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.
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References
Wu J, Xie J (2008) Computation-based discovery of cis-regulatory modules by hidden Markov model. J Computat Biol 15(3):279–290
Qin Z, Yu J, Shen J, Maher C, Hu M, Kalyana-Sundaram S, Yu J, Chinnaiyan A (2010) HPeak: an HMM-based algorithm for defining read-enriched regions in chip-seq data. BMC Bioinf 11(369)
Spyrou C, Stark R, Lynch A, Tavare S (2009) BayesPeak: Bayesian analysis of ChIP-seq data. BMC Bioinf 10(1):299
Mo Q (2012) A fully Bayesian hidden Ising model for ChIP-seq data analysis. Biostatistics 13(1):113–128
Bao Y, Vinciotti V, Wit E, ’t Hoen P (2014) Joint modelling of ChIP-seq data via a Markov random field model. Biostatistics 15(2):296–310
Zhang Y, Liu T, Meyer C, Eeckhoute J, Johnson D, Bernstein B, Nussbaum C, Myers R, Brown M, Li W (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol 201(1):R137
Robertson G, Hirst M, Bainbridge M, Bilenky M, Zhao Y, Zeng T, Euskirchen G, Bernier B, Varhol R, Delaney A, Thiessen N, Griffith O, He A, Marra M, Snyder M, Jones S (2007) Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat Methods 4(8):651–657
Bao Y, Vinciotti V, Wit E, ’t Hoen P (2013) Accounting for immunoprecipitation efficiencies in the statistical analysis of ChIP-seq data. BMC Bioinf 14(169)
 Ramos Y, Hestand M, Verlaan M, Krabbendam E, Ariyurek Y, van Dam H, van Ommen G, den Dunnen J, Zantema A, ’t Hoen P (2010) Genome-wide assessment of differential roles for p300 and CBP in transcription regulation. Nucleic Acids Res 38(16):5396–5408
Scott S (2002) Bayesian methods for hidden Markov models: recursive computing in the 21st century. J Am Stat Assoc 97(457):337–351
Broët P, Richardson S (2006) Detection of gene copy number changes in CGH microarrays using a spatially correlated mixture model. Bioinformatics 22(8):911–918
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Vinciotti, V. (2017). Modelling ChIP-seq Data Using HMMs. In: Westhead, D., Vijayabaskar, M. (eds) Hidden Markov Models. Methods in Molecular Biology, vol 1552. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6753-7_8
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DOI: https://doi.org/10.1007/978-1-4939-6753-7_8
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Online ISBN: 978-1-4939-6753-7
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