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Chromatin Interaction Neural Network (ChINN): A machine learning-based method for predicting chromatin interactions from DNA sequences

Fan Cao, Yu Zhang, View ORCID ProfileYichao Cai, Sambhavi Animesh, Ying Zhang, Semih Akincilar, Yan Ping Loh, Wee Joo Chng, Vinay Tergaonkar, Chee Keong Kwoh, Melissa J. Fullwood
doi: https://doi.org/10.1101/2020.12.30.424817
Fan Cao
1Cancer Science Institute of Singapore, National University of Singapore
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Yu Zhang
2School of Computer Science and Engineering, Nanyang Technological University, Singapore
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Yichao Cai
1Cancer Science Institute of Singapore, National University of Singapore
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  • ORCID record for Yichao Cai
Sambhavi Animesh
1Cancer Science Institute of Singapore, National University of Singapore
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Ying Zhang
1Cancer Science Institute of Singapore, National University of Singapore
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Semih Akincilar
3Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Singapore
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Yan Ping Loh
1Cancer Science Institute of Singapore, National University of Singapore
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Wee Joo Chng
1Cancer Science Institute of Singapore, National University of Singapore
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Vinay Tergaonkar
3Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Singapore
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Chee Keong Kwoh
2School of Computer Science and Engineering, Nanyang Technological University, Singapore
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Melissa J. Fullwood
1Cancer Science Institute of Singapore, National University of Singapore
3Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A*STAR), Singapore
4School of Biological Sciences, Nanyang Technological University, Singapore
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  • For correspondence: mjfullwood@gmail.com
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Abstract

Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. Various computational methods have been developed to predict chromatin interactions. Most of these methods rely on large collections of ChIP-Seq/RNA-Seq/DNase-Seq datasets and predict only enhancer-promoter interactions. Some of the ‘state-of-the-art’ methods have poor experimental designs, leading to over-exaggerated performances and misleading conclusions. Here we developed a computational method, Chromatin Interaction Neural Network (ChINN), to predict chromatin interactions between open chromatin regions by using only DNA sequences of the interacting open chromatin regions. ChINN is able to predict CTCF-, RNA polymerase II- and HiC-associated chromatin interactions between open chromatin regions. ChINN also shows good across-sample performances and captures various sequence features that are predictive of chromatin interactions. To apply our results to clinical patient data, we applied CHINN to predict chromatin interactions in 6 chronic lymphocytic leukemia (CLL) patient samples and a cohort of open chromatin data from 84 CLL samples that was previously published. Our results demonstrated extensive heterogeneity in chromatin interactions in patient samples, and one of the sources of this heterogeneity were the different subtypes of CLL.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted January 03, 2021.
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Chromatin Interaction Neural Network (ChINN): A machine learning-based method for predicting chromatin interactions from DNA sequences
Fan Cao, Yu Zhang, Yichao Cai, Sambhavi Animesh, Ying Zhang, Semih Akincilar, Yan Ping Loh, Wee Joo Chng, Vinay Tergaonkar, Chee Keong Kwoh, Melissa J. Fullwood
bioRxiv 2020.12.30.424817; doi: https://doi.org/10.1101/2020.12.30.424817
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Chromatin Interaction Neural Network (ChINN): A machine learning-based method for predicting chromatin interactions from DNA sequences
Fan Cao, Yu Zhang, Yichao Cai, Sambhavi Animesh, Ying Zhang, Semih Akincilar, Yan Ping Loh, Wee Joo Chng, Vinay Tergaonkar, Chee Keong Kwoh, Melissa J. Fullwood
bioRxiv 2020.12.30.424817; doi: https://doi.org/10.1101/2020.12.30.424817

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