title: Universal count correction for high-throughput sequencing.
abstract: We show that existing RNA-seq, DNase-seq, and ChIP-seq data exhibit overdispersed per-base read count distributions that are not matched to existing computational method assumptions. To compensate for this overdispersion we introduce a nonparametric and universal method for processing per-base sequencing read count data called FIXSEQ. We demonstrate that FIXSEQ substantially improves the performance of existing RNA-seq, DNase-seq, and ChIP-seq analysis tools when compared with existing alternatives.
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
authors.forename: Tatsunori B
authors.initials: TB
authors.lastname: Hashimoto
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
authors.forename: Matthew D
authors.initials: MD
authors.lastname: Edwards
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
authors.forename: David K
authors.initials: DK
authors.lastname: Gifford
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D002843:Chromatin / Q000737:chemistry; D047369:Chromatin Immunoprecipitation; D019295:Computational Biology; D003198:Computer Simulation; D004247:DNA / Q000737:chemistry; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D020014:K562 Cells; D016013:Likelihood Functions; D016012:Poisson Distribution; D012313:RNA / Q000737:chemistry; D017422:Sequence Analysis, DNA; D012984:Software; D014157:Transcription Factors / Q000737:chemistry
chemical_list: D002843:Chromatin; D014157:Transcription Factors; D012313:RNA; D004247:DNA