McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes
Authors
- D. Hafez
- A. Karabacak
- S. Krueger
- Y.C. Hwang
- L.S. Wang
- R.P. Zinzen
- U. Ohler
Journal
- Genome Biology
Citation
- Genome Biol 18 (1): 199
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.