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/31301718nas 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<p>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.</p>10aAlgorithms10aAnimals10aArabidopsis10aComputational Biology10aDrosophila melanogaster10aGene Expression Regulation10aGene Regulatory Networks10aHumans10aMicroRNAs10aMolecular Sequence Annotation10aNucleic Acid Conformation10aSoftware10aTranscription Factors1 aMegraw, Molly1 aMukherjee, Sayan1 aOhler, Uwe  uhttps://megraw.cgrb.oregonstate.edu/node/32002129nas a2200421   4500008004100000022001400041245007500055210006900130260001300199300001200212490000700224520091800231653002801149653001501177653001201192653002101204653002601225653002301251653002801274653001101302653003801313653001301351653000901364653001401373653003601387653001301423653002601436100002401462700002401486700002301510700001901533700002401552700001801576700001601594700001801610700003001628856004901658       2010                        eng d  a1362-496200amiRGen 2.0: a database of microRNA genomic information and regulation.0 amiRGen 20 a database of microRNA genomic information and regulat  c2010 Jan  aD137-410 v383 a<p>MicroRNAs are small, non-protein coding RNA molecules known to regulate the expression of genes by binding to the 3&#39;UTR region of mRNAs. MicroRNAs are produced from longer transcripts which can code for more than one mature miRNAs. miRGen 2.0 is a database that aims to provide comprehensive information about the position of human and mouse microRNA coding transcripts and their regulation by transcription factors, including a unique compilation of both predicted and experimentally supported data. Expression profiles of microRNAs in several tissues and cell lines, single nucleotide polymorphism locations, microRNA target prediction on protein coding genes and mapping of miRNA targets of co-regulated miRNAs on biological pathways are also integrated into the database and user interface. The miRGen database will be continuously maintained and freely available at http://www.microrna.gr/mirgen/.</p>10a3' Untranslated Regions10aAlgorithms10aAnimals10aCell Line, Tumor10aComputational Biology10aDatabases, Genetic10aDatabases, Nucleic Acid10aHumans10aInformation Storage and Retrieval10aInternet10aMice10aMicroRNAs10aPolymorphism, Single Nucleotide10aSoftware10aTranscription Factors1 aAlexiou, Panagiotis1 aVergoulis, Thanasis1 aGleditzsch, Martin1 aPrekas, George1 aDalamagas, Theodore1 aMegraw, Molly1 aGrosse, Ivo1 aSellis, Timos1 aHatzigeorgiou, Artemis, G  uhttps://megraw.cgrb.oregonstate.edu/node/32401508nas a2200301   4500008004100000022001400041245010700055210006900162260001300231300001000244490000600254520059300260653002800853653002800881653001200909653002600921653001900947653001100966653001400977653003000991653001901021653003201040653001301072100002401085700001801109700003001127856004901157       2006                        eng d  a1548-709100aA guide through present computational approaches for the identification of mammalian microRNA targets.0 aguide through present computational approaches for the identific  c2006 Nov  a881-60 v33 a<p>Computational microRNA (miRNA) target prediction is a field in flux. Here we present a guide through five widely used mammalian target prediction programs. We include an analysis of the performance of these individual programs and of various combinations of these programs. For this analysis we compiled several benchmark data sets of experimentally supported miRNA-target gene interactions. Based on the results, we provide a discussion on the status of target prediction and also suggest a stepwise approach toward predicting and selecting miRNA targets for experimental testing.</p>10a3' Untranslated Regions10a5' Untranslated Regions10aAnimals10aComputational Biology10aGene Targeting10aHumans10aMicroRNAs10aPredictive Value of Tests10aRNA, Messenger10aSensitivity and Specificity10aSoftware1 aSethupathy, Praveen1 aMegraw, Molly1 aHatzigeorgiou, Artemis, G  uhttps://megraw.cgrb.oregonstate.edu/node/330