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Submitted by ChenLiang on Fri, 09/02/2016 - 21:59



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Computational microRNA (miRNA) target prediction is one of the key means for deciphering the role of miRNAs in development and disease. Here, we present the DIANA-microT web server as the user interface to the DIANA-microT 3.0 miRNA target prediction algorithm. The web server provides extensive information for predicted miRNA:target gene interactions with a user-friendly interface, providing extensive connectivity to online biological resources. Target gene and miRNA functions may be elucidated through automated bibliographic searches and functional information is accessible through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The web server offers links to nomenclature, sequence and protein databases, and users are facilitated by being able to search for targeted genes using different nomenclatures or functional features, such as the genes possible involvement in biological pathways. The target prediction algorithm supports parameters calculated individually for each miRNA:target gene interaction and provides a signal-to-noise ratio and a precision score that helps in the evaluation of the significance of the predicted results. Using a set of miRNA targets recently identified through the pSILAC method, the performance of several computational target prediction programs was assessed. DIANA-microT 3.0 achieved there with 66% the highest ratio of correctly predicted targets over all predicted targets. The DIANA-microT web server is freely available at[1]

MicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and disease.
DIANA-microT 3.0 is an algorithm for microRNA target prediction which is based on several parameters calculated individually for each microRNA and combines conserved and non-conserved microRNA recognition elements into a final prediction score, which correlates with protein production fold change. Specifically, for each predicted interaction the program reports a signal to noise ratio and a precision score which can be used as an indication of the false positive rate of the prediction.
Recently, several computational target prediction programs were benchmarked based on a set of microRNA target genes identified by the pSILAC method. In this assessment DIANA-microT 3.0 was found to achieve the highest precision among the most widely used microRNA target prediction programs reaching approximately 66%. The DIANA-microT 3.0 prediction results are available online in a user friendly web server at[2]

microRNAs (miRNAs) are small endogenous RNA molecules that are implicated in many biological processes through post-transcriptional regulation of gene expression. The DIANA-microT Web server provides a user-friendly interface for comprehensive computational analysis of miRNA targets in human and mouse. The server has now been extended to support predictions for two widely studied species: Drosophila melanogaster and Caenorhabditis elegans. In the updated version, the Web server enables the association of miRNAs to diseases through bibliographic analysis and provides insights for the potential involvement of miRNAs in biological processes. The nomenclature used to describe mature miRNAs along different miRBase versions has been extensively analyzed, and the naming history of each miRNA has been extracted. This enables the identification of miRNA publications regardless of possible nomenclature changes. User interaction has been further refined allowing users to save results that they wish to analyze further. A connection to the UCSC genome browser is now provided, enabling users to easily preview predicted binding sites in comparison to a wide array of genomic tracks, such as single nucleotide polymorphisms. The Web server is publicly accessible in[3]

MicroRNAs (miRNAs) are small endogenous RNA molecules that regulate gene expression through mRNA degradation and/or translation repression, affecting many biological processes. DIANA-microT web server ( is dedicated to miRNA target prediction/functional analysis, and it is being widely used from the scientific community, since its initial launch in 2009. DIANA-microT v5.0, the new version of the microT server, has been significantly enhanced with an improved target prediction algorithm, DIANA-microT-CDS. It has been updated to incorporate miRBase version 18 and Ensembl version 69. The in silico-predicted miRNA-gene interactions in Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans exceed 11 million in total. The web server was completely redesigned, to host a series of sophisticated workflows, which can be used directly from the on-line web interface, enabling users without the necessary bioinformatics infrastructure to perform advanced multi-step functional miRNA analyses. For instance, one available pipeline performs miRNA target prediction using different thresholds and meta-analysis statistics, followed by pathway enrichment analysis. DIANA-microT web server v5.0 also supports a complete integration with the Taverna Workflow Management System (WMS), using the in-house developed DIANA-Taverna Plug-in. This plug-in provides ready-to-use modules for miRNA target prediction and functional analysis, which can be used to form advanced high-throughput analysis pipelines.[4]

Experimental evidence has accumulated showing that microRNA (miRNA) binding sites within protein coding sequences (CDSs) are functional in controlling gene expression.
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.[5]


  1. DIANA-microT web server: elucidating microRNA functions through target prediction.,
    Maragkakis, M, Reczko M, Simossis V A., Alexiou P, Papadopoulos G L., Dalamagas T, Giannopoulos G, Goumas G, Koukis E, Kourtis K, et al.
    , Nucleic Acids Res, 2009 Jul, Volume 37, Issue Web Server issue, p.W273-6, (2009)
  2. Accurate microRNA target prediction correlates with protein repression levels.,
    Maragkakis, Manolis, Alexiou Panagiotis, Papadopoulos Giorgio L., Reczko Martin, Dalamagas Theodore, Giannopoulos George, Goumas George, Koukis Evangelos, Kourtis Kornilios, Simossis Victor A., et al.
    , BMC Bioinformatics, 2009, Volume 10, p.295, (2009)
  3. DIANA-microT Web server upgrade supports Fly and Worm miRNA target prediction and bibliographic miRNA to disease association.,
    Maragkakis, Manolis, Vergoulis Thanasis, Alexiou Panagiotis, Reczko Martin, Plomaritou Kyriaki, Gousis Mixail, Kourtis Kornilios, Koziris Nectarios, Dalamagas Theodore, and Hatzigeorgiou Artemis G.
    , Nucleic Acids Res, 2011 Jul, Volume 39, Issue Web Server issue, p.W145-8, (2011)
  4. DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows.,
    Paraskevopoulou, Maria D., Georgakilas Georgios, Kostoulas Nikos, Vlachos Ioannis S., Vergoulis Thanasis, Reczko Martin, Filippidis Christos, Dalamagas Theodore, and Hatzigeorgiou A G.
    , Nucleic Acids Res, 2013 Jul, Volume 41, Issue Web Server issue, p.W169-73, (2013)
  5. Functional microRNA targets in protein coding sequences.,
    Reczko, Martin, Maragkakis Manolis, Alexiou Panagiotis, Grosse Ivo, and Hatzigeorgiou Artemis G.
    , Bioinformatics, 2012 Mar 15, Volume 28, Issue 6, p.771-6, (2012)