title: 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: 10.5281/zenodo.46262). This paper was selected for oral presentation at RECOMB 2016, and an abstract is published in the conference proceedings.
authors.affiliation: Department of Computer Science, Rutgers University, Piscataway, New Jersey, United States of America.
authors.forename: John
authors.initials: J
authors.lastname: Wiedenhoeft
authors.affiliation: Department of Computer Science, Rutgers University, Piscataway, New Jersey, United States of America.
authors.forename: Eric
authors.initials: E
authors.lastname: Brugel
authors.affiliation: Department of Computer Science, Rutgers University, Piscataway, New Jersey, United States of America.
authors.forename: Alexander
authors.initials: A
authors.lastname: Schliep
mesh_terms: D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000235:genetics; D002460:Cell Line; D055028:Comparative Genomic Hybridization / Q000706:statistics & numerical data*; D019295:Computational Biology; D003198:Computer Simulation; D056915:DNA Copy Number Variations; D044962:Data Compression; D005260:Female; D015894:Genome, Human; D006801:Humans; D008390:Markov Chains; D008957:Models, Genetic; D012984:Software