Abstract
Hidden Markov model (HMM) is widely used for modeling spatially correlated genomic data (series data). In genomics, datasets of this kind are generated from genome-wide mapping studies through high-throughput methods such as chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq). When multiple regulatory protein binding sites or related epigenetic modifications are mapped simultaneously, the correlation between data series can be incorporated into the latent variable inference in a multivariate form of HMM, potentially increasing the statistical power of signal detection. In this chapter, we review the challenges of multivariate HMMs and propose a computationally tractable method called sparsely correlated HMMs (scHMM). We illustrate the method and the scHMM package using an example mouse ChIP-seq dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Furey TS (2012) ChIP-seq and beyond: new and improved methodologies to detect and characterize protein-DNA interactions. Nat Rev Genet 13(12):840–852. doi:10.1038/nrg3306
Park PJ (2009) ChIP-seq: advantages and challenges of a maturing technology. Nat Rev Genet 10(10):669–680. doi:10.1038/nrg2641
Rabiner L (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77:257–286
Humburg P, Bulger D, Stone G (2008) Parameter estimation for robust HMM analysis of ChIP-chip data. BMC Bioinformatics 9:343. doi:10.1186/1471-2105-9-343
Ji H, Wong WH (2005) TileMap: create chromosomal map of tiling array hybridizations. Bioinformatics 21(18):3629–3636. doi:10.1093/bioinformatics/bti593
Li W, Meyer CA, Liu XS (2005) A hidden Markov model for analyzing ChIP-chip experiments on genome tiling arrays and its application to p53 binding sequences. Bioinformatics 21(Suppl 1):i274–i282. doi:10.1093/bioinformatics/bti1046
Qin ZS, Yu J, Shen J, Maher CA, Hu M, Kalyana-Sundaram S, Yu J, Chinnaiyan AM (2010) HPeak: an HMM-based algorithm for defining read-enriched regions in ChIP-Seq data. BMC Bioinformatics 11:369. doi:10.1186/1471-2105-11-369
Rashid N, Sun W, Ibrahim JG (2014) Some statistical strategies for DAE-seq data analysis: variable selection and modeling dependencies among observations. J Am Stat Assoc 109:78–94
Spyrou C, Stark R, Lynch AG, Tavare S (2009) BayesPeak: Bayesian analysis of ChIP-seq data. BMC Bioinformatics 10:299. doi:10.1186/1471-2105-10-299
Yau C, Holmes CC (2013) A decision-theoretic approach for segmental classification. Ann Appl Stat 7:1814–1835
Mikkelsen TS, Ku M, Jaffe DB, Issac B, Lieberman E, Giannoukos G, Alvarez P, Brockman W, Kim TK, Koche RP, Lee W, Mendenhall E, O'Donovan A, Presser A, Russ C, Xie X, Meissner A, Wernig M, Jaenisch R, Nusbaum C, Lander ES, Bernstein BE (2007) Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 448(7153):553–560. doi:10.1038/nature06008
Wang Z, Zang C, Rosenfeld JA, Schones DE, Barski A, Cuddapah S, Cui K, Roh TY, Peng W, Zhang MQ, Zhao K (2008) Combinatorial patterns of histone acetylations and methylations in the human genome. Nat Genet 40(7):897–903. doi:10.1038/ng.154
Wang Z, Zang C, Cui K, Schones DE, Barski A, Peng W, Zhao K (2009) Genome-wide mapping of HATs and HDACs reveals distinct functions in active and inactive genes. Cell 138(5):1019–1031. doi:10.1016/j.cell.2009.06.049
Ghahramani Z, Jordan M (1997) Factorial hidden Markov models. Mach Learn 29:245–273
Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc B 58:267–288
Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33:1–22
Choi H, Fermin D, Nesvizhskii AI, Ghosh D, Qin ZS (2013) Sparsely correlated hidden Markov models with application to genome-wide location studies. Bioinformatics 29(5):533–541. doi:10.1093/bioinformatics/btt012
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media LLC
About this protocol
Cite this protocol
Choi, H., Ghosh, D., Qin, Z. (2017). Computationally Tractable Multivariate HMM in Genome-Wide Mapping Studies. 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_10
Download citation
DOI: https://doi.org/10.1007/978-1-4939-6753-7_10
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-6751-3
Online ISBN: 978-1-4939-6753-7
eBook Packages: Springer Protocols