A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods

Abstract

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

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