Please use this identifier to cite or link to this item: https://doi.org/10.1186/s13059-021-02453-5
Title: Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences
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 J 
Keywords: Science & Technology
Life Sciences & Biomedicine
Biotechnology & Applied Microbiology
Genetics & Heredity
Machine learning
3D genome organization
Chromatin interactions
ChIA-PET
Hi-C
DNA sequence
Leukemia
Bioinformatics
READ ALIGNMENT
CTCF
GENOME
EXPRESSION
ORGANIZATION
PRINCIPLES
SURROGATE
TOPOLOGY
DOMAINS
MARKERS
Issue Date: 16-Aug-2021
Publisher: BMC
Citation: 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 J (2021-08-16). Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences. GENOME BIOLOGY 22 (1). ScholarBank@NUS Repository. https://doi.org/10.1186/s13059-021-02453-5
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.
Source Title: GENOME BIOLOGY
URI: https://scholarbank.nus.edu.sg/handle/10635/207756
ISSN: 1474760X
DOI: 10.1186/s13059-021-02453-5
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