ABSTRACT
MOTIVATION: The computational identification of gene transcription start sites (TSSs) can provide insights into the regulation and function of genes without performing expensive experiments, particularly in organisms with incomplete annotations. High-resolution general-purpose TSS prediction remains a challenging problem, with little recent progress on the identification and differentiation of TSSs which are arranged in different spatial patterns along the chromosome. RESULTS: In this work, we present the Transcription Initiation Pattern Recognizer (TIPR), a sequence-based machine learning model that identifies TSSs with high accuracy and resolution for multiple spatial distribution patterns along the genome, including broadly distributed TSS patterns that have previously been difficult to characterize. TIPR predicts not only the locations of TSSs but also the expected spatial initiation pattern each TSS will form along the chromosome-a novel capability for TSS prediction algorithms. As spatial initiation patterns are associated with spatiotemporal expression patterns and gene function, this capability has the potential to improve gene annotations and our understanding of the regulation of transcription initiation. The high nucleotide resolution of this model locates TSSs within 10 nucleotides or less on average. AVAILABILITY AND IMPLEMENTATION: Model source code is made available online at http://megraw.cgrb.oregonstate.edu/software/TIPR/. CONTACT: megrawm@science.oregonstate.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Subject(s)
Transcription Initiation Site , Transcription Initiation, Genetic , Algorithms , Genomics , Machine Learning , Molecular Sequence Annotation , Sequence Analysis, DNA , SoftwareABSTRACT
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 5' 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.
Subject(s)
Arabidopsis/genetics , Gene Expression Regulation, Plant , Genome, Plant/genetics , Promoter Regions, Genetic/genetics , Sequence Analysis, DNA/methods , Transcription Initiation Site , Arabidopsis/metabolism , Arabidopsis Proteins/genetics , Arabidopsis Proteins/metabolism , Binding Sites , Cluster Analysis , DNA, Plant/genetics , Models, Genetic , Nucleotide Motifs , Plant Roots/genetics , Plant Roots/metabolism , RNA, Messenger/genetics , RNA, Plant/genetics , Species Specificity , TATA Box , Transcription Factors/genetics , Transcription Factors/metabolismABSTRACT
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.
