An empirical Bayesian framework for somatic mutation detection from cancer genome sequencing data.

ABSTRACT: Recent advances in high-throughput sequencing technologies have enabled a comprehensive dissection of the cancer genome clarifying a large number of somatic mutations in a wide variety of cancer types. A number of methods have been proposed for mutation calling based on a large amount of sequencing data, which is accomplished in most cases by statistically evaluating the difference in the observed allele frequencies of possible single nucleotide variants between tumours and paired normal samples. However, an accurate detection of mutations remains a challenge under low sequencing depths or tumour contents. To overcome this problem, we propose a novel method, Empirical Bayesian mutation Calling (https://github.com/friend1ws/EBCall), for detecting somatic mutations. Unlike previous methods,

SUBMITTER: Shiraishi Y 

PROVIDER: S-EPMC3627598 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature