Identification of the Human DPR Promoter Element by using Machine Learning

The RNA polymerase II (Pol II) core promoter is the strategic site of convergence of the signals that lead to transcription initiation, but the downstream core promoter in humans has been difficult to decipher. Here, we analyze 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 that are each of known transcriptional strength. We then analyzed 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 studies revealed that the DPR is a functionally important core promoter element that is widely used in human promoters. Importantly, there appears to be a duality between the DPR and 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. Overall design: Analysis of human core promoters using HARPE, applied to the Downstream core Promoter Region and TATA-box.