@article {325, title = {MicroRNA promoter analysis.}, journal = {Methods Mol Biol}, volume = {592}, year = {2010}, month = {2010}, pages = {149-61}, abstract = {

In this chapter, we present a brief overview of current knowledge about the promoters of plant microRNAs (miRNAs), and provide a step-by-step guide for predicting plant miRNA promoter elements using known transcription factor binding motifs. The approach to promoter element prediction is based on a carefully constructed collection of Positional Weight Matrices (PWMs) for known transcription factors (TFs) in Arabidopsis. A key concept of the method is to use scoring thresholds for potential binding sites that are appropriate to each individual transcription factor. While the procedure can be applied to search for Transcription Factor Binding Sites (TFBSs) in any pol-II promoter region, it is particularly practical for the case of plant miRNA promoters where upstream sequence regions and binding sites are not readily available in existing databases. The majority of the material described in this chapter is available for download at http://microrna.gr.

[Link to Tools and Supplementary Materials]

}, keywords = {MicroRNAs, Promoter Regions, Genetic, Transcription Factors}, issn = {1940-6029}, doi = {10.1007/978-1-60327-005-2_11}, author = {Megraw, Molly and Hatzigeorgiou, Artemis G} } @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 {331, title = {MicroRNA promoter element discovery in Arabidopsis.}, journal = {RNA}, volume = {12}, year = {2006}, month = {2006 Sep}, pages = {1612-9}, abstract = {

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.

}, keywords = {Arabidopsis, Base Sequence, Binding Sites, Databases, Genetic, Feedback, Physiological, Genes, Plant, MicroRNAs, Promoter Regions, Genetic, TATA Box, Transcription Factors, Transcription Initiation Site}, issn = {1355-8382}, doi = {10.1261/rna.130506}, author = {Megraw, Molly and Baev, Vesselin and Rusinov, Ventsislav and Jensen, Shane T and Kalantidis, Kriton and Hatzigeorgiou, Artemis G} }