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

J. Wiedenhoeft, E. Brugel and A. Schliep

In Research in Computational Molecular Biology: 20th Annual Conference, RECOMB 2016, Santa Monica, CA, USA, April 17-21, 2016, Proceedings, Springer, 9649, 263, 2016.

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 The web supplement is at Preprint available from bioRxiv at

Supplementary information is available at A reprint is available as PDF.

DOI: 10.1007/978-3-319-31957-5.

The publication includes results from the following projects or software tools: BayesianHMM, HaMMLET.

Further publications by Alexander Schliep, John Wiedenhoeft, Eric J Brugel.