folder

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.


DOI

doi:10.1186/s13059-017-1316-x