%0 Journal Article %J Genome Res %D 2009 %T A transcription factor affinity-based code for mammalian transcription initiation. %A Megraw, Molly %A Pereira, Fernando %A Jensen, Shane T %A Ohler, Uwe %A Hatzigeorgiou, Artemis G %K Base Composition %K Databases, Genetic %K DNA %K Gene Expression Regulation %K Genome, Human %K Humans %K Promoter Regions, Genetic %K RNA Polymerase II %K TATA Box %K Transcription Factors %K Transcription Initiation Site %K Transcription, Genetic %X

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'-ends. Using this high-resolution single-peak model, we predict TSS for approximately 70% of mammalian microRNAs based on currently available data.

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%B Genome Res %V 19 %P 644-56 %8 2009 Apr %G eng %N 4 %R 10.1101/gr.085449.108 %0 Journal Article %J Proc Natl Acad Sci U S A %D 2008 %T Genomic and epigenetic alterations deregulate microRNA expression in human epithelial ovarian cancer. %A Zhang, Lin %A Volinia, Stefano %A Bonome, Tomas %A Calin, George Adrian %A Greshock, Joel %A Yang, Nuo %A Liu, Chang-Gong %A Giannakakis, Antonis %A Alexiou, Pangiotis %A Hasegawa, Kosei %A Johnstone, Cameron N %A Megraw, Molly S %A Adams, Sarah %A Lassus, Heini %A Huang, Jia %A Kaur, Sippy %A Liang, Shun %A Sethupathy, Praveen %A Leminen, Arto %A Simossis, Victor A %A Sandaltzopoulos, Raphael %A Naomoto, Yoshio %A Katsaros, Dionyssios %A Gimotty, Phyllis A %A DeMichele, Angela %A Huang, Qihong %A Bützow, Ralf %A Rustgi, Anil K %A Weber, Barbara L %A Birrer, Michael J %A Hatzigeorgiou, Artemis G %A Croce, Carlo M %A Coukos, George %K DNA, Neoplasm %K Down-Regulation %K Epigenesis, Genetic %K Epithelial Cells %K Female %K Gene Expression Profiling %K Gene Expression Regulation, Neoplastic %K Genome, Human %K Humans %K MicroRNAs %K Neoplasm Staging %K Ovarian Neoplasms %K Ribonuclease III %K RNA, Messenger %K Survival Analysis %X

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.

%B Proc Natl Acad Sci U S A %V 105 %P 7004-9 %8 2008 May 13 %G eng %N 19 %R 10.1073/pnas.0801615105