title: Quantifying copy number variations using a hidden Markov model with inhomogeneous emission distributions.
abstract: Copy number variations (CNVs) are a significant source of genetic variation and have been found frequently associated with diseases such as cancers and autism. High-throughput sequencing data are increasingly being used to detect and quantify CNVs; however, the distributional properties of the data are not fully understood. A hidden Markov model (HMM) is proposed using inhomogeneous emission distributions based on negative binomial regression to account for the sequencing biases. The model is tested on the whole genome sequencing data and simulated data sets. An algorithm for CNV detection is implemented in the R package CNVfinder. The model based on negative binomial regression is shown to provide a good fit to the data and provides competitive performance compared with methods based on normalization of read counts.
authors: Department of Statistics, Northwestern University, Evanston, IL 60208, USA. kennethmccallum2013@u.northwestern.edu
authors: Kenneth Jordan
authors: KJ
authors: McCallum
authors: Ji-Ping
authors: JP
authors: Wang
mesh_terms: D000465:Algorithms; D016010:Binomial Distribution; D056808:Biostatistics; D003198:Computer Simulation; D056915:DNA Copy Number Variations; D030561:Databases, Nucleic Acid / Q000706:statistics & numerical data; D059014:High-Throughput Nucleotide Sequencing / Q000706:statistics & numerical data; D006801:Humans; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D012984:Software
keywords: Copy number variation; Hidden Markov chain; High-throughput sequencing; Negative binomial regression; Sequence read depth