title: Improving detection of copy-number variation by simultaneous bias correction and read-depth segmentation.
abstract: 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.
authors.affiliation: Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599-7264, USA. jin_szatkiewicz@med.unc.edu
authors.forename: Jin P
authors.initials: JP
authors.lastname: Szatkiewicz
authors.forename: WeiBo
authors.initials: W
authors.lastname: Wang
authors.forename: Patrick F
authors.initials: PF
authors.lastname: Sullivan
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.forename: Wei
authors.initials: W
authors.lastname: Sun
mesh_terms: D000818:Animals; D056915:DNA Copy Number Variations; D023281:Genomics / Q000379:methods; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D016014:Linear Models; D008390:Markov Chains; D051379:Mice