Understanding plant gene promoter architecture has long been a challenge due to the lack of relevant large-scale data sets and analysis methods. Here we present a publicly available, large-scale transcription start site (TSS) dataset in plants using a high-resolution method for analysis of 5’ ends of mRNA transcripts. Our dataset is produced using the Paired-End Analysis of Transcription Start Sites (PEAT) protocol, providing millions of TSS locations from wild-type Col-0 Arabidopsis whole root samples. Using this dataset, we grouped TSS reads into “TSS tag clusters” and categorized clusters into three spatial initiation patterns: narrow peak, broad with peak, and weak peak. We then designed a machine learning model that predicts the presence of TSS tag clusters with outstanding sensitivity and specificity for all three initiation patterns. We used this model to analyze the transcription factor binding site content of promoters exhibiting these initiation patterns. In contrast to the canonical notions of TATA-containing and more broad “TATA-less” promoters, the model shows that, in plants, the vast majority of transcription start sites are TATA-free, and are defined by a large compendium of known DNA sequence binding elements. We present results on the usage of these elements, and provide our Plant PEAT Peaks (3PEAT) model that predicts the presence of TSSs directly from sequence.Keywords: Transcription factor, Start site, Arabidopsis, MicroRNA, Gene regulatio
