| Chapter title |
Computationally Tractable Multivariate HMM in Genome-Wide Mapping Studies
|
|---|---|
| Chapter number | 10 |
| Book title |
Hidden Markov Models
|
| Published in |
Methods in molecular biology, February 2017
|
| DOI | 10.1007/978-1-4939-6753-7_10 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6751-3, 978-1-4939-6753-7
|
| Authors |
Hyungwon Choi, Debashis Ghosh, Zhaohui Qin |
| Editors |
David R. Westhead, M. S. Vijayabaskar |
| 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. |
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