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McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes

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Item Type:Article
Title:McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes
Creators Name:Hafez, D. and Karabacak, A. and Krueger, S. and Hwang, Y.C. and Wang, L.S. and Zinzen, R.P. and Ohler, U.
Abstract:Transcriptional enhancers regulate spatio-temporal gene expression. While genomic assays can identify putative enhancers en masse, assigning target genes is a complex challenge. We devised a machine learning approach, McEnhancer, which links target genes to putative enhancers via a semi-supervised learning algorithm that predicts gene expression patterns based on enriched sequence features. Predicted expression patterns were 73-98% accurate, predicted assignments showed strong Hi-C interaction enrichment, enhancer-associated histone modifications were evident, and known functional motifs were recovered. Our model provides a general framework to link globally identified enhancers to targets and contributes to deciphering the regulatory genome.
Keywords:Interpolated Markov Model, Enhancer to Target Gene Assignment, Gene Expression, Gene Regulation, Machine Learning, Semi-Supervised Mode, Animals, Drosophila melanogaster
Source:Genome Biology
ISSN:1474-760X
Publisher:BioMed Central (U.K.)
Volume:18
Number:1
Page Range:199
Date:26 October 2017
Official Publication:https://doi.org/10.1186/s13059-017-1316-x
PubMed:View item in PubMed

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