02911nas 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/31901718nas a2200325 4500008004100000022001400041245012600055210006900181260000900250300000800259490000700267520073000274653001501004653001201019653001601031653002601047653002801073653003101101653002901132653001101161653001401172653003401186653003001220653001301250653002601263100001801289700002101307700001501328856004901343 2013 eng d a1474-760X00aSustained-input switches for transcription factors and microRNAs are central building blocks of eukaryotic gene circuits.0 aSustainedinput switches for transcription factors and microRNAs  c2013 aR850 v143 a

WaRSwap is a randomization algorithm that for the first time provides a practical network motif discovery method for large multi-layer networks, for example those that include transcription factors, microRNAs, and non-regulatory protein coding genes. The algorithm is applicable to systems with tens of thousands of genes, while accounting for critical aspects of biological networks, including self-loops, large hubs, and target rearrangements. We validate WaRSwap on a newly inferred regulatory network from Arabidopsis thaliana, and compare outcomes on published Drosophila and human networks. Specifically, sustained input switches are among the few over-represented circuits across this diverse set of eukaryotes.

10aAlgorithms10aAnimals10aArabidopsis10aComputational Biology10aDrosophila melanogaster10aGene Expression Regulation10aGene Regulatory Networks10aHumans10aMicroRNAs10aMolecular Sequence Annotation10aNucleic Acid Conformation10aSoftware10aTranscription Factors1 aMegraw, Molly1 aMukherjee, Sayan1 aOhler, Uwe uhttps://megraw.cgrb.oregonstate.edu/node/32002390nas a2200433 4500008004100000022001400041245007000055210006600125260001600191300000800207490000600215520115000221653001601371653002501387653003001412653002901442653001401471653001601485653003101501653002001532653002601552653003301578100002201611700001801633700001801651700002501669700001601694700001901710700001601729700001501745700002901760700001501789700002201804700001501826700001701841700002701858700002201885856004901907 2011 eng d a1744-429200aA stele-enriched gene regulatory network in the Arabidopsis root.0 asteleenriched gene regulatory network in the Arabidopsis root c2011 Jan 18 a4590 v73 a

Tightly controlled gene expression is a hallmark of multicellular development and is accomplished by transcription factors (TFs) and microRNAs (miRNAs). Although many studies have focused on identifying downstream targets of these molecules, less is known about the factors that regulate their differential expression. We used data from high spatial resolution gene expression experiments and yeast one-hybrid (Y1H) and two-hybrid (Y2H) assays to delineate a subset of interactions occurring within a gene regulatory network (GRN) that determines tissue-specific TF and miRNA expression in plants. We find that upstream TFs are expressed in more diverse cell types than their targets and that promoters that are bound by a relatively large number of TFs correspond to key developmental regulators. The regulatory consequence of many TFs for their target was experimentally determined using genetic analysis. Remarkably, molecular phenotypes were identified for 65% of the TFs, but morphological phenotypes were associated with only 16%. This indicates that the GRN is robust, and that gene expression changes may be canalized or buffered.

10aArabidopsis10aArabidopsis Proteins10aGene Expression Profiling10aGene Regulatory Networks10aMicroRNAs10aPlant Roots10aReproducibility of Results10aSystems Biology10aTranscription Factors10aTwo-Hybrid System Techniques1 aBrady, Siobhan, M1 aZhang, Lifang1 aMegraw, Molly1 aMartinez, Natalia, J1 aJiang, Eric1 aYi, Charles, S1 aLiu, Weilin1 aZeng, Anna1 aTaylor-Teeples, Mallorie1 aKim, Dahae1 aAhnert, Sebastian1 aOhler, Uwe1 aWare, Doreen1 aWalhout, Albertha, J M1 aBenfey, Philip, N uhttps://megraw.cgrb.oregonstate.edu/node/32202836nas 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

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'-ends. Using this high-resolution single-peak model, we predict TSS for approximately 70% of mammalian microRNAs based on currently available data.

[Links to Tools and Supplementary Materials]

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/326