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

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

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...

Full description

Saved in:
Bibliographic Details
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
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary: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.

Similar Items