<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Colleen Doherty</style></author><author><style face="normal" font="default" size="100%">Joanna Friesner</style></author><author><style face="normal" font="default" size="100%">Brian Gregory</style></author><author><style face="normal" font="default" size="100%">Ann Loraine</style></author><author><style face="normal" font="default" size="100%">Molly Megraw</style></author><author><style face="normal" font="default" size="100%">Nicholas Provart</style></author><author><style face="normal" font="default" size="100%">R Keith Slotkin</style></author><author><style face="normal" font="default" size="100%">Chris Town</style></author><author><style face="normal" font="default" size="100%">Sarah M Assmann</style></author><author><style face="normal" font="default" size="100%">Michael Axtell</style></author><author><style face="normal" font="default" size="100%">Tanya Berardini</style></author><author><style face="normal" font="default" size="100%">Sixue Chen</style></author><author><style face="normal" font="default" size="100%">Malia Gehan</style></author><author><style face="normal" font="default" size="100%">Eva Huala</style></author><author><style face="normal" font="default" size="100%">Pankaj Jaiswal</style></author><author><style face="normal" font="default" size="100%">Stephen Larson</style></author><author><style face="normal" font="default" size="100%">Song Li</style></author><author><style face="normal" font="default" size="100%">Sean May</style></author><author><style face="normal" font="default" size="100%">Todd Michael</style></author><author><style face="normal" font="default" size="100%">Chris Pires</style></author><author><style face="normal" font="default" size="100%">Chris Topp</style></author><author><style face="normal" font="default" size="100%">Justin Walley</style></author><author><style face="normal" font="default" size="100%">Eve Wurtele</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Arabidopsis bioinformatics resources: The current state, challenges, and priorities for the future</style></title><secondary-title><style face="normal" font="default" size="100%">Plant Direct</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">01/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://onlinelibrary.wiley.com/doi/full/10.1002/pld3.109</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">3</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">S Ha</style></author><author><style face="normal" font="default" size="100%">E Dimitrova</style></author><author><style face="normal" font="default" size="100%">Stefan Hoops</style></author><author><style face="normal" font="default" size="100%">D Altarawy</style></author><author><style face="normal" font="default" size="100%">M Ansariola</style></author><author><style face="normal" font="default" size="100%">D Deb</style></author><author><style face="normal" font="default" size="100%">J Glazebrook</style></author><author><style face="normal" font="default" size="100%">R Hillmer</style></author><author><style face="normal" font="default" size="100%">H Shahin</style></author><author><style face="normal" font="default" size="100%">F Katagiri</style></author><author><style face="normal" font="default" size="100%">J McDowell</style></author><author><style face="normal" font="default" size="100%">M Megraw</style></author><author><style face="normal" font="default" size="100%">J Setubal</style></author><author><style face="normal" font="default" size="100%">BM Tyler</style></author><author><style face="normal" font="default" size="100%">Reinhard Laubenbacher</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">PlantSimLab-a modeling and simulation web tool for plant biologists</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-3094-9</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">1-11</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sparks, E. E.</style></author><author><style face="normal" font="default" size="100%">Drapek, C.</style></author><author><style face="normal" font="default" size="100%">Gaudinier, A.</style></author><author><style face="normal" font="default" size="100%">Li, S.</style></author><author><style face="normal" font="default" size="100%">Ansariola, M.</style></author><author><style face="normal" font="default" size="100%">Shen, N.</style></author><author><style face="normal" font="default" size="100%">Hennacy, J. H.</style></author><author><style face="normal" font="default" size="100%">Zhang, J.</style></author><author><style face="normal" font="default" size="100%">Turco, G.</style></author><author><style face="normal" font="default" size="100%">Petricka, J. J.</style></author><author><style face="normal" font="default" size="100%">Foret, J.</style></author><author><style face="normal" font="default" size="100%">Hartemink, A. J.</style></author><author><style face="normal" font="default" size="100%">Gordan, R.</style></author><author><style face="normal" font="default" size="100%">Megraw, M.</style></author><author><style face="normal" font="default" size="100%">Brady, S. M.