Department of Computer Science, University of Central Florida, Orlando, FL, USA.
Burnett School of Biomedical Science, College of Medicine, University of Central Orlando, Orlando, FL, USA.
Bioinformatics. 2019 Oct 15;35(20):3877-3883. doi: 10.1093/bioinformatics/btz641.
The identification of enhancer-promoter interactions (EPIs), especially condition-specific ones, is important for the study of gene transcriptional regulation. Existing experimental approaches for EPI identification are still expensive, and available computational methods either do not consider or have low performance in predicting condition-specific EPIs.
We developed a novel computational method called EPIP to reliably predict EPIs, especially condition-specific ones. EPIP is capable of predicting interactions in samples with limited data as well as in samples with abundant data. Tested on more than eight cell lines, EPIP reliably identifies EPIs, with an average area under the receiver operating characteristic curve of 0.95 and an average area under the precision-recall curve of 0.73. Tested on condition-specific EPIPs, EPIP correctly identified 99.26% of them. Compared with two recently developed methods, EPIP outperforms them with a better accuracy.
The EPIP tool is freely available at http://www.cs.ucf.edu/˜xiaoman/EPIP/.
Supplementary data are available at Bioinformatics online.
增强子-启动子相互作用（EPIs）的鉴定，特别是条件特异性的相互作用，对于研究基因转录调控非常重要。现有的 EPI 鉴定实验方法仍然昂贵，并且现有的计算方法要么不考虑，要么在预测条件特异性 EPI 方面性能较低。
我们开发了一种名为 EPIP 的新型计算方法，可可靠地预测 EPI，特别是条件特异性的 EPI。EPIP 能够预测数据有限和数据丰富的样本中的相互作用。在超过八个细胞系上进行测试，EPIP 可靠地识别 EPI，其接收者操作特征曲线下的平均面积为 0.95，精度-召回曲线下的平均面积为 0.73。在条件特异性 EPI 上进行测试时，EPIP 正确识别了 99.26%的 EPI。与最近开发的两种方法相比，EPIP 的准确性更好。
EPIP 工具可在 http://www.cs.ucf.edu/˜xiaoman/EPIP/ 上免费获得。
补充数据可在生物信息学在线获得。
