title: HPeak: an HMM-based algorithm for defining read-enriched regions in ChIP-Seq data.
abstract: BACKGROUND
  Protein-DNA interaction constitutes a basic mechanism for the genetic regulation of target gene expression. Deciphering this mechanism has been a daunting task due to the difficulty in characterizing protein-bound DNA on a large scale. A powerful technique has recently emerged that couples chromatin immunoprecipitation (ChIP) with next-generation sequencing, (ChIP-Seq). This technique provides a direct survey of the cistrom of transcription factors and other chromatin-associated proteins. In order to realize the full potential of this technique, increasingly sophisticated statistical algorithms have been developed to analyze the massive amount of data generated by this method.
  
  
  RESULTS
  Here we introduce HPeak, a Hidden Markov model (HMM)-based Peak-finding algorithm for analyzing ChIP-Seq data to identify protein-interacting genomic regions. In contrast to the majority of available ChIP-Seq analysis software packages, HPeak is a model-based approach allowing for rigorous statistical inference. This approach enables HPeak to accurately infer genomic regions enriched with sequence reads by assuming realistic probability distributions, in conjunction with a novel weighting scheme on the sequencing read coverage.
  
  
  CONCLUSIONS
  Using biologically relevant data collections, we found that HPeak showed a higher prevalence of the expected transcription factor binding motifs in ChIP-enriched sequences relative to the control sequences when compared to other currently available ChIP-Seq analysis approaches. Additionally, in comparison to the ChIP-chip assay, ChIP-Seq provides higher resolution along with improved sensitivity and specificity of binding site detection. Additional file and the HPeak program are freely available at http://www.sph.umich.edu/csg/qin/HPeak.
authors.affiliation: Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, 48109-2029, USA. qin@umich.edu
authors.forename: Zhaohui S
authors.initials: ZS
authors.lastname: Qin
authors.forename: Jianjun
authors.initials: J
authors.lastname: Yu
authors.forename: Jincheng
authors.initials: J
authors.lastname: Shen
authors.forename: Christopher A
authors.initials: CA
authors.lastname: Maher
authors.forename: Ming
authors.initials: M
authors.lastname: Hu
authors.forename: Shanker
authors.initials: S
authors.lastname: Kalyana-Sundaram
authors.forename: Jindan
authors.initials: J
authors.lastname: Yu
authors.forename: Arul M
authors.initials: AM
authors.lastname: Chinnaiyan
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D047369:Chromatin Immunoprecipitation; D019295:Computational Biology / Q000379:methods*; D015894:Genome, Human; D006657:Histones / Q000378:metabolism; D006801:Humans; D011485:Protein Binding; D012097:Repressor Proteins / Q000378:metabolism; D050794:STAT1 Transcription Factor / Q000378:metabolism; D017422:Sequence Analysis, DNA; D014157:Transcription Factors / Q000378:metabolism; D000099229:RE1-Silencing Transcription Factor
chemical_list: D006657:Histones; D012097:Repressor Proteins; D050794:STAT1 Transcription Factor; D014157:Transcription Factors; D000099229:RE1-Silencing Transcription Factor