</style></author><author><style face="normal" font="default" size="100%">Benfey, P. N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Establishment of Expression in the SHORTROOT-SCARECROW Transcriptional Cascade through Opposing Activities of Both Activators and Repressors</style></title><secondary-title><style face="normal" font="default" size="100%">Dev Cell</style></secondary-title><short-title><style face="normal" font="default" size="100%">Developmental cell</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Arabidopsis Proteins/ genetics/ metabolism</style></keyword><keyword><style  face="normal" font="default" size="100%">Arabidopsis/ genetics/growth &amp; development/ metabolism</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes, Reporter</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes, Synthetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Plant Roots/cytology/metabolism</style></keyword><keyword><style  face="normal" font="default" size="100%">Plants, Genetically Modified</style></keyword><keyword><style  face="normal" font="default" size="100%">Promoter Regions, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Repressor Proteins/genetics/metabolism</style></keyword><keyword><style  face="normal" font="default" size="100%">Trans-Activators/genetics/metabolism</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors/ genetics/ metabolism</style></keyword><keyword><style  face="normal" font="default" size="100%">Two-Hybrid System Techniques</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2016</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.devcel.2016.09.031</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">39</style></volume><pages><style face="normal" font="default" size="100%">585-596</style></pages><isbn><style face="normal" font="default" size="100%">1878-1551 (Electronic)1534-5807 (Linking)</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Tissue-specific gene expression is often thought to arise from spatially restricted transcriptional cascades. However, it is unclear how expression is established at the top of these cascades in the absence of pre-existing specificity. We generated a transcriptional network to explore how transcription factor expression is established in the Arabidopsis thaliana root ground tissue. Regulators of the SHORTROOT-SCARECROW transcriptional cascade were validated in planta. At the top of this cascade, we identified both activators and repressors of SHORTROOT. The aggregate spatial expression of these regulators is not sufficient to predict transcriptional specificity. Instead, modeling, transcriptional reporters, and synthetic promoters support a mechanism whereby expression at the top of the SHORTROOT-SCARECROW cascade is established through opposing activities of activators and repressors.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Iizasa, Hisashi</style></author><author><style face="normal" font="default" size="100%">Wulff, Bjorn-Erik</style></author><author><style face="normal" font="default" size="100%">Alla, Nageswara R</style></author><author><style face="normal" font="default" size="100%">Maragkakis, Manolis</style></author><author><style face="normal" font="default" size="100%">Megraw, Molly</style></author><author><style face="normal" font="default" size="100%">Hatzigeorgiou, Artemis</style></author><author><style face="normal" font="default" size="100%">Iwakiri, Dai</style></author><author><style face="normal" font="default" size="100%">Takada, Kenzo</style></author><author><style face="normal" font="default" size="100%">Wiedmer, Andreas</style></author><author><style face="normal" font="default" size="100%">Showe, Louise</style></author><author><style face="normal" font="default" size="100%">Lieberman, Paul</style></author><author><style face="normal" font="default" size="100%">Nishikura, Kazuko</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Editing of Epstein-Barr virus-encoded BART6 microRNAs controls their dicer targeting and consequently affects viral latency.</style></title><secondary-title><style face="normal" font="default" size="100%">J Biol Chem</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J. Biol. Chem.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cell Line, Tumor</style></keyword><keyword><style  face="normal" font="default" size="100%">Epstein-Barr Virus Infections</style></keyword><keyword><style  face="normal" font="default" size="100%">Epstein-Barr Virus Nuclear Antigens</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Silencing</style></keyword><keyword><style  face="normal" font="default" size="100%">Herpesvirus 4, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Immediate-Early Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">MicroRNAs</style></keyword><keyword><style  face="normal" font="default" size="100%">Ribonuclease III</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA Editing</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Viral</style></keyword><keyword><style  face="normal" font="default" size="100%">Trans-Activators</style></keyword><keyword><style  face="normal" font="default" size="100%">Viral Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Virus Latency</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010 Oct 22</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">285</style></volume><pages><style face="normal" font="default" size="100%">33358-70</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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&amp;#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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">43</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Megraw, Molly</style></author><author><style face="normal" font="default" size="100%">Hatzigeorgiou, Artemis G</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MicroRNA promoter analysis.</style></title><secondary-title><style face="normal" font="default" size="100%">Methods Mol Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Methods Mol. Biol.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">MicroRNAs</style></keyword><keyword><style  face="normal" font="default" size="100%">Promoter Regions, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">592</style></volume><pages><style face="normal" font="default" size="100%">149-61</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this chapter, we present a brief overview of current knowledge about the promoters of plant microRNAs (miRNAs), and provide a step-by-step guide for predicting plant miRNA promoter elements using known transcription factor binding motifs. The approach to promoter element prediction is based on a carefully constructed collection of Positional Weight Matrices (PWMs) for known transcription factors (TFs) in Arabidopsis. A key concept of the method is to use scoring thresholds for potential binding sites that are appropriate to each individual transcription factor. While the procedure can be applied to search for Transcription Factor Binding Sites (TFBSs) in any pol-II promoter region, it is particularly practical for the case of plant miRNA promoters where upstream sequence regions and binding sites are not readily available in existing databases. The majority of the material described in this chapter is available for download at http://microrna.gr.