Introduction

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.

Publications

  1. Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression.
    Cite this
    Wiedenhoeft J, Brugel E, Schliep A, 2016-05-01 - PLoS computational biology

Credits

  1. John Wiedenhoeft
    Developer

    Department of Computer Science, Rutgers University, United States of America

  2. Eric Brugel
    Developer

    Department of Computer Science, Rutgers University, United States of America

  3. Alexander Schliep
    Investigator

    Department of Computer Science, Rutgers University, United States of America

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Summary
AccessionBT006892
Tool TypeApplication
Category
PlatformsLinux/Unix
Technologies
User InterfaceTerminal Command Line
Download Count0
Country/RegionUnited States of America
Submitted ByAlexander Schliep