01718nas 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/32002616nas a2200445   4500008004100000022001400041245012900055210006900184260001600253300001300269490000800282520127800290653002101568653003401589653004001623653001901663653002501682653001101707653002901718653001401747653002101761653001601782653001501798653002101813653001901834653001801853100002001871700002201891700002301913700002401936700001801960700002701978700001702005700001802022700002102040700001802061700002002079700002202099856004902121       2010                        eng d  a1083-351X00aEditing of Epstein-Barr virus-encoded BART6 microRNAs controls their dicer targeting and consequently affects viral latency.0 aEditing of EpsteinBarr virusencoded BART6 microRNAs controls the  c2010 Oct 22  a33358-700 v2853 a<p>Certain primary transcripts of miRNA (pri-microRNAs) undergo RNA editing that converts adenosine to inosine. The Epstein-Barr virus (EBV) genome encodes multiple microRNA genes of its own. Here we report that primary transcripts of ebv-miR-BART6 (pri-miR-BART6) are edited in latently EBV-infected cells. Editing of wild-type pri-miR-BART6 RNAs dramatically reduced loading of miR-BART6-5p RNAs onto the microRNA-induced silencing complex. Editing of a mutation-containing pri-miR-BART6 found in Daudi Burkitt lymphoma and nasopharyngeal carcinoma C666-1 cell lines suppressed processing of miR-BART6 RNAs. Most importantly, miR-BART6-5p RNAs silence Dicer through multiple target sites located in the 3&#39;-UTR of Dicer mRNA. The significance of miR-BART6 was further investigated in cells in various stages of latency. We found that miR-BART6-5p RNAs suppress the EBNA2 viral oncogene required for transition from immunologically less responsive type I and type II latency to the more immunoreactive type III latency as well as Zta and Rta viral proteins essential for lytic replication, revealing the regulatory function of miR-BART6 in EBV infection and latency. Mutation and A-to-I editing appear to be adaptive mechanisms that antagonize miR-BART6 activities.</p>10aCell Line, Tumor10aEpstein-Barr Virus Infections10aEpstein-Barr Virus Nuclear Antigens10aGene Silencing10aHerpesvirus 4, Human10aHumans10aImmediate-Early Proteins10aMicroRNAs10aRibonuclease III10aRNA Editing10aRNA, Viral10aTrans-Activators10aViral Proteins10aVirus Latency1 aIizasa, Hisashi1 aWulff, Bjorn-Erik1 aAlla, Nageswara, R1 aMaragkakis, Manolis1 aMegraw, Molly1 aHatzigeorgiou, Artemis1 aIwakiri, Dai1 aTakada, Kenzo1 aWiedmer, Andreas1 aShowe, Louise1 aLieberman, Paul1 aNishikura, Kazuko  uhttps://megraw.cgrb.oregonstate.edu/node/32302129nas 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/32402836nas 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/32602435nas a2200385   4500008004100000022001400041245005900055210005800114260001300172300001200185490000700197520139400204653001401598653002401612653001201636653001801648653001001666653001101676653001201687653000901699653001401708653002801722653001601750653001901766653004101785653002501826100002001851700001801871700002001889700002001909700001901929700003001948700002201978856004902000       2008                        eng d  a1362-496200aFrequency and fate of microRNA editing in human brain.0 aFrequency and fate of microRNA editing in human brain  c2008 Sep  a5270-800 v363 a<p>Primary transcripts of certain microRNA (miRNA) genes (pri-miRNAs) are subject to RNA editing that converts adenosine to inosine (A--&gt;I RNA editing). However, the frequency of the pri-miRNA editing and the fate of edited pri-miRNAs remain largely to be determined. Examination of already known pri-miRNA editing sites indicated that adenosine residues of the UAG triplet sequence might be edited more frequently. In the present study, therefore, we conducted a large-scale survey of human pri-miRNAs containing the UAG triplet sequence. By direct sequencing of RT-PCR products corresponding to pri-miRNAs, we examined 209 pri-miRNAs and identified 43 UAG and also 43 non-UAG editing sites in 47 pri-miRNAs, which were highly edited in human brain. In vitro miRNA processing assay using recombinant Drosha-DGCR8 and Dicer-TRBP (the human immuno deficiency virus transactivating response RNA-binding protein) complexes revealed that a majority of pri-miRNA editing is likely to interfere with the miRNA processing steps. In addition, four new edited miRNAs with altered seed sequences were identified by targeted cloning and sequencing of the miRNAs that would be processed from edited pri-miRNAs. Our studies predict that approximately 16% of human pri-miRNAs are subject to A--&gt;I editing and, thus, miRNA editing could have a large impact on the miRNA-mediated gene silencing.