Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression
J. Wiedenhoeft, E. Brugel and A. Schliep
PLoS Computational Biology 2016, 12:5, e1004871.
By combining Haar wavelets with Bayesian Hidden Markov Models, we improve detection of genomic copy number variants (CNV) in array CGH experiments compared to the state-of-the-art, including standard Gibbs sampling. At the same time, we achieve drastically reduced running times, as 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/. The web supplement is at http://schlieplab.org/Supplements/HaMMLET/. Preprint available from bioRxiv at http://biorxiv.org/content/early/2015/07/31/023705.
Supplementary information is available at https://schlieplab.org/Supplements/HaMMLET/. Data used or generated by this study is available at https://zenodo.org/record/46263. A reprint is available as PDF.
Pubmed ID: 27177143. DOI: 10.1371/journal.pcbi.1004871.
The publication includes results from the following projects or software tools: BayesianHMM, HaMMLET.
Further publications by Alexander Schliep, John Wiedenhoeft, Eric J Brugel.