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;http://megraw-dev.cgrb.oregonstate.edu/node/714&quot;&gt;[Link to Tools and Supplementary Materials]&lt;/a&gt;&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alexiou, Panagiotis</style></author><author><style face="normal" font="default" size="100%">Vergoulis, Thanasis</style></author><author><style face="normal" font="default" size="100%">Gleditzsch, Martin</style></author><author><style face="normal" font="default" size="100%">Prekas, George</style></author><author><style face="normal" font="default" size="100%">Dalamagas, Theodore</style></author><author><style face="normal" font="default" size="100%">Megraw, Molly</style></author><author><style face="normal" font="default" size="100%">Grosse, Ivo</style></author><author><style face="normal" font="default" size="100%">Sellis, Timos</style></author><author><style face="normal" font="default" size="100%">Hatzigeorgiou, Artemis G</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">miRGen 2.0: a database of microRNA genomic information and regulation.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nucleic Acids Res.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">3' Untranslated Regions</style></keyword><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Cell Line, Tumor</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Nucleic Acid</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Information Storage and Retrieval</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">MicroRNAs</style></keyword><keyword><style  face="normal" font="default" size="100%">Polymorphism, Single Nucleotide</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010 Jan</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">38</style></volume><pages><style face="normal" font="default" size="100%">D137-41</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;MicroRNAs are small, non-protein coding RNA molecules known to regulate the expression of genes by binding to the 3&amp;#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/.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">Database issue</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Megraw, Molly</style></author><author><style face="normal" font="default" size="100%">Pereira, Fernando</style></author><author><style face="normal" font="default" size="100%">Jensen, Shane T</style></author><author><style face="normal" font="default" size="100%">Ohler, Uwe</style></author><author><style face="normal" font="default" size="100%">Hatzigeorgiou, Artemis G</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A transcription factor affinity-based code for mammalian transcription initiation.</style></title><secondary-title><style face="normal" font="default" size="100%">Genome Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genome Res.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Base Composition</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Promoter Regions, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA Polymerase II</style></keyword><keyword><style  face="normal" font="default" size="100%">TATA Box</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Initiation Site</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription, Genetic</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2009 Apr</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">644-56</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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&amp;#39;-ends. Using this high-resolution single-peak model, we predict TSS for approximately 70% of mammalian microRNAs based on currently available data.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;http://megraw-dev.cgrb.oregonstate.edu/node/715&quot;&gt;[Links to Tools and Supplementary Materials]&lt;/a&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kawahara, Yukio</style></author><author><style face="normal" font="default" size="100%">Megraw, Molly</style></author><author><style face="normal" font="default" size="100%">Kreider, Edward</style></author><author><style face="normal" font="default" size="100%">Iizasa, Hisashi</style></author><author><style face="normal" font="default" size="100%">Valente, Louis</style></author><author><style face="normal" font="default" size="100%">Hatzigeorgiou, Artemis G</style></author><author><style face="normal" font="default" size="100%">Nishikura, Kazuko</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Frequency and fate of microRNA editing in human brain.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nucleic Acids Res.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adenosine</style></keyword><keyword><style  face="normal" font="default" size="100%">Adenosine Deaminase</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Base Sequence</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Inosine</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">MicroRNAs</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Sequence Data</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA Editing</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA Precursors</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA Processing, Post-Transcriptional</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA-Binding Proteins</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008 Sep</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">5270-80</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Primary transcripts of certain microRNA (miRNA) genes (pri-miRNAs) are subject to RNA editing that converts adenosine to inosine (A--&amp;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--&amp;gt;I editing and, thus, miRNA editing could have a large impact on the miRNA-mediated gene silencing.