</p>10aAdenosine10aAdenosine Deaminase10aAnimals10aBase Sequence10aBrain10aHumans10aInosine10aMice10aMicroRNAs10aMolecular Sequence Data10aRNA Editing10aRNA Precursors10aRNA Processing, Post-Transcriptional10aRNA-Binding Proteins1 aKawahara, Yukio1 aMegraw, Molly1 aKreider, Edward1 aIizasa, Hisashi1 aValente, Louis1 aHatzigeorgiou, Artemis, G1 aNishikura, Kazuko  uhttps://megraw.cgrb.oregonstate.edu/node/32703231nas a2200709   4500008004100000022001400041245010600055210006900161260001600230300001100246490000800257520120600265653001801471653002001489653002401509653002101533653001101554653003001565653004301595653001801638653001101656653001401667653002101681653002201702653002101724653001901745653002201764100001501786700002101801700001801822700002601840700001901866700001401885700002001899700002501919700002301944700002001967700002601987700002102013700001702034700001802051700001502069700001602084700001602100700002402116700001802140700002402158700002902182700002002211700002502231700002402256700002202280700001802302700001802320700002002338700002202358700002302380700003002403700002002433700001902453856004902472       2008                        eng d  a1091-649000aGenomic and epigenetic alterations deregulate microRNA expression in human epithelial ovarian cancer.0 aGenomic and epigenetic alterations deregulate microRNA expressio  c2008 May 13  a7004-90 v1053 a<p>MicroRNAs (miRNAs) are an abundant class of small noncoding RNAs that function as negative gene regulators. miRNA deregulation is involved in the initiation and progression of human cancer; however, the underlying mechanism and its contributions to genome-wide transcriptional changes in cancer are still largely unknown. We studied miRNA deregulation in human epithelial ovarian cancer by integrative genomic approach, including miRNA microarray (n = 106), array-based comparative genomic hybridization (n = 109), cDNA microarray (n = 76), and tissue array (n = 504). miRNA expression is markedly down-regulated in malignant transformation and tumor progression. Genomic copy number loss and epigenetic silencing, respectively, may account for the down-regulation of approximately 15% and at least approximately 36% of miRNAs in advanced ovarian tumors and miRNA down-regulation contributes to a genome-wide transcriptional deregulation. Last, eight miRNAs located in the chromosome 14 miRNA cluster (Dlk1-Gtl2 domain) were identified as potential tumor suppressor genes. Therefore, our results suggest that miRNAs may offer new biomarkers and therapeutic targets in epithelial ovarian cancer.</p>10aDNA, Neoplasm10aDown-Regulation10aEpigenesis, Genetic10aEpithelial Cells10aFemale10aGene Expression Profiling10aGene Expression Regulation, Neoplastic10aGenome, Human10aHumans10aMicroRNAs10aNeoplasm Staging10aOvarian Neoplasms10aRibonuclease III10aRNA, Messenger10aSurvival Analysis1 aZhang, Lin1 aVolinia, Stefano1 aBonome, Tomas1 aCalin, George, Adrian1 aGreshock, Joel1 aYang, Nuo1 aLiu, Chang-Gong1 aGiannakakis, Antonis1 aAlexiou, Pangiotis1 aHasegawa, Kosei1 aJohnstone, Cameron, N1 aMegraw, Molly, S1 aAdams, Sarah1 aLassus, Heini1 aHuang, Jia1 aKaur, Sippy1 aLiang, Shun1 aSethupathy, Praveen1 aLeminen, Arto1 aSimossis, Victor, A1 aSandaltzopoulos, Raphael1 aNaomoto, Yoshio1 aKatsaros, Dionyssios1 aGimotty, Phyllis, A1 aDeMichele, Angela1 aHuang, Qihong1 aBützow, Ralf1 aRustgi, Anil, K1 aWeber, Barbara, L1 aBirrer, Michael, J1 aHatzigeorgiou, Artemis, G1 aCroce, Carlo, M1 aCoukos, George  uhttps://megraw.cgrb.oregonstate.edu/node/32802199nas a2200301   4500008004100000022001400041245009100055210006900146260001300215300001200228490000700240520133700247653001201584653003701596653002801633653001301661653001101674653001301685653000901698653001401707653000901721653002801730100001801758700002401776700001801800700003001818856004901848       2007                        eng d  a1362-496200amiRGen: a database for the study of animal microRNA genomic organization and function.0 amiRGen a database for the study of