<?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%">Joanna Friesner</style></author><author><style face="normal" font="default" size="100%">Sarah M. Assmann</style></author><author><style face="normal" font="default" size="100%">Ruth Bastow</style></author><author><style face="normal" font="default" size="100%">Julia Bailey-Serres</style></author><author><style face="normal" font="default" size="100%">Jim Beynon</style></author><author><style face="normal" font="default" size="100%">Volker Brendel</style></author><author><style face="normal" font="default" size="100%">C. Robin Buell</style></author><author><style face="normal" font="default" size="100%">Alexander Bucksch</style></author><author><style face="normal" font="default" size="100%">Wolfgang Busch</style></author><author><style face="normal" font="default" size="100%">Taku Demura</style></author><author><style face="normal" font="default" size="100%">Jose R. Dinneny</style></author><author><style face="normal" font="default" size="100%">Colleen J. Doherty</style></author><author><style face="normal" font="default" size="100%">Andrea L. Eveland</style></author><author><style face="normal" font="default" size="100%">Pascal Falter-Braun</style></author><author><style face="normal" font="default" size="100%">Malia A. Gehan</style></author><author><style face="normal" font="default" size="100%">Michael Gonzales</style></author><author><style face="normal" font="default" size="100%">Erich Grotewold</style></author><author><style face="normal" font="default" size="100%">Rodrigo Gutierrez</style></author><author><style face="normal" font="default" size="100%">Ute Kramer</style></author><author><style face="normal" font="default" size="100%">Gabriel Krouk</style></author><author><style face="normal" font="default" size="100%">Shisong Ma</style></author><author><style face="normal" font="default" size="100%">R.J. Cody Markelz</style></author><author><style face="normal" font="default" size="100%">Molly Megraw</style></author><author><style face="normal" font="default" size="100%">Blake C. Meyers</style></author><author><style face="normal" font="default" size="100%">James A.H. Murray</style></author><author><style face="normal" font="default" size="100%">Nicholas J. Provart</style></author><author><style face="normal" font="default" size="100%">Sue Rhee</style></author><author><style face="normal" font="default" size="100%">Roger Smith</style></author><author><style face="normal" font="default" size="100%">Edgar P. Spalding</style></author><author><style face="normal" font="default" size="100%">Crispin Taylor</style></author><author><style face="normal" font="default" size="100%">Tracy K. Teal</style></author><author><style face="normal" font="default" size="100%">Keiko U. Torii</style></author><author><style face="normal" font="default" size="100%">Chris Town</style></author><author><style face="normal" font="default" size="100%">Matthew Vaughn</style></author><author><style face="normal" font="default" size="100%">Richard Vierstra</style></author><author><style face="normal" font="default" size="100%">Doreen Ware</style></author><author><style face="normal" font="default" size="100%">Olivia Wilkins</style></author><author><style face="normal" font="default" size="100%">Cranos Williams</style></author><author><style face="normal" font="default" size="100%">Siobhan M. Brady</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Next Generation of Training for Arabidopsis Researchers: Bioinformatics and Quantitative Biology</style></title><secondary-title><style face="normal" font="default" size="100%">Plant Physiology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2017</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.plantphysiol.org/content/175/4/1499</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">175</style></volume><pages><style face="normal" font="default" size="100%">1499-1509</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div&gt;It has been more than 50 years since Arabidopsis (Arabidopsis thaliana) was first introduced as a model organism to understand basic processes in plant biology. A well-organized scientific community has used this small reference plant species to make numerous fundamental plant biology discoveries (Provart et al., 2016). Due to an extremely well-annotated genome and advances in high-throughput sequencing, our understanding of this organism and other plant species has become even more intricate and complex. Computational resources, including CyVerse,3 Araport,4 The Arabidopsis Information Resource (TAIR),5 and BAR,6 have further facilitated novel findings with just the click of a mouse. As we move toward understanding biological systems, Arabidopsis researchers will need to use more quantitative and computational approaches to extract novel biological findings from these data. Here, we discuss guidelines, skill sets, and core competencies that should be considered when developing curricula or training undergraduate or graduate students, postdoctoral researchers, and faculty. A selected case study provides more specificity as to the concrete issues plant biologists face and how best to address such challenges.&lt;/div&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%">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%">Morton, Taj</style></author><author><style face="normal" font="default" size="100%">Petricka, Jalean</style></author><author><style face="normal" font="default" size="100%">Corcoran, David L</style></author><author><style face="normal" font="default" size="100%">Li, Song</style></author><author><style face="normal" font="default" size="100%">Winter, Cara M</style></author><author><style face="normal" font="default" size="100%">Carda, Alexa</style></author><author><style face="normal" font="default" size="100%">Benfey, Philip N</style></author><author><style face="normal" font="default" size="100%">Ohler, Uwe</style></author><author><style face="normal" font="default" size="100%">Megraw, Molly</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Paired-end analysis of transcription start sites in Arabidopsis reveals plant-specific promoter signatures.</style></title><secondary-title><style face="normal" font="default" size="100%">Plant Cell</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Plant Cell</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Arabidopsis</style></keyword><keyword><style  face="normal" font="default" size="100%">Arabidopsis Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Binding Sites</style></keyword><keyword><style  face="normal" font="default" size="100%">Cluster Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Genome, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Nucleotide Motifs</style></keyword><keyword><style  face="normal" font="default" size="100%">Plant Roots</style></keyword><keyword><style  face="normal" font="default" size="100%">Promoter Regions, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Messenger</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequence Analysis, DNA</style></keyword><keyword><style  face="normal" font="default" size="100%">Species Specificity</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%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014 Jul</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">26</style></volume><pages><style face="normal" font="default" size="100%">2746-60</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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&amp;#39; ends