EPIP: a Novel Approach for Condition-specific Enhancer-promoter Interaction Prediction

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2019 Aug 15

PMID 31410461

Citations 17

Authors

Amlan Talukder

Samaneh Saadat

Xiaoman Li

Haiyan Hu

Affiliations

Soon will be listed here.

Abstract

Motivation: 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.

Results: 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.

Availability And Implementation: The EPIP tool is freely available at http://www.cs.ucf.edu/˜xiaoman/EPIP/.

Supplementary Information: Supplementary data are available at Bioinformatics online.

Citing Articles

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