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.
Conflict of interest statement
Z. Weng is a cofounder of Rgenta Therapeutics and she serves on its scientific advisory board.
Opening up the blackbox: an interpretable deep neural network-based classifier for cell-type specific enhancer predictions.BMC Syst Biol. 2016 Aug 1;10 Suppl 2(Suppl 2):54. doi: 10.1186/s12918-016-0302-3. BMC Syst Biol. 2016. PMID: 27490187 Free PMC article.
Local Epigenomic Data are more Informative than Local Genome Sequence Data in Predicting Enhancer-Promoter Interactions Using Neural Networks.Genes (Basel). 2019 Dec 29;11(1):41. doi: 10.3390/genes11010041. Genes (Basel). 2019. PMID: 31905774 Free PMC article.
Integrating distal and proximal information to predict gene expression via a densely connected convolutional neural network.Bioinformatics. 2020 Jan 15;36(2):496-503. doi: 10.1093/bioinformatics/btz562. Bioinformatics. 2020. PMID: 31318408
Computational schemes for the prediction and annotation of enhancers from epigenomic assays.Methods. 2015 Jan 15;72:86-94. doi: 10.1016/j.ymeth.2014.10.008. Epub 2014 Oct 15. Methods. 2015. PMID: 25461775 Free PMC article. Review.
A survey of recently emerged genome-wide computational enhancer predictor tools.Comput Biol Chem. 2018 Jun;74:132-141. doi: 10.1016/j.compbiolchem.2018.03.019. Epub 2018 Mar 16. Comput Biol Chem. 2018. PMID: 29602043 Review.
Cited by 1 article
Exploring 3D chromatin contacts in gene regulation: The evolution of approaches for the identification of functional enhancer-promoter interaction.Comput Struct Biotechnol J. 2020 Feb 28;18:558-570. doi: 10.1016/j.csbj.2020.02.013. eCollection 2020. Comput Struct Biotechnol J. 2020. PMID: 32226593 Free PMC article. Review.