ChIAMM: A Mixture Model for Statistical Analysis of Long-Range Chromatin Interactions From ChIA-PET Experiments

Arega, Yibeltal and Jiang, Hao and Wang, Shuangqi and Zhang, Jingwen and Niu, Xiaohui and Li, Guoliang (2020) ChIAMM: A Mixture Model for Statistical Analysis of Long-Range Chromatin Interactions From ChIA-PET Experiments. Frontiers in Genetics, 11. ISSN 1664-8021

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Abstract

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.

Item Type: Article
Subjects: Library Keep > Medical Science
Depositing User: Unnamed user with email support@librarykeep.com
Date Deposited: 31 Jan 2023 11:53
Last Modified: 10 Feb 2024 04:10
URI: http://archive.jibiology.com/id/eprint/93

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