title: An optimal method to segment piecewise poisson distributed signals with application to sequencing data.
abstract: To analyze the next generation sequencing data, the so-called read depth signal is often segmented with standard segmentation tools. However, these tools usually assume the signal to be a piecewise constant signal and contaminated with zero mean Gaussian noise, and therefore modeling error occurs. This paper models the read depth signal with piecewise Poisson distribution, which is more appropriate to the next generation sequencing mechanism. Based on the proposed model, an opti- mal dynamic programming algorithm with parallel computing is proposed to segment the piecewise signal, and furthermore detect the copy number variation.
authors.forename: Junbo
authors.initials: J
authors.lastname: Duan
authors.forename: Charles
authors.initials: C
authors.lastname: Soussen
authors.forename: David
authors.initials: D
authors.lastname: Brie
authors.forename: Jerome
authors.initials: J
authors.lastname: Idier
authors.forename: Yu-Ping
authors.initials: YP
authors.lastname: Wang
authors.forename: Mingxi
authors.initials: M
authors.lastname: Wan
mesh_terms: D000465:Algorithms; D000818:Animals; D004247:DNA / Q000032:analysis / Q000235:genetics; D056915:DNA Copy Number Variations; D016678:Genome; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D016011:Normal Distribution; D016012:Poisson Distribution; D017422:Sequence Analysis, DNA
chemical_list: D004247:DNA