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

John Wiedenhoeft, Eric Brugel, Alexander Schliep
doi: https://doi.org/10.1101/023705
John Wiedenhoeft
Department of Computer Science Rutgers University
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Eric Brugel
Department of Computer Science Rutgers University
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Alexander Schliep
Department of Computer Science Rutgers University
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Abstract

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/

Footnotes

  • john.wiedenhoeft{at}cs.rutgers.edu, schliep{at}cs.rutgers.edu

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted July 31, 2015.
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Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression
John Wiedenhoeft, Eric Brugel, Alexander Schliep
bioRxiv 023705; doi: https://doi.org/10.1101/023705
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Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression
John Wiedenhoeft, Eric Brugel, Alexander Schliep
bioRxiv 023705; doi: https://doi.org/10.1101/023705

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