RT Journal Article SR Electronic T1 Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression JF bioRxiv FD Cold Spring Harbor Laboratory SP 023705 DO 10.1101/023705 A1 John Wiedenhoeft A1 Eric Brugel A1 Alexander Schliep YR 2015 UL http://biorxiv.org/content/early/2015/07/31/023705.abstract AB 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://bioinformatics.rutgers.edu/Software/HaMMLET/. The web supplement is at http://bioinformatics.rutgers.edu/Supplements/HaMMLET/