Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles

Author:

Tao Huan1,Li Hao1,Xu Kang1,Hong Hao1,Jiang Shuai1,Du Guifang1,Wang Junting1,Sun Yu1,Huang Xin1,Ding Yang1,Li Fei2,Zheng Xiaofei1,Chen Hebing1,Bo Xiaochen1ORCID

Affiliation:

1. Beijing Institute of Radiation Medicine, Beijing 100850, China

2. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China

Abstract

Abstract The exploration of three-dimensional chromatin interaction and organization provides insight into mechanisms underlying gene regulation, cell differentiation and disease development. Advances in chromosome conformation capture technologies, such as high-throughput chromosome conformation capture (Hi-C) and chromatin interaction analysis by paired-end tag (ChIA-PET), have enabled the exploration of chromatin interaction and organization. However, high-resolution Hi-C and ChIA-PET data are only available for a limited number of cell lines, and their acquisition is costly, time consuming, laborious and affected by theoretical limitations. Increasing evidence shows that DNA sequence and epigenomic features are informative predictors of regulatory interaction and chromatin architecture. Based on these features, numerous computational methods have been developed for the prediction of chromatin interaction and organization, whereas they are not extensively applied in biomedical study. A systematical study to summarize and evaluate such methods is still needed to facilitate their application. Here, we summarize 48 computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles, categorize them and compare their performance. Besides, we provide a comprehensive guideline for the selection of suitable methods to predict chromatin interaction and organization based on available data and biological question of interest.

Funder

National Natural Science Foundation of China

Beijing Nova Program of Science and Technology

Hebing Chen

Beijing Natural Science Foundation

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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