02178nas a2200277   4500008004100000022001400041245011900055210006900174260000900243300000800252490000700260520133000267653001601597653001701613653001801630653001901648653001601667653003001683653002701713653001301740653003401753100002101787700002501808700001801833856004901851       2015                        eng d  a1471-216400aNanoCAGE-XL and CapFilter: an approach to genome wide identification of high confidence transcription start sites.0 aNanoCAGEXL and CapFilter an approach to genome wide identificati  c2015  a5970 v163 a<p><b>BACKGROUND: </b>Identifying the transcription start sites (TSS) of genes is essential for characterizing promoter regions. Several protocols have been developed to capture the 5&#39; end of transcripts via Cap Analysis of Gene Expression (CAGE) or linker-ligation strategies such as Paired-End Analysis of Transcription Start Sites (PEAT), but often require large amounts of tissue. More recently, nanoCAGE was developed for sequencing on the Illumina GAIIx to overcome these difficulties.</p><p><b>RESULTS: </b>Here we present the first publicly available adaptation of nanoCAGE for sequencing on recent ultra-high throughput platforms such as Illumina HiSeq-2000, and CapFilter, a computational pipeline that greatly increases confidence in TSS identification. We report excellent gene coverage, reproducibility, and precision in transcription start site discovery for samples from Arabidopsis thaliana roots.</p><p><b>CONCLUSION: </b>nanoCAGE-XL together with CapFilter allows for genome wide identification of high confidence transcription start sites in large eukaryotic genomes.</p><p><a href="http://megraw-dev.cgrb.oregonstate.edu/nanocage">[Link to Protocol, Additional Data, and Supplementary Materials]</a></p><p><a href="http://megraw-dev.cgrb.oregonstate.edu/capfilter">[Link to CapFilter Software]</a></p>10aArabidopsis10aGenes, Plant10aGenome, Plant10aNanotechnology10aPlant Roots10aPromoter Regions, Genetic10aSequence Analysis, DNA10aSoftware10aTranscription Initiation Site1 aCumbie, Jason, S1 aIvanchenko, Maria, G1 aMegraw, Molly  uhttps://megraw.cgrb.oregonstate.edu/node/31102325nas 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<p><b>MOTIVATION: </b>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.</p><p><b>RESULTS: </b>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.</p><p><b>CONTACT: </b>megrawm@science.oregonstate.edu.</p><p><a href="http://megraw-dev.cgrb.oregonstate.edu/TIPR">[Software and Supplementary Materials Link]</a></p>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<p>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&#39; 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 &quot;TSS tag clusters&quot; 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 &quot;TATA-less&quot; 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.</p><p><a href="http://megraw-dev.cgrb.oregonstate.edu/3PEAT">[Link to Additional Data and Supplementary Materials]</a></p>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/31902836nas a2200337   4500008004100000022001400041245008700055210006900142260001300211300001100224490000700235520183700242653002102079653002302100653000802123653003102131653001802162653001102180653003002191653002202221653001302243653002602256653003402282653002702316100001802343700002202361700002102383700001502404700003002419856004902449       2009                        eng d  a1088-905100aA transcription factor affinity-based code for mammalian transcription initiation.0 atranscription factor affinitybased code for mammalian transcript  c2009 Apr  a644-560 v193 a<p>The recent arrival of large-scale cap analysis of gene expression (CAGE) data sets in mammals provides a wealth of quantitative information on coding and noncoding RNA polymerase II transcription start sites (TSS). Genome-wide CAGE studies reveal that a large fraction of TSS exhibit peaks where the vast majority of associated tags map to a particular location ( approximately 45%), whereas other active regions contain a broader distribution of initiation events. The presence of a strong single peak suggests that transcription at these locations may be mediated by position-specific sequence features. We therefore propose a new model for single-peaked TSS based solely on known transcription factors (TFs) and their respective regions of positional enrichment. This probabilistic model leads to near-perfect classification results in cross-validation (auROC = 0.98), and performance in genomic scans demonstrates that TSS prediction with both high accuracy and spatial resolution is achievable for a specific but large subgroup of mammalian promoters. The interpretable model structure suggests a DNA code in which canonical sequence features such as TATA-box, Initiator, and GC content do play a significant role, but many additional TFs show distinct spatial biases with respect to TSS location and are important contributors to the accurate prediction of single-peak transcription initiation sites. The model structure also reveals that CAGE tag clusters distal from annotated gene starts have distinct characteristics compared to those close to gene 5&#39;-ends. Using this high-resolution single-peak model, we predict TSS for approximately 70% of mammalian microRNAs based on currently available data.</p><p><a href="http://megraw-dev.cgrb.oregonstate.edu/node/715">[Links to Tools and Supplementary Materials]</a></p>10aBase Composition10aDatabases, Genetic10aDNA10aGene Expression Regulation10aGenome, Human10aHumans10aPromoter Regions, Genetic10aRNA Polymerase II10aTATA Box10aTranscription Factors10aTranscription Initiation Site10aTranscription, Genetic1 aMegraw, Molly1 aPereira, Fernando1 aJensen, Shane, T1 aOhler, Uwe1 aHatzigeorgiou, Artemis, G  uhttps://megraw.cgrb.oregonstate.edu/node/32601854nas a2200337   4500008004100000022001400041245005600055210005500111260001300166300001100179490000700190520089800197653001601095653001801111653001801129653002301147653002801170653001701198653001401215653003001229653001301259653002601272653003401298100001801332700001901350700002401369700002101393700002301414700003001437856004901467       2006                        eng d  a1355-838200aMicroRNA promoter element discovery in Arabidopsis.0 aMicroRNA promoter element discovery in Arabidopsis  c2006 Sep  a1612-90 v123 a<p>In this study we present a method of identifying Arabidopsis miRNA promoter elements using known transcription factor binding motifs. We provide a comparative analysis of the representation of these elements in miRNA promoters, protein-coding gene promoters, and random genomic sequences. We report five transcription factor (TF) binding motifs that show evidence of overrepresentation in miRNA promoter regions relative to the promoter regions of protein-coding genes. This investigation is based on the analysis of 800-nucleotide regions upstream of 63 experimentally verified Transcription Start Sites (TSS) for miRNA primary transcripts in Arabidopsis. While the TATA-box binding motif was also previously reported by Xie and colleagues, the transcription factors AtMYC2, ARF, SORLREP3, and LFY are identified for the first time as overrepresented binding motifs in miRNA promoters.</p>10aArabidopsis10aBase Sequence10aBinding Sites10aDatabases, Genetic10aFeedback, Physiological10aGenes, Plant10aMicroRNAs10aPromoter Regions, Genetic10aTATA Box10aTranscription Factors10aTranscription Initiation Site1 aMegraw, Molly1 aBaev, Vesselin1 aRusinov, Ventsislav1 aJensen, Shane, T1 aKalantidis, Kriton1 aHatzigeorgiou, Artemis, G  uhttps://megraw.cgrb.oregonstate.edu/node/331