| Autor: |
Dina Hafez, Aslihan Karabacak, Sabrina Krueger, Yih-Chii Hwang, Li-San Wang, Robert P. Zinzen, Uwe Ohler |
| Jazyk: |
angličtina |
| Rok vydání: |
2017 |
| Předmět: |
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| Zdroj: |
Genome Biology, Vol 18, Iss 1, Pp 1-21 (2017) |
| Druh dokumentu: |
article |
| ISSN: |
1474-760X |
| DOI: |
10.1186/s13059-017-1316-x |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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