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Bayesian localization of CNV candidates in WGS data within minutes

Journal article
Authors John Wiedenhoeft
A. Cagan
R. Kozhemyakina
R. Gulevich
Alexander Schliep
Published in Algorithms for Molecular Biology
Volume 14
Issue 1
Publication year 2019
Published at Department of Computer Science and Engineering, Computing Science (GU)
Language en
Keywords HMM, Wavelet, CNV, Bayesian inference, hidden markov-models, analysis toolkit, domestication, adaptation, webgestalt, evolution, variants, behavior, fox, Biochemistry & Molecular Biology, Biotechnology & Applied Microbiology, Mathematical & Computational Biology
Subject categories Cell and molecular biology


Background Full Bayesian inference for detecting copy number variants (CNV) from whole-genome sequencing (WGS) data is still largely infeasible due to computational demands. A recently introduced approach to perform Forward-Backward Gibbs sampling using dynamic Haar wavelet compression has alleviated issues of convergence and, to some extent, speed. Yet, the problem remains challenging in practice. Results In this paper, we propose an improved algorithmic framework for this approach. We provide new space-efficient data structures to query sufficient statistics in logarithmic time, based on a linear-time, in-place transform of the data, which also improves on the compression ratio. We also propose a new approach to efficiently store and update marginal state counts obtained from the Gibbs sampler. Conclusions Using this approach, we discover several CNV candidates in two rat populations divergently selected for tame and aggressive behavior, consistent with earlier results concerning the domestication syndrome as well as experimental observations. Computationally, we observe a 29.5-fold decrease in memory, an average 5.8-fold speedup, as well as a 191-fold decrease in minor page faults. We also observe that metrics varied greatly in the old implementation, but not the new one. We conjecture that this is due to the better compression scheme. The fully Bayesian segmentation of the entire WGS data set required 3.5 min and 1.24 GB of memory, and can hence be performed on a commodity laptop.

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