Bioinformatics. 2022 Feb 17:btac096. doi: 10.1093/bioinformatics/btac096. Online ahead of print.
ABSTRACT
MOTIVATION: Cross-sectional analyses of primary cancer genomes have identified regions of recurrent somatic copy-number alteration, many of which result from positive selection during cancer formation and contain driver genes. However, no effective approach exists for identifying genomic loci under significantly different degrees of selection in cancers of different subtypes, anatomic sites, or disease stages.
RESULTS: CNGPLD is a new tool for performing case-control somatic copy-number analysis that facilitates the discovery of differentially amplified or deleted copy-number aberrations in a case group of cancer compared to a control group of cancer. This tool uses a Gaussian process statistical framework in order to account for the covariance structure of copy-number data along genomic coordinates and to control the false discovery rate at the region level.
AVAILABILITY: CNGPLD is freely available at https://bitbucket.org/djhshih/cngpld as an R package.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
PMID:35176131 | DOI:10.1093/bioinformatics/btac096
