EPIP: a novel approach for condition-specific enhancer-promoter interaction prediction.

The identification of enhancer-promoter interactions (EPIs), especially condition-specific ones, is important for the study of gene transcriptional regulation. Existing experimental approaches for EPI identification are still expensive, and available computational methods either do not consider or have low performance in predicting condition-specific EPIs. We developed a novel computational method called EPIP to reliably predict EPIs, especially condition-specific ones. EPIP is capable of predicting interactions in samples with limited data as well as in samples with abundant data. Tested on more than eight cell lines, EPIP reliably identifies EPIs, with an average area under the receiver operating characteristic curve of 0.95 and an average area under the precision-recall curve of 0.73. Tested on condition-specific EPIPs, EPIP correctly identified 99.26% of them. Compared with two recently developed methods, EPIP outperforms them with a better accuracy. The EPIP tool is freely available at http://www.cs.ucf.edu/˜xiaoman/EPIP/. Supplementary data are available at Bioinformatics online. © The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Citation

Amlan Talukder, Samaneh Saadat, Xiaoman Li, Haiyan Hu. EPIP: a novel approach for condition-specific enhancer-promoter interaction prediction. Bioinformatics (Oxford, England). 2019 Oct 15;35(20):3877-3883

Mesh Tags

PMID: 31410461

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