Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences

Page view(s)
62
Checked on Mar 18, 2024
Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences
Title:
Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences
Journal Title:
Genome Biology
Keywords:
Publication Date:
16 August 2021
Citation:
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(1). https://doi.org/10.1186/s13059-021-02453-5
Abstract:
AbstractChromatin 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.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the NRF - NRF Fellowship
Grant Reference no. : NRF-NRFF2012-054

This research / project is supported by the Singapore Ministry of Education - Academic Research Fund Tier 3
Grant Reference no. : MOE2014-T3-1-006

This research / project is supported by the National Research Foundation - Competitive Research Programme
Grant Reference no. : NRF-CRP17-2017-02

This research / project is supported by the Ministry of Education - Tier II grant
Grant Reference no. : T2EP30120-0020

This research / project is supported by the Nanyang Technological University (NTU) - Start-Up Grant
Grant Reference no. : #001220-00001

This research / project is supported by the National Research Foundation / Singapore Ministry of Education - Research Centres of Excellence initiative
Grant Reference no. : N.A
Description:
ISSN:
1474-760X