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

Genome Biol. 2017 Oct 26;18(1):199. doi: 10.1186/s13059-017-1316-x.


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: Drosophila melanogaster; Enhancer to target gene assignment; Gene expression; Gene regulation; Interpolated Markov model; Machine learning; Semi-supervised model.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, N.I.H., Extramural

MeSH terms

  • Animals
  • Deoxyribonuclease I
  • Drosophila melanogaster / embryology
  • Drosophila melanogaster / genetics
  • Embryonic Development / genetics
  • Enhancer Elements, Genetic*
  • Gene Expression Regulation, Developmental*
  • Genes, Reporter
  • Histone Code
  • Machine Learning*
  • Nucleotide Motifs
  • Promoter Regions, Genetic
  • Sequence Analysis, DNA
  • Transcription Factors / metabolism


  • Transcription Factors
  • Deoxyribonuclease I