Please use this identifier to cite or link to this item: 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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