Panel of Normals

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. This allows the algorithm to subtract system level biases that are not sample specific.

In this 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.

Perform target counts for all your samples, whether the samples are to be used as references or are the case samples under analysis. The case samples and all samples to be used as a panel of normals sample must have identical intervals and therefore should be generated with identical settings. The target counts stage also performs GC Bias correction. GC Bias correction is enabled by default.

The following examples are for WGS processing. For exome processing, see Whole Exome.

The following is an example command for processing a BAM file.

dragen \
-r <HASHTABL> \
--bam-input <BAM> \
--output-directory <OUTPUT> \
--output-file-prefix <SAMPLE. \
--enable-map-align false \
--enable-cnv true

The following is an example command for processing a CRAM file.

dragen \
-r <HASHTABLE> \
--cram-input <CRAM> \
--output-directory <OUTPUT> \
--output-file-prefix <SAMPLE> \
--enable-map-align false \
--enable-cnv true

The next step in the CNV pipeline when using a panel of normals is to perform the normalization and to make the calls. This step involves selecting a panel of normals, which is a list of target counts files to be used for reference-based median normalization.

You can run the analysis in other workflow combinations. The CNV events are called for the reference samples used. Ideally the panel of normals samples follow library prep and sequencing workflows that are identical to the workflows of the case sample under analysis. For calling on sex chromosomes, the panel of normals should include a balance of both male and female samples. DRAGEN automatically handles calling on sex chromosomes based on the predicted sex of each sample in the panel.

For optimal bias correction, a minimum of 50 samples is recommended as a panel. DRAGEN can run with a single-sample panel, but single-sample panels can result in artifactual calls in the test sample where the panel sample has copy number changes.

To generate a Panel of Normals (PON), create a plain text file in which each line in the file contains a path pointing to a .target.counts.gz file generated from the target counts stage. Relative paths are supported if the paths are relative to the current working directory. Absolute paths are recommended in case the workflow is used later or shared with other users.

The following is an example PON file, which uses a subset of the GC corrected files from the target counts stage.

/data/output_trio1/sample1.target.counts.gc-corrected.gz
/data/output_trio1/sample2.target.counts.gc-corrected.gz
/data/output_trio2/sample4.target.counts.gc-corrected.gz
/data/output_trio2/sample5.target.counts.gc-corrected.gz
/data/output_trio3/sample7.target.counts.gc-corrected.gz
/data/output_trio3/sample8.target.counts.gc-corrected.gz 
....

Alternatively, you can specify the files to be used in the panel of normals with the --cnv-normals-file option. This option uses a single file name and can be specified multiple times.

After you have created a PON file, you can run the caller by specifying your case sample with the --cnv-input option and the PON file with the --cnv-normals-list option. If you used GC bias corrected counts in the previous stage, then you do not need to run GC bias correction again. To disable GC bias correction, set --cnv-enable-gcbias-correction to false.

For example, the following command normalizes the case sample against the panel of normals.

dragen \
-r <HASHTABLE> \
--output-directory <OUTPUT> \
--output-file-prefix <SAMPLE> \
--enable-map-align false \
--enable-cnv true \
--cnv-input <CASE_COUNTS> \
--cnv-normals-list <NORMALS> \
--cnv-enable-gcbias-correction false