title: A short survey of computational analysis methods in analysing ChIP-seq data.
abstract: Chromatin immunoprecipitation followed by massively parallel next-generation sequencing (ChIP-seq) is a valuable experimental strategy for assaying protein-DNA interaction over the whole genome. Many computational tools have been designed to find the peaks of the signals corresponding to protein binding sites. In this paper, three computational methods, ChIP-seq processing pipeline (spp), PeakSeq and CisGenome, used in ChIP-seq data analysis are reviewed. There is also a comparison of how they agree and disagree on finding peaks using the publically available Signal Transducers and Activators of Transcription protein 1 (STAT1) and RNA polymerase II (PolII) datasets with corresponding negative controls.
authors.affiliation: Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, CO, USA. hyun.kim@ucdenver.edu
authors.forename: Hyunmin
authors.initials: H
authors.lastname: Kim
authors.forename: Jihye
authors.initials: J
authors.lastname: Kim
authors.forename: Heather
authors.initials: H
authors.lastname: Selby
authors.forename: Dexiang
authors.initials: D
authors.lastname: Gao
authors.forename: Tiejun
authors.initials: T
authors.lastname: Tong
authors.forename: Tzu Lip
authors.initials: TL
authors.lastname: Phang
authors.forename: Aik Choon
authors.initials: AC
authors.lastname: Tan
mesh_terms: D047369:Chromatin Immunoprecipitation / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D011485:Protein Binding; D012319:RNA Polymerase II / Q000235:genetics; D012107:Research Design; D050794:STAT1 Transcription Factor / Q000235:genetics; D017422:Sequence Analysis, DNA; D012984:Software
chemical_list: D050794:STAT1 Transcription Factor; D012319:RNA Polymerase II