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Identification of the human DPR core promoter element using machine learning
Nature ( IF 64.8 ) Pub Date : 2020-09-09 , DOI: 10.1038/s41586-020-2689-7
Long Vo Ngoc 1 , Cassidy Yunjing Huang 1 , California Jack Cassidy 1 , Claudia Medrano 1 , James T Kadonaga 1
Affiliation  

The RNA polymerase II (Pol II) core promoter is the strategic site of convergence of the signals that lead to the initiation of DNA transcription 1 – 5 , but the downstream core promoter in humans has been difficult to understand 1 – 3 . Here we analyse the human Pol II core promoter and use machine learning to generate predictive models for the downstream core promoter region (DPR) and the TATA box. We developed a method termed HARPE (high-throughput analysis of randomized promoter elements) to create hundreds of thousands of DPR (or TATA box) variants, each with known transcriptional strength. We then analysed the HARPE data by support vector regression (SVR) to provide comprehensive models for the sequence motifs, and found that the SVR-based approach is more effective than a consensus-based method for predicting transcriptional activity. These results show that the DPR is a functionally important core promoter element that is widely used in human promoters. Notably, there appears to be a duality between the DPR and the TATA box, as many promoters contain one or the other element. More broadly, these findings show that functional DNA motifs can be identified by machine learning analysis of a comprehensive set of sequence variants. A machine learning approach shows that the downstream core promoter region (DPR) is widely used in human gene promoters, and that many promoters contain either a DPR or a TATA box, but not both.

中文翻译:

使用机器学习识别人类 DPR 核心启动子元件

RNA 聚合酶 II (Pol II) 核心启动子是导致 DNA 转录起始的信号汇聚的战略位点 1-5,但人类的下游核心启动子一直难以理解 1-3。在这里,我们分析了人类 Pol II 核心启动子,并使用机器学习为下游核心启动子区域 (DPR) 和 TATA 框生成预测模型。我们开发了一种称为 HARPE(随机启动子元件的高通量分析)的方法来创建数十万个 DPR(或 TATA 框)变体,每个变体都具有已知的转录强度。然后我们通过支持向量回归 (SVR) 分析了 HARPE 数据以提供序列基序的综合模型,并发现基于 SVR 的方法比基于共识的方法更有效地预测转录活性。这些结果表明,DPR 是一种功能重要的核心启动子元件,广泛用于人类启动子。值得注意的是,DPR 和 TATA 盒之间似乎存在双重性,因为许多启动子包含一个或另一个元素。更广泛地说,这些发现表明功能性 DNA 基序可以通过机器学习分析一组全面的序列变体来识别。机器学习方法表明下游核心启动子区域 (DPR) 广泛用于人类基因启动子,并且许多启动子包含 DPR 或 TATA 框,但不能同时包含两者。这些发现表明,功能性 DNA 基序可以通过对一组综合序列变体的机器学习分析来识别。机器学习方法表明下游核心启动子区域 (DPR) 广泛用于人类基因启动子,并且许多启动子包含 DPR 或 TATA 框,但不能同时包含两者。这些发现表明,功能性 DNA 基序可以通过对一组综合序列变体的机器学习分析来识别。机器学习方法表明下游核心启动子区域 (DPR) 广泛用于人类基因启动子,并且许多启动子包含 DPR 或 TATA 框,但不能同时包含两者。
更新日期:2020-09-09
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