely on the normalized copy ratio values and the deviation from the expected reference baseline levels. This workflow can be useful for applications that require only the detection of gains and losses in targeted genes. The somatic WES CNV model is similar to the germline WES CNV model, but utilizes a different quality scoring and calling model.
Use one of the following input options.
| • | --tumor-fastq1 and --tumor-fastq2—Specify a FASTQ file |
| • | --tumor-bam-input—Specify an existing BAM file |
| • | --tumor-cram-input—Specify an existing CRAM file |
The Somatic WES CNV Caller requires a panel of normals. The panel of normals samples help measure instrinsic biases of the upstream processes to allow for proper normalization. To generate a panel of normals, see Panel of Normals. The panel of normals sample should be well matched to the case sample under analysis.
If a matched normal sample is available, the sample can be included in the panel of normals. The workflow does not change if a matched normal is or is not available.
Example Command Lines
The following example command line runs somatic analysis on WES data.
dragen \
-r <HASHTABLE> \
--output-directory <OUTPUT> \
--output-file-prefix <SAMPLE> \
--enable-map-align false \
--enable-cnv true \
--tumor-bam-input <TUMOR_BAM> \
--cnv-normals-list <NORMALS> \
--cnv-target-bed <BED> \
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. If using this option, all events in the segmentation BED are reported in the output VCF. For more information on the segmentation BED file, see Targeted Segmentation (Segment BED).
The following example command line runs somatic analysis on a targeted panel.
dragen \
-r <HASHTABLE> \
--output-directory <OUTPUT> \
--output-file-prefix <SAMPLE> \
--enable-map-align false \
--enable-cnv true \
--tumor-bam-input <TUMOR_BAM> \
--cnv-normals-list <NORMALS> \
--cnv-target-bed <BED> \
--cnv-segmentation-bed <SEGMENT_BED> \
--cnv-segmentation-mode bed \
