Bayesian localization of CNV candidates in WGS data within minutes

Wiedenhoeft, John; Cagan, Alexander; Kozhemyakina, Rimma; Gulevich, Rimma; Schliep, Alexander

doi:10.1186/s13015-019-0154-7

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Bayesian localization of CNV candidates in WGS data within minutes

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Cagan, Alexander
Department of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Max Planck Society;
The Leipzig School of Human Origins (IMPRS), Max Planck Institute for Evolutionary Anthropology, Max Planck Society;

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Citation

Wiedenhoeft, J., Cagan, A., Kozhemyakina, R., Gulevich, R., & Schliep, A. (2019). Bayesian localization of CNV candidates in WGS data within minutes. Algorithms for Molecular Biology, 14: 20. doi:10.1186/s13015-019-0154-7.

Cite as: https://hdl.handle.net/21.11116/0000-0004-D3A7-1

Abstract

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.