of mRNA transcripts. Our data set is produced using the paired-end analysis of transcription start sites (PEAT) protocol, providing millions of TSS locations from wild-type Columbia-0 Arabidopsis thaliana whole root samples. Using this data set, we grouped TSS reads into &amp;quot;TSS tag clusters&amp;quot; and categorized clusters into three spatial initiation patterns: narrow peak, broad with peak, and weak peak. We then designed a machine learning model that predicts the presence of TSS tag clusters with outstanding sensitivity and specificity for all three initiation patterns. We used this model to analyze the transcription factor binding site content of promoters exhibiting these initiation patterns. In contrast to the canonical notions of TATA-containing and more broad &amp;quot;TATA-less&amp;quot; promoters, the model shows that, in plants, the vast majority of transcription start sites are TATA free and are defined by a large compendium of known DNA sequence binding elements. We present results on the usage of these elements and provide our Plant PEAT Peaks (3PEAT) model that predicts the presence of TSSs directly from sequence.&lt;/p&gt;&lt;p&gt;&lt;a href=&quot;http://megraw-dev.cgrb.oregonstate.edu/3PEAT&quot;&gt;[Link to Additional Data and Supplementary Materials]&lt;/a&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</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%">Petricka, Jalean J</style></author><author><style face="normal" font="default" size="100%">Schauer, Monica A</style></author><author><style face="normal" font="default" size="100%">Megraw, Molly</style></author><author><style face="normal" font="default" size="100%">Breakfield, Natalie W</style></author><author><style face="normal" font="default" size="100%">Thompson, J Will</style></author><author><style face="normal" font="default" size="100%">Georgiev, Stoyan</style></author><author><style face="normal" font="default" size="100%">Soderblom, Erik J</style></author><author><style face="normal" font="default" size="100%">Ohler, Uwe</style></author><author><style face="normal" font="default" size="100%">Moseley, Martin Arthur</style></author><author><style face="normal" font="default" size="100%">Grossniklaus, Ueli</style></author><author><style face="normal" font="default" size="100%">Benfey, Philip N</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The protein expression landscape of the Arabidopsis root.</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%">Arabidopsis</style></keyword><keyword><style  face="normal" font="default" size="100%">Arabidopsis Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Base Sequence</style></keyword><keyword><style  face="normal" font="default" size="100%">Chromatography, Liquid</style></keyword><keyword><style  face="normal" font="default" size="100%">DNA Primers</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Plant Roots</style></keyword><keyword><style  face="normal" font="default" size="100%">Plants, Genetically Modified</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Array Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Protein Interaction Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteome</style></keyword><keyword><style  face="normal" font="default" size="100%">Proteomics</style></keyword><keyword><style  face="normal" font="default" size="100%">RNA, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Tandem Mass Spectrometry</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2012 May 1</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">109</style></volume><pages><style face="normal" font="default" size="100%">6811-8</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Because proteins are the major functional components of cells, knowledge of their cellular localization is crucial to gaining an understanding of the biology of multicellular organisms. We have generated a protein expression map of the Arabidopsis root providing the identity and cell type-specific localization of nearly 2,000 proteins. Grouping proteins into functional categories revealed unique cellular functions and identified cell type-specific biomarkers. Cellular colocalization provided support for numerous protein-protein interactions. With a binary comparison, we found that RNA and protein expression profiles are weakly correlated. We then performed peak integration at cell type-specific resolution and found an improved correlation with transcriptome data using continuous values. We performed GeLC-MS/MS (in-gel tryptic digestion followed by liquid chromatography-tandem mass spectrometry) proteomic experiments on mutants with ectopic and no root hairs, providing complementary proteomic data. Finally, among our root hair-specific proteins we identified two unique regulators of root hair development.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">18</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%">Brady, Siobhan M</style></author><author><style face="normal" font="default" size="100%">Zhang, Lifang</style></author><author><style face="normal" font="default" size="100%">Megraw, Molly</style></author><author><style face="normal" font="default" size="100%">Martinez, Natalia J</style></author><author><style face="normal" font="default" size="100%">Jiang, Eric</style></author><author><style face="normal" font="default" size="100%">Yi, Charles S</style></author><author><style face="normal" font="default" size="100%">Liu, Weilin</style></author><author><style face="normal" font="default" size="100%">Zeng, Anna</style></author><author><style face="normal" font="default" size="100%">Taylor-Teeples, Mallorie</style></author><author><style face="normal" font="default" size="100%">Kim, Dahae</style></author><author><style face="normal" font="default" size="100%">Ahnert, Sebastian</style></author><author><style face="normal" font="default" size="100%">Ohler, Uwe</style></author><author><style face="normal" font="default" size="100%">Ware, Doreen</style></author><author><style face="normal" font="default" size="100%">Walhout, Albertha J M</style></author><author><style face="normal" font="default" size="100%">Benfey, Philip N</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A stele-enriched gene regulatory network in the Arabidopsis root.</style></title><secondary-title><style face="normal" font="default" size="100%">Mol Syst Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mol. Syst. Biol.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Arabidopsis</style></keyword><keyword><style  face="normal" font="default" size="100%">Arabidopsis Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</style></keyword><keyword><style  face="normal" font="default" size="100%">MicroRNAs</style></keyword><keyword><style  face="normal" font="default" size="100%">Plant Roots</style></keyword><keyword><style  face="normal" font="default" size="100%">Reproducibility of Results</style></keyword><keyword><style  face="normal" font="default" size="100%">Systems Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription Factors</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%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011 Jan 18</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">459</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&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%">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%">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>