%0 Journal Article %A Wiedenhoeft, John %A Cagan, Alexander %A Kozhemyakina, Rimma %A Gulevich, Rimma %A Schliep, 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 %T Bayesian localization of CNV candidates in WGS data within minutes : %G eng %U https://hdl.handle.net/21.11116/0000-0004-D3A7-1 %R 10.1186/s13015-019-0154-7 %7 2019 %D 2019 %* Review method: peer-reviewed %X 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. %K HMM; Wavelet; CNV; Bayesian inference %J Algorithms for Molecular Biology %V 14 %] 20 %I Springer Nature %C Berlin %@ 1748-7188