UMMS Affiliation

Program in Bioinformatics and Integrative Biology; Graduate School of Biomedical Sciences

Publication Date

2020-01-22

Document Type

Article

Disciplines

Bioinformatics | Computational Biology | Genetic Phenomena | Genomics

Abstract

BACKGROUND: Many genome-wide collections of candidate cis-regulatory elements (cCREs) have been defined using genomic and epigenomic data, but it remains a major challenge to connect these elements to their target genes.

RESULTS: To facilitate the development of computational methods for predicting target genes, we develop a Benchmark of candidate Enhancer-Gene Interactions (BENGI) by integrating the recently developed Registry of cCREs with experimentally derived genomic interactions. We use BENGI to test several published computational methods for linking enhancers with genes, including signal correlation and the TargetFinder and PEP supervised learning methods. We find that while TargetFinder is the best-performing method, it is only modestly better than a baseline distance method for most benchmark datasets when trained and tested with the same cell type and that TargetFinder often does not outperform the distance method when applied across cell types.

CONCLUSIONS: Our results suggest that current computational methods need to be improved and that BENGI presents a useful framework for method development and testing.

Keywords

Benchmark, Enhancer, Genomic interactions, Machine learning, Target gene, Transcriptional regulation

Rights and Permissions

© The Author(s). 2020 Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

DOI of Published Version

10.1186/s13059-019-1924-8

Source

Moore JE, Pratt HE, Purcaro MJ, Weng Z. A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods. Genome Biol. 2020 Jan 22;21(1):17. doi: 10.1186/s13059-019-1924-8. PMID: 31969180; PMCID: PMC6977301. Link to article on publisher's site

Journal/Book/Conference Title

Genome biology

Related Resources

Link to Article in PubMed

PubMed ID

31969180

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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