<?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%">Gouthu, Satyanarayana</style></author><author><style face="normal" font="default" size="100%">O'Neil, Shawn T</style></author><author><style face="normal" font="default" size="100%">Di, Yanming</style></author><author><style face="normal" font="default" size="100%">Ansarolia, Mitra</style></author><author><style face="normal" font="default" size="100%">Megraw, Molly</style></author><author><style face="normal" font="default" size="100%">Deluc, Laurent G</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A comparative study of ripening among berries of the grape cluster reveals an altered transcriptional programme and enhanced ripening rate in delayed berries.</style></title><secondary-title><style face="normal" font="default" size="100%">J Exp Bot</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J. Exp. Bot.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Fruit</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, Plant</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Plant Growth Regulators</style></keyword><keyword><style  face="normal" font="default" size="100%">Time Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription, Genetic</style></keyword><keyword><style  face="normal" font="default" size="100%">Vitis</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 Nov</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">65</style></volume><pages><style face="normal" font="default" size="100%">5889-902</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Transcriptional studies in relation to fruit ripening generally aim to identify the transcriptional states associated with physiological ripening stages and the transcriptional changes between stages within the ripening programme. In non-climacteric fruits such as grape, all ripening-related genes involved in this programme have not been identified, mainly due to the lack of mutants for comparative transcriptomic studies. A feature in grape cluster ripening (Vitis vinifera cv. Pinot noir), where all berries do not initiate the ripening at the same time, was exploited to study their shifted ripening programmes in parallel. Berries that showed marked ripening state differences in a véraison-stage cluster (ripening onset) ultimately reached similar ripeness states toward maturity, indicating the flexibility of the ripening programme. The expression variance between these véraison-stage berry classes, where 11% of the genes were found to be differentially expressed, was reduced significantly toward maturity, resulting in the synchronization of their transcriptional states. Defined quantitative expression changes (transcriptional distances) not only existed between the véraison transitional stages, but also between the véraison to maturity stages, regardless of the berry class. It was observed that lagging berries complete their transcriptional programme in a shorter time through altered gene expressions and ripening-related hormone dynamics, and enhance the rate of physiological ripening progression. Finally, the reduction in expression variance of genes can identify new genes directly associated with ripening and also assess the relevance of gene activity to the phase of the ripening programme.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">20</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%">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%">Megraw, Molly</style></author><author><style face="normal" font="default" size="100%">Mukherjee, Sayan</style></author><author><style face="normal" font="default" size="100%">Ohler, Uwe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sustained-input switches for transcription factors and microRNAs are central building blocks of eukaryotic gene circuits.</style></title><secondary-title><style face="normal" font="default" size="100%">Genome Biol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Genome Biol.</style></alt-title></titles><keywords><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%">Arabidopsis</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology</style></keyword><keyword><style  face="normal" font="default" size="100%">Drosophila melanogaster</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Regulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Regulatory Networks</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%">Molecular Sequence Annotation</style></keyword><keyword><style  face="normal" font="default" size="100%">Nucleic Acid Conformation</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%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">R85</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;WaRSwap is a randomization algorithm that for the first time provides a practical network motif discovery method for large multi-layer networks, for example those that include transcription factors, microRNAs, and non-regulatory protein coding genes. The algorithm is applicable to systems with tens of thousands of genes, while accounting for critical aspects of biological networks, including self-loops, large hubs, and target rearrangements. We validate WaRSwap on a newly inferred regulatory network from Arabidopsis thaliana, and compare outcomes on published Drosophila and human networks. Specifically, sustained input switches are among the few over-represented circuits across this diverse set of eukaryotes.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">8</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%">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%">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>