02836nas a2200337   4500008004100000022001400041245008700055210006900142260001300211300001100224490000700235520183700242653002102079653002302100653000802123653003102131653001802162653001102180653003002191653002202221653001302243653002602256653003402282653002702316100001802343700002202361700002102383700001502404700003002419856004902449       2009                        eng d  a1088-905100aA transcription factor affinity-based code for mammalian transcription initiation.0 atranscription factor affinitybased code for mammalian transcript  c2009 Apr  a644-560 v193 a<p>The recent arrival of large-scale cap analysis of gene expression (CAGE) data sets in mammals provides a wealth of quantitative information on coding and noncoding RNA polymerase II transcription start sites (TSS). Genome-wide CAGE studies reveal that a large fraction of TSS exhibit peaks where the vast majority of associated tags map to a particular location ( approximately 45%), whereas other active regions contain a broader distribution of initiation events. The presence of a strong single peak suggests that transcription at these locations may be mediated by position-specific sequence features. We therefore propose a new model for single-peaked TSS based solely on known transcription factors (TFs) and their respective regions of positional enrichment. This probabilistic model leads to near-perfect classification results in cross-validation (auROC = 0.98), and performance in genomic scans demonstrates that TSS prediction with both high accuracy and spatial resolution is achievable for a specific but large subgroup of mammalian promoters. The interpretable model structure suggests a DNA code in which canonical sequence features such as TATA-box, Initiator, and GC content do play a significant role, but many additional TFs show distinct spatial biases with respect to TSS location and are important contributors to the accurate prediction of single-peak transcription initiation sites. The model structure also reveals that CAGE tag clusters distal from annotated gene starts have distinct characteristics compared to those close to gene 5&#39;-ends. Using this high-resolution single-peak model, we predict TSS for approximately 70% of mammalian microRNAs based on currently available data.</p><p><a href="http://megraw-dev.cgrb.oregonstate.edu/node/715">[Links to Tools and Supplementary Materials]</a></p>10aBase Composition10aDatabases, Genetic10aDNA10aGene Expression Regulation10aGenome, Human10aHumans10aPromoter Regions, Genetic10aRNA Polymerase II10aTATA Box10aTranscription Factors10aTranscription Initiation Site10aTranscription, Genetic1 aMegraw, Molly1 aPereira, Fernando1 aJensen, Shane, T1 aOhler, Uwe1 aHatzigeorgiou, Artemis, G  uhttps://megraw.cgrb.oregonstate.edu/node/32603231nas a2200709   4500008004100000022001400041245010600055210006900161260001600230300001100246490000800257520120600265653001801471653002001489653002401509653002101533653001101554653003001565653004301595653001801638653001101656653001401667653002101681653002201702653002101724653001901745653002201764100001501786700002101801700001801822700002601840700001901866700001401885700002001899700002501919700002301944700002001967700002601987700002102013700001702034700001802051700001502069700001602084700001602100700002402116700001802140700002402158700002902182700002002211700002502231700002402256700002202280700001802302700001802320700002002338700002202358700002302380700003002403700002002433700001902453856004902472       2008                        eng d  a1091-649000aGenomic and epigenetic alterations deregulate microRNA expression in human epithelial ovarian cancer.0 aGenomic and epigenetic alterations deregulate microRNA expressio  c2008 May 13  a7004-90 v1053 a<p>MicroRNAs (miRNAs) are an abundant class of small noncoding RNAs that function as negative gene regulators. miRNA deregulation is involved in the initiation and progression of human cancer; however, the underlying mechanism and its contributions to genome-wide transcriptional changes in cancer are still largely unknown. We studied miRNA deregulation in human epithelial ovarian cancer by integrative genomic approach, including miRNA microarray (n = 106), array-based comparative genomic hybridization (n = 109), cDNA microarray (n = 76), and tissue array (n = 504). miRNA expression is markedly down-regulated in malignant transformation and tumor progression. Genomic copy number loss and epigenetic silencing, respectively, may account for the down-regulation of approximately 15% and at least approximately 36% of miRNAs in advanced ovarian tumors and miRNA down-regulation contributes to a genome-wide transcriptional deregulation. Last, eight miRNAs located in the chromosome 14 miRNA cluster (Dlk1-Gtl2 domain) were identified as potential tumor suppressor genes. Therefore, our results suggest that miRNAs may offer new biomarkers and therapeutic targets in epithelial ovarian cancer.</p>10aDNA, Neoplasm10aDown-Regulation10aEpigenesis, Genetic10aEpithelial Cells10aFemale10aGene Expression Profiling10aGene Expression Regulation, Neoplastic10aGenome, Human10aHumans10aMicroRNAs10aNeoplasm Staging10aOvarian Neoplasms10aRibonuclease III10aRNA, Messenger10aSurvival Analysis1 aZhang, Lin1 aVolinia, Stefano1 aBonome, Tomas1 aCalin, George, Adrian1 aGreshock, Joel1 aYang, Nuo1 aLiu, Chang-Gong1 aGiannakakis, Antonis1 aAlexiou, Pangiotis1 aHasegawa, Kosei1 aJohnstone, Cameron, N1 aMegraw, Molly, S1 aAdams, Sarah1 aLassus, Heini1 aHuang, Jia1 aKaur, Sippy1 aLiang, Shun1 aSethupathy, Praveen1 aLeminen, Arto1 aSimossis, Victor, A1 aSandaltzopoulos, Raphael1 aNaomoto, Yoshio1 aKatsaros, Dionyssios1 aGimotty, Phyllis, A1 aDeMichele, Angela1 aHuang, Qihong1 aBützow, Ralf1 aRustgi, Anil, K1 aWeber, Barbara, L1 aBirrer, Michael, J1 aHatzigeorgiou, Artemis, G1 aCroce, Carlo, M1 aCoukos, George  uhttps://megraw.cgrb.oregonstate.edu/node/328