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Reczko M.,Institute of Molecular Oncology | Reczko M.,Synaptic Ltd. | Maragkakis M.,Institute of Molecular Oncology | Maragkakis M.,Martin Luther University of Halle Wittenberg | And 6 more authors.
Frontiers in Genetics | Year: 2012

MicroRNAs (miRNAs) are a class of small regulatory genes regulating gene expression by targeting messenger RNA. Though computational methods for miRNA target prediction are the prevailing means to analyze their function, they still miss a large fraction of the targeted genes and additionally predict a large number of false positives. Here we introduce a novel algorithm called DIANA-microT-ANN which combines multiple novel target site features through an artificial neural network (ANN) and is trained using recently published high-throughput data measuring the change of protein levels after miRNA overexpression, providing positive and negative targeting examples. The features characterizing each miRNA recognition element include binding structure, conservation level, and a specific profile of structural accessibility. The ANN is trained to integrate the features of each recognition element along the 3′untranslated region into a targeting score, reproducing the relative repression fold change of the protein. Tested on two different sets the algorithm outperforms other widely used algorithms and also predicts a significant number of unique and reliable targets not predicted by the other methods. For 542 human miRNAs DIANA-microT-ANN predicts 120000 targets not provided by TargetScan 5.0. The algorithm is freely available at http://microrna.gr/microT-ANN. © 2012 Reczko, Maragkakis, Alexiou, Papadopoulos and Hatzigeorgiou. Source


Reczko M.,Institute of Molecular Oncology | Reczko M.,Synaptic Ltd. | Maragkakis M.,Institute of Molecular Oncology | Maragkakis M.,Martin Luther University of Halle Wittenberg | And 5 more authors.
Bioinformatics | Year: 2012

Motivation: Experimental evidence has accumulated showing that microRNA (miRNA) binding sites within protein coding sequences (CDSs) are functional in controlling gene expression. Results: Here we report a computational analysis of such miRNA target sites, based on features extracted from existing mammalian high-throughput immunoprecipitation and sequencing data. The analysis is performed independently for the CDS and the 3 '-untranslated regions (3 '-UTRs) and reveals different sets of features and models for the two regions. The two models are combined into a novel computational model for miRNA target genes, DIANA-microT-CDS, which achieves higher sensitivity compared with other popular programs and the model that uses only the 3 '-UTR target sites. Further analysis indicates that genes with shorter 3 '-UTRs are preferentially targeted in the CDS, suggesting that evolutionary selection might favor additional sites on the CDS in cases where there is restricted space on the 3′-UTR. © The Author 2012. Published by Oxford University Press. All rights reserved. Source


Paraskevopoulou M.D.,Biomedical science Research Center Alexander Fleming | Paraskevopoulou M.D.,National and Kapodistrian University of Athens | Georgakilas G.,Biomedical science Research Center Alexander Fleming | Georgakilas G.,University of Thessaly | And 9 more authors.
Nucleic Acids Research | Year: 2013

Recently, the attention of the research community has been focused on long non-coding RNAs (IncRNAs) and their physiological/pathological implications. As the number of experiments increase in a rapid rate and transcriptional units are better annotated, databases indexing IncRNA properties and function gradually become essential tools to this process. Aim of DIANA-LncBase (www. microrna.gr/LncBase) is to reinforce researchers' attempts and unravel microRNA (miRNA)-IncRNA putative functional interactions. This study provides, for the first time, a comprehensive annotation of miRNA targets on IncRNAs. DIANA-LncBase hosts transcriptome-wide experimentally verified and computationally predicted miRNA recognition elements (MREs) on human and mouse IncRNAs. The analysis performed includes an integration of most of the available IncRNA resources, relevant high-throughput HITS-CLIP and PAR-CLIP experimental data as well as state-of-the-art in silico target predictions. The experimentally supported entries available in DIANA-LncBase correspond to >5000 interactions, while the computationally predicted interactions exceed 10 million. DIANA-LncBase hosts detailed information for each miRNA-IncRNA pair, such as external links, graphic plots of transcripts' genomic location, representation of the binding sites, IncRNA tissue expression as well as MREs conservation and prediction scores. © The Author(s) 2012. Source


Riback J.,Institute of Molecular Oncology | Hatzigeorgiou A.G.,Institute of Molecular Oncology | Hatzigeorgiou A.G.,University of Pennsylvania | Reczko M.,Institute of Molecular Oncology | Reczko M.,Synaptic Ltd.
Theoretical Chemistry Accounts | Year: 2010

MicroRNAs (miRNAs) have been shown to play an important regulatory role in plants and animals. A large number of known and novel miRNAs can be uncovered from next-generation sequencing (NGS) experiments that measure the complement of a given cell's small RNAs under various conditions. Here, we present an algorithm based on radial basis functions for the identification of potential miRNA precursor structures. Computationally assessing features of known human miRNA precursors, such as structural linearity, normalized minimum folding energy, and nucleotide pairing frequencies, this model robustly differentiates between miRNAs and other types of non-coding RNAs. Without relying on cross species conservation, the method also identifies non-conserved precursors and achieves high sensitivity. The presented method can be used routinely for the identification of known and novel miRNAs present in NGS experiments. © Springer-Verlag 2009. Source


Tzamali E.,University of Crete | Tzamali E.,Foundation for Research and Technology Hellas | Poirazi P.,Foundation for Research and Technology Hellas | Tollis I.G.,University of Crete | And 3 more authors.
BMC Systems Biology | Year: 2011

Background: Metabolic interactions involve the exchange of metabolic products among microbial species. Most microbes live in communities and usually rely on metabolic interactions to increase their supply for nutrients and better exploit a given environment. Constraint-based models have successfully analyzed cellular metabolism and described genotype-phenotype relations. However, there are only a few studies of genome-scale multi-species interactions. Based on genome-scale approaches, we present a graph-theoretic approach together with a metabolic model in order to explore the metabolic variability among bacterial strains and identify and describe metabolically interacting strain communities in a batch culture consisting of two or more strains. We demonstrate the applicability of our approach to the bacterium E. coli across different single-carbon-source conditions.Results: A different diversity graph is constructed for each growth condition. The graph-theoretic properties of the constructed graphs reflect the inherent high metabolic redundancy of the cell to single-gene knockouts, reveal mutant-hubs of unique metabolic capabilities regarding by-production, demonstrate consistent metabolic behaviors across conditions and show an evolutionary difficulty towards the establishment of polymorphism, while suggesting that communities consisting of strains specifically adapted to a given condition are more likely to evolve. We reveal several strain communities of improved growth relative to corresponding monocultures, even though strain communities are not modeled to operate towards a collective goal, such as the community growth and we identify the range of metabolites that are exchanged in these batch co-cultures. Conclusions: This study provides a genome-scale description of the metabolic variability regarding by-production among E. coli strains under different conditions and shows how metabolic differences can be used to identify metabolically interacting strain communities. This work also extends the existing stoichiometric models in order to describe batch co-cultures and provides the extent of metabolic interactions in a strain community revealing their importance for growth. © 2011 Tzamali et al; licensee BioMed Central Ltd. Source

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