Chromatin interaction neural network (ChINN) : a machine learning-based method for predicting chromatin interactions from DNA sequences

• Main Authors: Cao, Fan, Zhang, Yu, Cai, Yichao, Animesh, Sambhavi, Zhang, Ying, Akincilar, Semih Can, Loh, Yan Ping, Li, Xinya, Chng, Wee Joo, Tergaonkar, Vinay, Kwoh, Chee Keong, Fullwood, Melissa Jane
• Other Authors: School of Biological Sciences
• Format: Article
• Language: English
• Published: 2021
• Subjects: Science::Biological sciences; Engineering::Computer science and engineering; Machine Learning; 3D Genome Organization
• Online Access: https://hdl.handle.net/10356/152926
• Institution: Nanyang Technological University

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.