ChIAMM: A Mixture Model for Statistical Analysis of Long-Range Chromatin Interactions From ChIA-PET Experiments
Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is an important experimental method for detecting specific protein-mediated chromatin loops genome-wide at high resolution. Here, we proposed a new statistical approach with a mixture model, chromatin interaction analysis using m...
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Frontiers Media S.A.
2020-12-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2020.616160/full |
| _version_ | 1818391415260971008 |
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| author | Yibeltal Arega Hao Jiang Shuangqi Wang Jingwen Zhang Xiaohui Niu Guoliang Li Guoliang Li |
| author_facet | Yibeltal Arega Hao Jiang Shuangqi Wang Jingwen Zhang Xiaohui Niu Guoliang Li Guoliang Li |
| author_sort | Yibeltal Arega |
| collection | DOAJ |
| description | Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is an important experimental method for detecting specific protein-mediated chromatin loops genome-wide at high resolution. Here, we proposed a new statistical approach with a mixture model, chromatin interaction analysis using mixture model (ChIAMM), to detect significant chromatin interactions from ChIA-PET data. The statistical model is cast into a Bayesian framework to consider more systematic biases: the genomic distance, local enrichment, mappability, and GC content. Using different ChIA-PET datasets, we evaluated the performance of ChIAMM and compared it with the existing methods, including ChIA-PET Tool, ChiaSig, Mango, ChIA-PET2, and ChIAPoP. The result showed that the new approach performed better than most top existing methods in detecting significant chromatin interactions in ChIA-PET experiments. |
| first_indexed | 2024-12-14T05:13:09Z |
| format | Article |
| id | doaj.art-4dcaadfff6c14d4392b15948a1e320ba |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-14T05:13:09Z |
| publishDate | 2020-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-4dcaadfff6c14d4392b15948a1e320ba2022-12-21T23:15:54ZengFrontiers Media S.A.Frontiers in Genetics1664-80212020-12-011110.3389/fgene.2020.616160616160ChIAMM: A Mixture Model for Statistical Analysis of Long-Range Chromatin Interactions From ChIA-PET ExperimentsYibeltal Arega0Hao Jiang1Shuangqi Wang2Jingwen Zhang3Xiaohui Niu4Guoliang Li5Guoliang Li6Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, ChinaAgricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, ChinaNational Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, ChinaNational Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, ChinaAgricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, ChinaAgricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, College of Informatics, Huazhong Agricultural University, Wuhan, ChinaNational Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, ChinaChromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is an important experimental method for detecting specific protein-mediated chromatin loops genome-wide at high resolution. Here, we proposed a new statistical approach with a mixture model, chromatin interaction analysis using mixture model (ChIAMM), to detect significant chromatin interactions from ChIA-PET data. The statistical model is cast into a Bayesian framework to consider more systematic biases: the genomic distance, local enrichment, mappability, and GC content. Using different ChIA-PET datasets, we evaluated the performance of ChIAMM and compared it with the existing methods, including ChIA-PET Tool, ChiaSig, Mango, ChIA-PET2, and ChIAPoP. The result showed that the new approach performed better than most top existing methods in detecting significant chromatin interactions in ChIA-PET experiments.https://www.frontiersin.org/articles/10.3389/fgene.2020.616160/fullChIA-PETchromatin interactionsgenome-widemixture modelbayesian framework |
| spellingShingle | Yibeltal Arega Hao Jiang Shuangqi Wang Jingwen Zhang Xiaohui Niu Guoliang Li Guoliang Li ChIAMM: A Mixture Model for Statistical Analysis of Long-Range Chromatin Interactions From ChIA-PET Experiments Frontiers in Genetics ChIA-PET chromatin interactions genome-wide mixture model bayesian framework |
| title | ChIAMM: A Mixture Model for Statistical Analysis of Long-Range Chromatin Interactions From ChIA-PET Experiments |
| title_full | ChIAMM: A Mixture Model for Statistical Analysis of Long-Range Chromatin Interactions From ChIA-PET Experiments |
| title_fullStr | ChIAMM: A Mixture Model for Statistical Analysis of Long-Range Chromatin Interactions From ChIA-PET Experiments |
| title_full_unstemmed | ChIAMM: A Mixture Model for Statistical Analysis of Long-Range Chromatin Interactions From ChIA-PET Experiments |
| title_short | ChIAMM: A Mixture Model for Statistical Analysis of Long-Range Chromatin Interactions From ChIA-PET Experiments |
| title_sort | chiamm a mixture model for statistical analysis of long range chromatin interactions from chia pet experiments |
| topic | ChIA-PET chromatin interactions genome-wide mixture model bayesian framework |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2020.616160/full |
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