Petmodule: A Motif Module Based Approach For Enhancer Target Gene Prediction

Abstract

The identification of enhancer-target gene (ETG) pairs is vital for the understanding of gene transcriptional regulation. Experimental approaches such as Hi-C have generated valuable resources of ETG pairs. Several computational methods have also been developed to successfully predict ETG interactions. Despite these progresses, high-throughput experimental approaches are still costly and existing computational approaches are still suboptimal and not easy to apply. Here we developed a motif module based approach called PETModule that predicts ETG pairs. Tested on eight human cell types and two mouse cell types, we showed that a large number of our predictions were supported by Hi-C and/or ChIA-PET experiments. Compared with two recently developed approaches for ETG pair prediction, we shown that PETModule had a much better recall, a similar or better F1 score, and a larger area under the receiver operating characteristic curve. The PETModule tool is freely available at http://hulab.ucf.edu/research/projects/PETModule/.

Publication Date

7-20-2016

Publication Title

Scientific Reports

Volume

6

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1038/srep30043

Socpus ID

84979508872 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84979508872

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