title: Modelling ChIP-seq Data Using HMMs.
abstract: Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have now become routine in biology for the detection of protein binding sites. In this chapter, we show how hidden Markov models can be used for the analysis of data generated by ChIP-seq experiments. We show how a hidden Markov model can naturally account for spatial dependencies in the ChIP-seq data, how it can be used in the presence of data from multiple ChIP-seq experiments under the same biological condition, and how it naturally accounts for the different IP efficiencies of individual ChIP-seq experiments.
authors.affiliation: Department of Mathematics, Brunel University London, Uxbridge, UB8 3PH, UK. veronica.vinciotti@brunel.ac.uk.
authors.forename: Veronica
authors.initials: V
authors.lastname: Vinciotti
mesh_terms: D047369:Chromatin Immunoprecipitation / Q000379:methods*; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D011485:Protein Binding; D017422:Sequence Analysis, DNA; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D014157:Transcription Factors
keywords: ChIP-sequencing; Hidden Markov models; Histone Modifications