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| Item Type: | Article |
|---|---|
| Title: | McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes |
| Creators Name: | Hafez, D. and Karabacak, A. and Krueger, S. and Hwang, Y.C. and Wang, L.S. and Zinzen, R.P. and Ohler, U. |
| 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. |
| Keywords: | Interpolated Markov Model, Enhancer to Target Gene Assignment, Gene Expression, Gene Regulation, Machine Learning, Semi-Supervised Mode, Animals, Drosophila melanogaster |
| Source: | Genome Biology |
| ISSN: | 1474-760X |
| Publisher: | BioMed Central |
| Volume: | 18 |
| Number: | 1 |
| Page Range: | 199 |
| Date: | 26 October 2017 |
| Official Publication: | https://doi.org/10.1186/s13059-017-1316-x |
| PubMed: | View item in PubMed |
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