A Review of Three Different Studies on Hidden Markov Models for Epigenetic Problems: A Computational Perspective
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 analy...
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BioMed Central
2014-12-01
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| Series: | Genomics & Informatics |
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| Online Access: | http://genominfo.org/upload/pdf/gni-12-145.pdf |
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| author | Kyung-Eun Lee Hyun-Seok Park |
| author_facet | Kyung-Eun Lee Hyun-Seok Park |
| author_sort | Kyung-Eun Lee |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6d2ee8cee9494f4c9888cd1239491e53 |
| institution | Kabale University |
| issn | 1598-866X 2234-0742 |
| language | English |
| publishDate | 2014-12-01 |
| publisher | BioMed Central |
| record_format | Article |
| series | Genomics & Informatics |
| spelling | doaj-art-6d2ee8cee9494f4c9888cd1239491e532025-02-03T02:03:21ZengBioMed CentralGenomics & Informatics1598-866X2234-07422014-12-0112414515010.5808/GI.2014.12.4.14598A Review of Three Different Studies on Hidden Markov Models for Epigenetic Problems: A Computational PerspectiveKyung-Eun Lee0Hyun-Seok Park1Ewha Information and Telecommunication Institute, Ewha Womans University, Seoul 120-750, Korea.Ewha Information and Telecommunication Institute, Ewha Womans University, Seoul 120-750, Korea.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.http://genominfo.org/upload/pdf/gni-12-145.pdfchromatin statesepigenomicshidden Markov modelsnoncoding DNA |
| spellingShingle | Kyung-Eun Lee Hyun-Seok Park A Review of Three Different Studies on Hidden Markov Models for Epigenetic Problems: A Computational Perspective Genomics & Informatics chromatin states epigenomics hidden Markov models noncoding DNA |
| title | A Review of Three Different Studies on Hidden Markov Models for Epigenetic Problems: A Computational Perspective |
| title_full | A Review of Three Different Studies on Hidden Markov Models for Epigenetic Problems: A Computational Perspective |
| title_fullStr | A Review of Three Different Studies on Hidden Markov Models for Epigenetic Problems: A Computational Perspective |
| title_full_unstemmed | A Review of Three Different Studies on Hidden Markov Models for Epigenetic Problems: A Computational Perspective |
| title_short | A Review of Three Different Studies on Hidden Markov Models for Epigenetic Problems: A Computational Perspective |
| title_sort | review of three different studies on hidden markov models for epigenetic problems a computational perspective |
| topic | chromatin states epigenomics hidden Markov models noncoding DNA |
| url | http://genominfo.org/upload/pdf/gni-12-145.pdf |
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