@article {324, title = {miRGen 2.0: a database of microRNA genomic information and regulation.}, journal = {Nucleic Acids Res}, volume = {38}, year = {2010}, month = {2010 Jan}, pages = {D137-41}, abstract = {

MicroRNAs are small, non-protein coding RNA molecules known to regulate the expression of genes by binding to the 3\&$\#$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/.

}, keywords = {3{\textquoteright} Untranslated Regions, Algorithms, Animals, Cell Line, Tumor, Computational Biology, Databases, Genetic, Databases, Nucleic Acid, Humans, Information Storage and Retrieval, Internet, Mice, MicroRNAs, Polymorphism, Single Nucleotide, Software, Transcription Factors}, issn = {1362-4962}, doi = {10.1093/nar/gkp888}, author = {Alexiou, Panagiotis and Vergoulis, Thanasis and Gleditzsch, Martin and Prekas, George and Dalamagas, Theodore and Megraw, Molly and Grosse, Ivo and Sellis, Timos and Hatzigeorgiou, Artemis G} } @article {326, title = {A transcription factor affinity-based code for mammalian transcription initiation.}, journal = {Genome Res}, volume = {19}, year = {2009}, month = {2009 Apr}, pages = {644-56}, abstract = {

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.

[Links to Tools and Supplementary Materials]

}, keywords = {Base Composition, Databases, Genetic, DNA, Gene Expression Regulation, Genome, Human, Humans, Promoter Regions, Genetic, RNA Polymerase II, TATA Box, Transcription Factors, Transcription Initiation Site, Transcription, Genetic}, issn = {1088-9051}, doi = {10.1101/gr.085449.108}, author = {Megraw, Molly and Pereira, Fernando and Jensen, Shane T and Ohler, Uwe and Hatzigeorgiou, Artemis G} } @article {327, title = {Frequency and fate of microRNA editing in human brain.}, journal = {Nucleic Acids Res}, volume = {36}, year = {2008}, month = {2008 Sep}, pages = {5270-80}, abstract = {

Primary transcripts of certain microRNA (miRNA) genes (pri-miRNAs) are subject to RNA editing that converts adenosine to inosine (A--\>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--\>I editing and, thus, miRNA editing could have a large impact on the miRNA-mediated gene silencing.

}, keywords = {Adenosine, Adenosine Deaminase, Animals, Base Sequence, Brain, Humans, Inosine, Mice, MicroRNAs, Molecular Sequence Data, RNA Editing, RNA Precursors, RNA Processing, Post-Transcriptional, RNA-Binding Proteins}, issn = {1362-4962}, doi = {10.1093/nar/gkn479}, author = {Kawahara, Yukio and Megraw, Molly and Kreider, Edward and Iizasa, Hisashi and Valente, Louis and Hatzigeorgiou, Artemis G and Nishikura, Kazuko} } @article {328, title = {Genomic and epigenetic alterations deregulate microRNA expression in human epithelial ovarian cancer.}, journal = {Proc Natl Acad Sci U S A}, volume = {105}, year = {2008}, month = {2008 May 13}, pages = {7004-9}, abstract = {

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.

}, keywords = {DNA, Neoplasm, Down-Regulation, Epigenesis, Genetic, Epithelial Cells, Female, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Genome, Human, Humans, MicroRNAs, Neoplasm Staging, Ovarian Neoplasms, Ribonuclease III, RNA, Messenger, Survival Analysis}, issn = {1091-6490}, doi = {10.1073/pnas.0801615105}, author = {Zhang, Lin and Volinia, Stefano and Bonome, Tomas and Calin, George Adrian and Greshock, Joel and Yang, Nuo and Liu, Chang-Gong and Giannakakis, Antonis and Alexiou, Pangiotis and Hasegawa, Kosei and Johnstone, Cameron N and Megraw, Molly S and Adams, Sarah and Lassus, Heini and Huang, Jia and Kaur, Sippy and Liang, Shun and Sethupathy, Praveen and Leminen, Arto and Simossis, Victor A and Sandaltzopoulos, Raphael and Naomoto, Yoshio and Katsaros, Dionyssios and Gimotty, Phyllis A and DeMichele, Angela and Huang, Qihong and B{\"u}tzow, Ralf and Rustgi, Anil K and Weber, Barbara L and Birrer, Michael J and Hatzigeorgiou, Artemis G and Croce, Carlo M and Coukos, George} } @article {329, title = {miRGen: a database for the study of animal microRNA genomic organization and function.}, journal = {Nucleic Acids Res}, volume = {35}, year = {2007}, month = {2007 Jan}, pages = {D149-55}, abstract = {

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.

}, keywords = {Animals, Data Interpretation, Statistical, Databases, Nucleic Acid, Genomics, Humans, Internet, Mice, MicroRNAs, Rats, User-Computer Interface}, issn = {1362-4962}, doi = {10.1093/nar/gkl904}, author = {Megraw, Molly and Sethupathy, Praveen and Corda, Benoit and Hatzigeorgiou, Artemis G} } @article {330, title = {A guide through present computational approaches for the identification of mammalian microRNA targets.}, journal = {Nat Methods}, volume = {3}, year = {2006}, month = {2006 Nov}, pages = {881-6}, abstract = {

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.

}, keywords = {3{\textquoteright} Untranslated Regions, 5{\textquoteright} Untranslated Regions, Animals, Computational Biology, Gene Targeting, Humans, MicroRNAs, Predictive Value of Tests, RNA, Messenger, Sensitivity and Specificity, Software}, issn = {1548-7091}, doi = {10.1038/nmeth954}, author = {Sethupathy, Praveen and Megraw, Molly and Hatzigeorgiou, Artemis G} }