Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression.

ABSTRACT: By integrating Haar wavelets with Hidden Markov Models, we achieve drastically reduced running times for Bayesian inference using Forward-Backward Gibbs sampling. We show that this improves detection of genomic copy number variants (CNV) in array CGH experiments compared to the state-of-the-art, including standard Gibbs sampling. The method concentrates computational effort on chromosomal segments which are difficult to call, by dynamically and adaptively recomputing consecutive blocks of observations likely to share a copy number. This makes routine diagnostic use and re-analysis of legacy data collections feasible; to this end, we also propose an effective automatic prior. An open source software implementation of our method is available at http://schlieplab.org/Software/HaMMLET/ (DOI: 1

SUBMITTER: Wiedenhoeft J 

PROVIDER: S-EPMC4866742 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature