Improving detection of copy-number variation by simultaneous bias correction and read-depth segmentation

Structural variation is an important class of genetic variation in mammals. High-throughput sequencing (HTS) technologies promise to revolutionize copy-number variation (CNV) detection but present substantial analytic challenges. Converging evidence suggests that multiple types of CNV-informative da...

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Main Authors:	Szatkiewicz, Jin P., Wang, WeiBo, Sullivan, Patrick F., Wang, Wei, Sun, Wei
Format:	Online
Language:	English
Published:	Oxford University Press 2013
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3561969/

id	pubmed-3561969
recordtype	oai_dc
spelling	pubmed-35619692013-02-01 Improving detection of copy-number variation by simultaneous bias correction and read-depth segmentation Szatkiewicz, Jin P. Wang, WeiBo Sullivan, Patrick F. Wang, Wei Sun, Wei Computational Biology Structural variation is an important class of genetic variation in mammals. High-throughput sequencing (HTS) technologies promise to revolutionize copy-number variation (CNV) detection but present substantial analytic challenges. Converging evidence suggests that multiple types of CNV-informative data (e.g. read-depth, read-pair, split-read) need be considered, and that sophisticated methods are needed for more accurate CNV detection. We observed that various sources of experimental biases in HTS confound read-depth estimation, and note that bias correction has not been adequately addressed by existing methods. We present a novel read-depth–based method, GENSENG, which uses a hidden Markov model and negative binomial regression framework to identify regions of discrete copy-number changes while simultaneously accounting for the effects of multiple confounders. Based on extensive calibration using multiple HTS data sets, we conclude that our method outperforms existing read-depth–based CNV detection algorithms. The concept of simultaneous bias correction and CNV detection can serve as a basis for combining read-depth with other types of information such as read-pair or split-read in a single analysis. A user-friendly and computationally efficient implementation of our method is freely available. Oxford University Press 2013-02 2012-12-26 /pmc/articles/PMC3561969/ /pubmed/23275535 http://dx.doi.org/10.1093/nar/gks1363 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Szatkiewicz, Jin P. Wang, WeiBo Sullivan, Patrick F. Wang, Wei Sun, Wei
spellingShingle	Szatkiewicz, Jin P. Wang, WeiBo Sullivan, Patrick F. Wang, Wei Sun, Wei Improving detection of copy-number variation by simultaneous bias correction and read-depth segmentation
author_facet	Szatkiewicz, Jin P. Wang, WeiBo Sullivan, Patrick F. Wang, Wei Sun, Wei
author_sort	Szatkiewicz, Jin P.
title	Improving detection of copy-number variation by simultaneous bias correction and read-depth segmentation
title_short	Improving detection of copy-number variation by simultaneous bias correction and read-depth segmentation
title_full	Improving detection of copy-number variation by simultaneous bias correction and read-depth segmentation
title_fullStr	Improving detection of copy-number variation by simultaneous bias correction and read-depth segmentation
title_full_unstemmed	Improving detection of copy-number variation by simultaneous bias correction and read-depth segmentation
title_sort	improving detection of copy-number variation by simultaneous bias correction and read-depth segmentation
description	Structural variation is an important class of genetic variation in mammals. High-throughput sequencing (HTS) technologies promise to revolutionize copy-number variation (CNV) detection but present substantial analytic challenges. Converging evidence suggests that multiple types of CNV-informative data (e.g. read-depth, read-pair, split-read) need be considered, and that sophisticated methods are needed for more accurate CNV detection. We observed that various sources of experimental biases in HTS confound read-depth estimation, and note that bias correction has not been adequately addressed by existing methods. We present a novel read-depth–based method, GENSENG, which uses a hidden Markov model and negative binomial regression framework to identify regions of discrete copy-number changes while simultaneously accounting for the effects of multiple confounders. Based on extensive calibration using multiple HTS data sets, we conclude that our method outperforms existing read-depth–based CNV detection algorithms. The concept of simultaneous bias correction and CNV detection can serve as a basis for combining read-depth with other types of information such as read-pair or split-read in a single analysis. A user-friendly and computationally efficient implementation of our method is freely available.
publisher	Oxford University Press
publishDate	2013
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3561969/
_version_	1611951941840011264

Improving detection of copy-number variation by simultaneous bias correction and read-depth segmentation

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