animal microRNA genomic organ  c2007 Jan  aD149-550 v353 a<p>miRGen is an integrated database of (i) positional relationships between animal miRNAs and genomic annotation sets and (ii) animal miRNA targets according to combinations of widely used target prediction programs. A major goal of the database is the study of the relationship between miRNA genomic organization and miRNA function. This is made possible by three integrated and user friendly interfaces. The Genomics interface allows the user to explore where whole-genome collections of miRNAs are located with respect to UCSC genome browser annotation sets such as Known Genes, Refseq Genes, Genscan predicted genes, CpG islands and pseudogenes. These miRNAs are connected through the Targets interface to their experimentally supported target genes from TarBase, as well as computationally predicted target genes from optimized intersections and unions of several widely used mammalian target prediction programs. Finally, the Clusters interface provides predicted miRNA clusters at any given inter-miRNA distance and provides specific functional information on the targets of miRNAs within each cluster. All of these unique features of miRGen are designed to facilitate investigations into miRNA genomic organization, co-transcription and targeting. miRGen can be freely accessed at http://www.diana.pcbi.upenn.edu/miRGen.</p>10aAnimals10aData Interpretation, Statistical10aDatabases, Nucleic Acid10aGenomics10aHumans10aInternet10aMice10aMicroRNAs10aRats10aUser-Computer Interface1 aMegraw, Molly1 aSethupathy, Praveen1 aCorda, Benoit1 aHatzigeorgiou, Artemis, G  uhttps://megraw.cgrb.oregonstate.edu/node/32901508nas 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/33002695nas a2200481   4500008004100000022001400041245007400055210006900129260001600198300001200214490000800226520132400234653002101558653001101579653001601590653003001606653001101636653001401647653001401661653003101675653004401706653002201750653002401772100001501796700001501811700001401826700001901840700002101859700002501880700001601905700002001921700002201941700002201963700001601985700002102001700002502022700002502047700002702072700002402099700002202123700001902145856004902164       2006                        eng d  a0027-842400amicroRNAs exhibit high frequency genomic alterations in human cancer.0 amicroRNAs exhibit high frequency genomic alterations in human ca  c2006 Jun 13  a9136-410 v1033 a<p>MicroRNAs (miRNAs) are endogenous noncoding RNAs, which negatively regulate gene expression. To determine genomewide miRNA DNA copy number abnormalities in cancer, 283 known human miRNA genes were analyzed by high-resolution array-based comparative genomic hybridization in 227 human ovarian cancer, breast cancer, and melanoma specimens. A high proportion of genomic loci containing miRNA genes exhibited DNA copy number alterations in ovarian cancer (37.1%), breast cancer (72.8%), and melanoma (85.9%), where copy number alterations observed in &gt;15% tumors were considered significant for each miRNA gene. We identified 41 miRNA genes with gene copy number changes that were shared among the three cancer types (26 with gains and 15 with losses) as well as miRNA genes with copy number changes that were unique to each tumor type. Importantly, we show that miRNA copy changes correlate with miRNA expression. Finally, we identified high frequency copy number abnormalities of Dicer1, Argonaute2, and other miRNA-associated genes in breast and ovarian cancer as well as melanoma. These findings support the notion that copy number alterations of miRNAs and their regulatory genes are highly prevalent in cancer and may account partly for the frequent miRNA gene deregulation reported in several tumor types.</p>10aBreast Neoplasms10aFemale10aGene Dosage10aGene Expression Profiling10aHumans10aMicroRNAs10aNeoplasms10aNucleic Acid Hybridization10aOligonucleotide Array Sequence Analysis10aOvarian Neoplasms10aStatistics as Topic1 aZhang, Lin1 aHuang, Jia1 aYang, Nuo1 aGreshock, Joel1 aMegraw, Molly, S1 aGiannakakis, Antonis1 aLiang, Shun1 aNaylor, Tara, L1 aBarchetti, Andrea1 aWard, Michelle, R1 aYao, George1 aMedina, Angelica1 aO'brien-Jenkins, Ann1 aKatsaros, Dionyssios1 aHatzigeorgiou, Artemis1 aGimotty, Phyllis, A1 aWeber, Barbara, L1 aCoukos, George  uhttps://megraw.cgrb.oregonstate.edu/node/332