McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes.
Genome Biol
; 18(1): 199, 2017 10 26.
Article
in En
| MEDLINE
| ID: mdl-29070071
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Enhancer Elements, Genetic
/
Gene Expression Regulation, Developmental
/
Machine Learning
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Animals
Language:
En
Journal:
Genome Biol
Journal subject:
BIOLOGIA MOLECULAR
/
GENETICA
Year:
2017
Type:
Article
Affiliation country:
United States