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">16</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhang, Lin</style></author><author><style face="normal" font="default" size="100%">Volinia, Stefano</style></author><author><style face="normal" font="default" size="100%">Bonome, Tomas</style></author><author><style face="normal" font="default" size="100%">Calin, George Adrian</style></author><author><style face="normal" font="default" size="100%">Greshock, Joel</style></author><author><style face="normal" font="default" size="100%">Yang, Nuo</style></author><author><style face="normal" font="default" size="100%">Liu, Chang-Gong</style></author><author><style face="normal" font="default" size="100%">Giannakakis, Antonis</style></author><author><style face="normal" font="default" size="100%">Alexiou, Pangiotis</style></author><author><style face="normal" font="default" size="100%">Hasegawa, Kosei</style></author><author><style face="normal" font="default" size="100%">Johnstone, Cameron N</style></author><author><style face="normal" font="default" size="100%">Megraw, Molly S</style></author><author><style face="normal" font="default" size="100%">Adams, Sarah</style></author><author><style face="normal" font="default" size="100%">Lassus, Heini</style></author><author><style face="normal" font="default" size="100%">Huang, Jia</style></author><author><style face="normal" font="default" size="100%">Kaur, Sippy</style></author><author><style face="normal" font="default" size="100%">Liang, Shun</style></author><author><style face="normal" font="default" size="100%">Sethupathy, Praveen</style></author><author><style face="normal" font="default" size="100%">Leminen, Arto</style></author><author><style face="normal" font="default" size="100%">Simossis, Victor A</style></author><author><style face="normal" font="default" size="100%">Sandaltzopoulos, Raphael</style></author><author><style face="normal" font="default" size="100%">Naomoto, Yoshio</style></author><author><style face="normal" font="default" size="100%">Katsaros, Dionyssios</style></author><author><style face="normal" font="default" size="100%">Gimotty, Phyllis A</style></author><author><style face="normal" font="default" size="100%">DeMichele, Angela</style></author><author><style face="normal" font="default" size="100%">Huang, Qihong</style></author><author><style face="normal" font="default" size="100%">Bützow, Ralf</style></author><author><style face="normal" font="default" size="100%">Rustgi, Anil K</style></author><author><style face="normal" font="default" size="100%">Weber, Barbara L</style></author><author><style face="normal" font="default" size="100%">Birrer, Michael J</style></author><author><style face="normal" font="default" size="100%">Hatzigeorgiou, Artemis G</style></author><author><style face="normal" font="default" size="100%">Croce, Carlo M</style></author><author><style face="normal" font="default" size="100%">Coukos, George</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Genomic and epigenetic alterations deregulate microRNA expression in human epithelial ovarian cancer.</style></title><secondary-title><style face="normal" font="default" size="100%">Proc Natl Acad Sci U S A</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Proc. Natl. Acad. Sci. U.S.A.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">DNA, Neoplasm</style></keyword><keyword><style  face="normal" font="default" size="100%">Down-Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Epigenesis, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Epithelial Cells</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Neoplastic</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Human</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">MicroRNAs</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasm Staging</style></keyword><keyword><style  face="normal" font="default" size="100%">Ovarian Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Ribonuclease III</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Messenger</style></keyword><keyword><style  face="normal" font="default" size="100%">Survival Analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008 May 13</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">105</style></volume><pages><style face="normal" font="default" size="100%">7004-9</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">19</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Megraw, Molly</style></author><author><style face="normal" font="default" size="100%">Sethupathy, Praveen</style></author><author><style face="normal" font="default" size="100%">Corda, Benoit</style></author><author><style face="normal" font="default" size="100%">Hatzigeorgiou, Artemis G</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">miRGen: a database for the study of animal microRNA genomic organization and function.</style></title><secondary-title><style face="normal" font="default" size="100%">Nucleic Acids Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nucleic Acids Res.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Data Interpretation, Statistical</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Nucleic Acid</style></keyword><keyword><style  face="normal" font="default" size="100%">Genomics</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Internet</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">MicroRNAs</style></keyword><keyword><style  face="normal" font="default" size="100%">Rats</style></keyword><keyword><style  face="normal" font="default" size="100%">User-Computer Interface</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2007 Jan</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">D149-55</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">Database issue</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sethupathy, Praveen</style></author><author><style face="normal" font="default" size="100%">Megraw, Molly</style></author><author><style face="normal" font="default" size="100%">Hatzigeorgiou, Artemis G</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A guide through present computational approaches for the identification of mammalian microRNA targets.