A Review of Three Different Studies on Hidden Markov Models for Epigenetic Problems: A Computational Perspective.
- Author:
Kyung Eun LEE
1
;
Hyun Seok PARK
Author Information
- Publication Type:Review
- Keywords: chromatin states; epigenomics; hidden Markov models; noncoding DNA
- MeSH: Chromatin; Chromatin Immunoprecipitation; Classification; Dataset; Epigenomics*; Genome; Oligonucleotide Array Sequence Analysis
- From:Genomics & Informatics 2014;12(4):145-150
- CountryRepublic of Korea
- Language:English
- Abstract: Recent technical advances, such as chromatin immunoprecipitation combined with DNA microarrays (ChIp-chip) and chromatin immunoprecipitation-sequencing (ChIP-seq), have generated large quantities of high-throughput data. Considering that epigenomic datasets are arranged over chromosomes, their analysis must account for spatial or temporal characteristics. In that sense, simple clustering or classification methodologies are inadequate for the analysis of multi-track ChIP-chip or ChIP-seq data. Approaches that are based on hidden Markov models (HMMs) can integrate dependencies between directly adjacent measurements in the genome. Here, we review three HMM-based studies that have contributed to epigenetic research, from a computational perspective. We also give a brief tutorial on HMM modelling-targeted at bioinformaticians who are new to the field.
