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) data set in plants using a high-resolution method for analysis of 59 ends of mRNA transcripts. Our data set is produced using the paired-end analysis of transcription start sites (PEAT) protocol, providing millions of TSS locations from wild-type Columbia-0 Arabidopsis thaliana whole root samples. Using this data set, 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.

Supplementary data are available at Bioinformatics online.

Abstract-Helping elderly people to live independently within their homes for as long as possible, before transitioning to higher levels of care, can significantly reduce healthcare expenditures. However, achieving this vision requires continuous monitoring of the condition of elderly adults within their homes. In particular, activity, gait velocity, movement, and location of elderly adults are critical biomarkers for healthy aging. We present a prototype integrating a wearable location-tracking sensor with back-end cloud-based data processing, thereby enabling real-time tracking and analysis of a large number of people simultaneously. The resulting vertically-integrated prototype provides a basic infrastructure for future work, including new products and services that offer real-time monitoring and early disease diagnosis to help elderly people live independently for as long as possible.