Calculating the spatial density of regulatory chromatin interactions using multi-modal datasets from the same cell line

Haiyan Gong; Minghong Li; Mengdie Ji; Xiaotong Zhang; Zan Yuan; Sichen Zhang; Yi Yang; Chun Li; Yang Chen

doi:10.1016/j.xpro.2023.102188

Calculating the spatial density of regulatory chromatin interactions using multi-modal datasets from the same cell line

STAR Protoc. 2023 Mar 30;4(2):102188. doi: 10.1016/j.xpro.2023.102188. Online ahead of print.

Authors

Haiyan Gong¹, Minghong Li¹, Mengdie Ji², Xiaotong Zhang³, Zan Yuan², Sichen Zhang¹, Yi Yang¹, Chun Li⁴, Yang Chen⁵

Affiliations

¹ Beijing Advanced Innovation Center for Materials Genome Engineering, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
² State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China.
³ Beijing Advanced Innovation Center for Materials Genome Engineering, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China; Shunde Innovation School, University of Science and Technology Beijing, Foshan 528399, China. Electronic address: zxt@ies.ustb.edu.cn.
⁴ School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China.
⁵ State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China. Electronic address: yc@ibms.pumc.edu.cn.

Abstract

Here, we present a protocol for calculating the spatial density of regulatory chromatin interactions (SD-RCI) using Hi-C, ATAC-seq, and ChIP-seq datasets from the same cell line. We describe steps for selecting and preprocessing datasets, training and predicting a model to obtain regulatory chromatin interactions, and evaluating model performance. We then detail calculation of SD-RCI and visualization of the correlation between SD-RCI and gene expression. This protocol is applicable to Hi-C, ATAC-seq, and ChIP-seq data from the human cell line. For complete details on the use and execution of this protocol, please refer to Gong et al. (2023).¹.

Keywords: Bioinformatics; Biotechnology and Bioengineering; ChIPseq; Gene Expression; Sequence Analysis; Sequencing.