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Plant Cell. 2014 Jul;26(7):2746-60. doi: 10.1105/tpc.114.125617. Epub 2014 Jul 17.

Paired-end analysis of transcription start sites in Arabidopsis reveals plant-specific promoter signatures.

Author information

1
Department of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon 97331.
2
Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina 27708 Department of Biology, HHMI and Center for Systems Biology, Duke University, Durham, North Carolina 27708 Department of Biology, Carleton College, Northfield, Minnesota 55057.
3
Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina 27708.
4
Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina 27708 Department of Biology, HHMI and Center for Systems Biology, Duke University, Durham, North Carolina 27708.
5
Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina 27708 Department of Computer Science, Duke University, 308 Research Drive, Durham, North Carolina 27708 Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina 27710 Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, 13125 Berlin, Germany.
6
Department of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon 97331 Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina 27708 Department of Botany and Plant Pathology, Oregon State University, Corvallis, Oregon 97331 Center for Genome Research and Biocomputing, Oregon State University, Corvallis, Oregon 97331 megrawm@science.oregonstate.edu.

Abstract

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.

PMID:
25035402
PMCID:
PMC4145111
DOI:
10.1105/tpc.114.125617
[Indexed for MEDLINE]
Free PMC Article

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