A computational approach to distinguish somatic vs. germline origin of genomic alterations from deep sequencing of cancer specimens without a matched normal
Fig 2
Copy number detection overview.
Aligned DNA sequences of the tumor specimen are normalized against a process‐matched normal, producing log‐ratio and minor allele frequency (MAF) data. Next, whole‐genome segmentation is performed using a circular binary segmentation (CBS) algorithm on the log‐ratio data. Then, a Gibbs sampler fitted copy number model and a grid‐based model are fit to the segmented log‐ratio and MAF data, producing genome‐wide copy number estimates. Finally, the degree of fit of candidate models returned by Gibbs sampling and grid sampling are compared and the optimal model is selected by an automated heuristic.