1. Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression.
- Author
-
Wiedenhoeft, John, Brugel, Eric, and Schliep, Alexander
- Subjects
- *
MARKOV processes , *WAVELETS (Mathematics) , *FORWARD-backward algorithm , *CHROMOSOME fragments , *BAYESIAN analysis - 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 (DOI: ). This paper was selected for oral presentation at RECOMB 2016, and an abstract is published in the conference proceedings. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF