A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods
A curated benchmark of enhancer-gene interactions for evaluating enhancer-target gene prediction methods
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DOI：
10.1186/s13059-019-1924-8
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发表时间：
2020-01-22
期刊：
影响因子：
12.3
通讯作者：
Weng, Zhiping
中科院分区：
文献类型：
--
作者：
Moore, Jill E.;Pratt, Henry E.;Weng, Zhiping
背景许多候选顺式调控元件（cCRE）的全基因组集合已经使用基因组和表观基因组数据进行了定义，但将这些元件与其目标基因连接起来仍然是一个重大挑战。结果 为了促进预测靶基因的计算方法的开发，我们通过将最近开发的 cCRE 注册表与实验衍生的基因组相互作用相结合，开发了候选增强子基因相互作用 (BENGI) 的基准。我们使用 BENGI 测试了几种已发布的用于将增强子与基因连接的计算方法，包括信号相关性以及 TargetFinder 和 PEP 监督学习方法。我们发现，虽然 TargetFinder 是性能最佳的方法，但在使用相同细胞类型进行训练和测试时，对于大多数基准数据集来说，它仅比基线距离方法稍好一些，并且在跨细胞类型应用时，TargetFinder 通常不会优于距离方法。结论 我们的结果表明，当前的计算方法需要改进，并且 BENGI 为方法开发和测试提供了一个有用的框架。
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.