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https://hdl.handle.net/10356/152926| 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 Jane |
Keywords: | Science::Biological sciences Engineering::Computer science and engineering |
Issue Date: | 2021 | Source: | Cao, F., Zhang, Y., Cai, Y., Animesh, S., Zhang, Y., Akincilar, S. C., Loh, Y. P., Li, X., Chng, W. J., Tergaonkar, V., Kwoh, C. K. & Fullwood, M. J. (2021). Chromatin interaction neural network (ChINN) : a machine learning-based method for predicting chromatin interactions from DNA sequences. Genome Biology, 22, 226-. https://dx.doi.org/10.1186/s13059-021-02453-5 | Project: | NRF-NRFF2012-054 MOE2014-T3-1-006 NRF-CRP17-2017-02 T2EP30120-0020 |
Journal: | Genome Biology | 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. | URI: | https://hdl.handle.net/10356/152926 | ISSN: | 1474-760X | DOI: | 10.1186/s13059-021-02453-5 | Rights: | © The Author(s) 2021 Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
| Appears in Collections: | SBS Journal Articles SCSE Journal Articles |
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| Chromatin interaction neural network (ChINN)_ a machine learning-based method for predicting chromatin interactions from DNA sequences.pdf | 3.11 MB | Adobe PDF | View/Open |
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