Chromatin Interaction Neural Network (ChINN): A Machine Learning-Based Method for Predicting Chromatin Interactions from DNA Sequences. (Genome Biol, Aug 2021)

Fan Cao^#¹, Yu Zhang^#², Yichao Cai^#¹, Sambhavi Animesh¹, Ying Zhang¹, Semih Can Akincilar³, Yan Ping Loh¹, Xinya Li⁴, Wee Joo Chng^1 5 6, Vinay Tergaonkar^3 7, Chee Keong Kwoh², Melissa J Fullwood^8 9 10

Affiliations

¹Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore, 117599, Singapore.
²School of Computer Science and Engineering, Nanyang Technological University, Block N4, 50 Nanyang Avenue, Singapore, 639798, Singapore.
³Institute of Molecular and Cell Biology, Agency for Science (IMCB), A*STAR (Agency for Science, Technology and Research,, Singapore, 138673, Singapore.
⁴School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore.
⁵Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, Singapore, 119228, Singapore.
⁶Department of Haematology-Oncology, National University Cancer Institute, National University Health System, NUH Zone B, Medical Centre, Singapore, 119074, Singapore.
⁷Department of Pathology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore, 117597, Singapore.
⁸Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Dr, Singapore, 117599, Singapore. mfullwood@ntu.edu.sg.
⁹Institute of Molecular and Cell Biology, Agency for Science (IMCB), A*STAR (Agency for Science, Technology and Research,, Singapore, 138673, Singapore. mfullwood@ntu.edu.sg.
¹⁰School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore. mfullwood@ntu.edu.sg.

^#Contributed equally.

Abstract

Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences. ChINN predicts CTCF- and RNA polymerase II-associated and Hi-C chromatin interactions. ChINN shows good across-sample performances and captures various sequence features for chromatin interaction prediction. We apply ChINN to 6 chronic lymphocytic leukemia (CLL) patient samples and a published cohort of 84 CLL open chromatin samples. Our results demonstrate extensive heterogeneity in chromatin interactions among CLL patient samples.

Keywords: 3D genome organization; Bioinformatics; ChIA-PET; Chromatin interactions; DNA sequence; Hi-C; Leukemia; Machine learning.

PMID: 34399797 PMCID: PMC8365954 DOI: 10.1186/s13059-021-02453-5

Chromatin Interaction Neural Network (ChINN): A Machine Learning-Based Method for Predicting Chromatin Interactions from DNA Sequences. (Genome Biol, Aug 2021)

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