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McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes
Dina Hafez,
Aslihan Karabacak,
Sabrina Krueger,
Yih-Chii Hwang,
Li-San Wang,
Robert Zinzen,
Uwe Ohler
Genome Biology
2017
18 1 199
DOI
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.
Additional info
Software page and tool download
GitHub Repository
