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Exploiting sequence-based features for predicting enhancer-promoter interactions.

ABSTRACT: A large number of distal enhancers and proximal promoters form enhancer-promoter interactions to regulate target genes in the human genome. Although recent high-throughput genome-wide mapping approaches have allowed us to more comprehensively recognize potential enhancer-promoter interactions, it is still largely unknown whether sequence-based features alone are sufficient to predict such interactions.Here, we develop a new computational method (named PEP) to predict enhancer-promoter interactions based on sequence-based features only, when the locations of putative enhancers and promoters in a particular cell type are given. The two modules in PEP (PEP-Motif and PEP-Word) use different but complementary feature extraction strategies to exploit sequence-based information. The results across six different cell types demonstrate that our method is effective in predicting enhancer-promoter interactions as compared to the state-of-the-art methods that use functional genomic signals. Our work demonstrates that sequence-based features alone can reliably predict enhancer-promoter interactions genome-wide, which could potentially facilitate the discovery of important sequence determinants for long-range gene regulation.The source code of PEP is available at: https://github.com/ma-compbio/PEP .jianma@cs.cmu.edu.Supplementary data are available at Bioinformatics online.

SUBMITTER: Yang Y

PROVIDER: S-EPMC5870728 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Exploiting sequence-based features for predicting enhancer-promoter interactions.

Yang Yang Y Zhang Ruochi R Singh Shashank S Ma Jian J

Bioinformatics (Oxford, England) 20170701 14

<h4>Motivation</h4>A large number of distal enhancers and proximal promoters form enhancer-promoter interactions to regulate target genes in the human genome. Although recent high-throughput genome-wide mapping approaches have allowed us to more comprehensively recognize potential enhancer-promoter interactions, it is still largely unknown whether sequence-based features alone are sufficient to predict such interactions.<h4>Results</h4>Here, we develop a new computational method (named PEP) to p ...[more]

PMID: 28881991

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Project description:MotivationMicroRNAs (miRNAs) play important roles in general biological processes and diseases pathogenesis. Identifying miRNA target genes is an essential step to fully understand the regulatory effects of miRNAs. Many computational methods based on the sequence complementary rules and the miRNA and mRNA expression profiles have been developed for this purpose. It is noted that there have been many sequence features of miRNA targets available, including the context features of the target sites, the thermodynamic stability and the accessibility energy for miRNA-mRNA interaction. However, most of current computational methods that combine sequence and expression information do not effectively integrate full spectrum of these features; instead, they perceive putative miRNA-mRNA interactions from sequence-based prediction as equally meaningful. Therefore, these sequence features have not been fully utilized for improving miRNA target prediction.ResultsWe propose a novel regularized regression approach that is based on the adaptive Lasso procedure for detecting functional miRNA-mRNA interactions. Our method fully takes into account the gene sequence features and the miRNA and mRNA expression profiles. Given a set of sequence features for each putative miRNA-mRNA interaction and their expression values, our model quantifies the down-regulation effect of each miRNA on its targets while simultaneously estimating the contribution of each sequence feature to predicting functional miRNA-mRNA interactions. By applying our model to the expression datasets from two cancer studies, we have demonstrated our prediction results have achieved better sensitivity and specificity and are more biologically meaningful compared with those based on other methods.Availability and implementationThe source code is available at: http://nba.uth.tmc.edu/homepage/liu/miRNALasso.Supplementary informationSupplementary data are available at Bioinformatics online.ContactYin.Liu@uth.tmc.edu.

Dataset Information

Exploiting sequence-based features for predicting enhancer-promoter interactions.

Publications

Exploiting sequence-based features for predicting enhancer-promoter interactions.

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