Blavatnik School of Computer Science, Tel Aviv University, 69978, Tel Aviv, Israel.
Department of Human Molecular Genetics & Biochemistry, Sackler School of Medicine, Tel Aviv University, 69978, Tel Aviv, Israel.
Genome Biol. 2018 May 1;19(1):56. doi: 10.1186/s13059-018-1432-2.
Recent sequencing technologies enable joint quantification of promoters and their enhancer regions, allowing inference of enhancer-promoter links. We show that current enhancer-promoter inference methods produce a high rate of false positive links. We introduce FOCS, a new inference method, and by benchmarking against ChIA-PET, HiChIP, and eQTL data show that it results in lower false discovery rates and at the same time higher inference power. By applying FOCS to 2630 samples taken from ENCODE, Roadmap Epigenomics, FANTOM5, and a new compendium of GRO-seq samples, we provide extensive enhancer-promotor maps ( http://acgt.cs.tau.ac.il/focs ). We illustrate the usability of our maps for deriving biological hypotheses.
最近的测序技术能够联合定量分析启动子及其增强子区域，从而推断增强子-启动子之间的联系。我们发现，目前的增强子-启动子推断方法会产生大量的假阳性关联。我们引入了一种新的推断方法 FOCS，并通过与 ChIA-PET、HiChIP 和 eQTL 数据进行基准测试，结果表明该方法能够降低假发现率，同时提高推断能力。我们将 FOCS 应用于从 ENCODE、Roadmap Epigenomics、FANTOM5 以及一个新的 GRO-seq 样本综合集中获取的 2630 个样本，提供了广泛的增强子-启动子图谱（http://acgt.cs.tau.ac.il/focs）。我们说明了这些图谱在得出生物学假设方面的可用性。
