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

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 chromati...

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Bibliographic Details
Main Authors: Fan Cao, Yu Zhang, Yichao Cai, Sambhavi Animesh, Ying Zhang, Semih Can Akincilar, Yan Ping Loh, Xinya Li, Wee Joo Chng, Vinay Tergaonkar, Chee Keong Kwoh, Melissa J. Fullwood
Format: Article
Language:English
Published: BMC 2021-08-01
Series:Genome Biology
Subjects:
Machine learning
3D genome organization
Chromatin interactions
ChIA-PET
Hi-C
DNA sequence
Online Access:https://doi.org/10.1186/s13059-021-02453-5
Description
Summary: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.
ISSN:1474-760X

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