% pubman genre = article
@article{item_3169012,
title = {{Bayesian localization of CNV candidates in WGS data within minutes}},
author = {Wiedenhoeft, John and Cagan, Alexander and Kozhemyakina, Rimma and Gulevich, Rimma and Schliep, Alexander},
language = {eng},
issn = {1748-7188},
doi = {10.1186/s13015-019-0154-7},
publisher = {Springer Nature},
address = {Berlin},
year = {2019},
date = {2019},
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{\textendash}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.}},
journal = {{Algorithms for Molecular Biology}},
volume = {14},
eid = {20},
}