02325nas a2200265 4500008004100000022001400041245007400055210006900129260001500198300001200213490000700225520152900232653001501761653001301776653002101789653003401810653002701844653001301871653003401884653003801918100001601956700002001972700001801992856004902010 2015 eng d a1367-481100aTIPR: transcription initiation pattern recognition on a genome scale.0 aTIPR transcription initiation pattern recognition on a genome sc c2015 Dec 1 a3725-320 v313 a

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.

CONTACT: megrawm@science.oregonstate.edu.

[Software and Supplementary Materials Link]

10aAlgorithms10aGenomics10aMachine Learning10aMolecular Sequence Annotation10aSequence Analysis, DNA10aSoftware10aTranscription Initiation Site10aTranscription Initiation, Genetic1 aMorton, Taj1 aWong, Weng-Keen1 aMegraw, Molly uhttps://megraw.cgrb.oregonstate.edu/node/31302911nas a2200457 4500008004100000022001400041245011200055210006900167260001300236300001200249490000700261520157400268653001601842653002501858653001801883653002101901653001501922653003801937653001801975653002001993653002202013653001602035653003002051653001902081653001502100653002702115653002402142653001302166653002602179653003402205100001602239700002102255700002302276700001302299700002002312700001702332700002202349700001502371700001802386856004902404 2014 eng d a1532-298X00aPaired-end analysis of transcription start sites in Arabidopsis reveals plant-specific promoter signatures.0 aPairedend analysis of transcription start sites in Arabidopsis r c2014 Jul a2746-600 v263 a

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.

[Link to Additional Data and Supplementary Materials]

10aArabidopsis10aArabidopsis Proteins10aBinding Sites10aCluster Analysis10aDNA, Plant10aGene Expression Regulation, Plant10aGenome, Plant10aModels, Genetic10aNucleotide Motifs10aPlant Roots10aPromoter Regions, Genetic10aRNA, Messenger10aRNA, Plant10aSequence Analysis, DNA10aSpecies Specificity10aTATA Box10aTranscription Factors10aTranscription Initiation Site1 aMorton, Taj1 aPetricka, Jalean1 aCorcoran, David, L1 aLi, Song1 aWinter, Cara, M1 aCarda, Alexa1 aBenfey, Philip, N1 aOhler, Uwe1 aMegraw, Molly uhttps://megraw.cgrb.oregonstate.edu/node/319