Nature. 2020 Sep;585(7825):459-463. doi: 10.1038/s41586-020-2689-7. | PubMed

Long Vo Ngoc¹, Cassidy Yunjing Huang¹, California Jack Cassidy¹, Claudia Medrano¹, James T Kadonaga²

1. Section of Molecular Biology, University of California, San Diego, La Jolla, CA, USA.
2. Section of Molecular Biology, University of California, San Diego, La Jolla, CA, USA. jkadonaga@ucsd.edu.

Abstract

The RNA polymerase II (Pol II) core promoter is the strategic site of convergence of the signals that lead to the initiation of DNA transcription1-5, but the downstream core promoter in humans has been difficult to understand1-3. 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.

Presented By Long Vo Ngoc | ORCID iD