Delta.EPI: a probabilistic voting-based enhancer-promoter interaction prediction platform

Yuyang Zhang; Haoyu Wang; Jing Liu; Junlin Li; Qing Zhang; Bixia Tang; Zhihua Zhang

doi:10.1016/j.jgg.2023.02.006

Volume 50 Issue 7

Jul. 2023

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Article Navigation > Journal of Genetics and Genomics > 2023 > 50(7): 519-527

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Delta.EPI: a probabilistic voting-based enhancer-promoter interaction prediction platform

doi: 10.1016/j.jgg.2023.02.006

a. CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation, Beijing 100101, China;
b. College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China;
c. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China

Funds:

This work was supported by Special Investigation on Science and Technology Basic Resources of MOST, China (2019FY100102), the Science and Technology Innovation 2030 – Major Project (2022ZD04017), the National Key R&D Program of China (2018YFC2000400), the National Natural Science Foundation of China (31871331, 31671342, 91940304), and the Beijing Natural Science Foundation (Z200021).

Received Date: 2022-11-22
Accepted Date: 2023-02-10
Rev Recd Date: 2023-01-20
Publish Date: 2023-07-28

Abstract

Abstract

Enhancer promoter interaction (EPI) involves most of gene transcriptional regulation in the high eukaryotes. Predicting the EPIs from given genomic loci or DNA sequences is not a trivial task. The benchmarking work so far for EPI predictors is more or less empirical and lacks quantitative model-based comparisons, posing challenges for molecular biologists to obtain reliable EPI predictions. Here, we present an EPI prediction platform, namely Delta.EPI. Based on a statistic model of the data integration, Delta.EPI is capable of comprehensively assessing the predictions from four state-of-the-art EPI predictors. Equipped with a user-friendly interface and visualization platform, Delta.EPI presents the sorted results with the confidence of EPI relevance, which may guide the molecular biologists who lack the pre-knowledge of the algorithms of EPI prediction. Last, we showcase the utility of Delta.EPI with a case study. Delta.EPI provides a powerful tool to fuel the gene regulation and 3D genome studies by ease-to-access EPI predictions. Delta.EPI can be freely accessed at https://ngdc.cncb.ac.cn/deltaEPI/.
- Enhancer–promoter interaction,
- Hi-C,
- Prediction,
- Benchmark,
- Web server

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References(60)

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