</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Methods</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nat. Methods</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">3' Untranslated Regions</style></keyword><keyword><style  face="normal" font="default" size="100%">5' Untranslated Regions</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Targeting</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">MicroRNAs</style></keyword><keyword><style  face="normal" font="default" size="100%">Predictive Value of Tests</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Messenger</style></keyword><keyword><style  face="normal" font="default" size="100%">Sensitivity and Specificity</style></keyword><keyword><style  face="normal" font="default" size="100%">Software</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2006 Nov</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">881-6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">11</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Megraw, Molly</style></author><author><style face="normal" font="default" size="100%">Baev, Vesselin</style></author><author><style face="normal" font="default" size="100%">Rusinov, Ventsislav</style></author><author><style face="normal" font="default" size="100%">Jensen, Shane T</style></author><author><style face="normal" font="default" size="100%">Kalantidis, Kriton</style></author><author><style face="normal" font="default" size="100%">Hatzigeorgiou, Artemis G</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MicroRNA promoter element discovery in Arabidopsis.</style></title><secondary-title><style face="normal" font="default" size="100%">RNA</style></secondary-title><alt-title><style face="normal" font="default" size="100%">RNA</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Arabidopsis</style></keyword><keyword><style  face="normal" font="default" size="100%">Base Sequence</style></keyword><keyword><style  face="normal" font="default" size="100%">Binding Sites</style></keyword><keyword><style  face="normal" font="default" size="100%">Databases, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Feedback, Physiological</style></keyword><keyword><style  face="normal" font="default" size="100%">Genes, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">MicroRNAs</style></keyword><keyword><style  face="normal" font="default" size="100%">Promoter Regions, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">TATA Box</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Initiation Site</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2006 Sep</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">1612-9</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhang, Lin</style></author><author><style face="normal" font="default" size="100%">Huang, Jia</style></author><author><style face="normal" font="default" size="100%">Yang, Nuo</style></author><author><style face="normal" font="default" size="100%">Greshock, Joel</style></author><author><style face="normal" font="default" size="100%">Megraw, Molly S</style></author><author><style face="normal" font="default" size="100%">Giannakakis, Antonis</style></author><author><style face="normal" font="default" size="100%">Liang, Shun</style></author><author><style face="normal" font="default" size="100%">Naylor, Tara L</style></author><author><style face="normal" font="default" size="100%">Barchetti, Andrea</style></author><author><style face="normal" font="default" size="100%">Ward, Michelle R</style></author><author><style face="normal" font="default" size="100%">Yao, George</style></author><author><style face="normal" font="default" size="100%">Medina, Angelica</style></author><author><style face="normal" font="default" size="100%">O'brien-Jenkins, Ann</style></author><author><style face="normal" font="default" size="100%">Katsaros, Dionyssios</style></author><author><style face="normal" font="default" size="100%">Hatzigeorgiou, Artemis</style></author><author><style face="normal" font="default" size="100%">Gimotty, Phyllis A</style></author><author><style face="normal" font="default" size="100%">Weber, Barbara L</style></author><author><style face="normal" font="default" size="100%">Coukos, George</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">microRNAs exhibit high frequency genomic alterations in human cancer.</style></title><secondary-title><style face="normal" font="default" size="100%">Proc Natl Acad Sci U S A</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Proc. Natl. Acad. Sci. U.S.A.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Breast Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Dosage</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">MicroRNAs</style></keyword><keyword><style  face="normal" font="default" size="100%">Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Nucleic Acid Hybridization</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Ovarian Neoplasms</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistics as Topic</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2006 Jun 13</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">103</style></volume><pages><style face="normal" font="default" size="100%">9136-41</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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 &amp;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">24</style></issue></record></records></xml>