Diana microt web server elucidating microrna functions through target prediction Grannydatesnoregristrationfree

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The total predicted score of a mi RNA:target gene interaction is the weighted sum of conserved and unconserved MREs of a gene.We also provide a signal-to-noise ratio (SNR) and a precision score specific for each interaction that can be used as a helpful confidence estimation of the ‘correctness’ and the false positive rate of each predicted mi TG.Most of these programs are mainly based on sequence alignment of the mi RNA seed region (nucleotides 2–7 from the 5′-end of the mi RNA) to the 3′-UTR of candidate target genes leading to the identification of putative binding sites.Their specificity is usually increased by exploiting the commonly observed evolutionary conservation of the binding sites or by using additional features such as structural accessibility (4,5), nucleotide composition (6) as well as location of the binding sites within the 3′-UTR (7).To learn more or modify/prevent the use of cookies, see our Cookie Policy and Privacy Policy.Computational micro RNA (mi RNA) target prediction is one of the key means for deciphering the role of mi RNAs in development and disease.The server offers an efficient search engine allowing multiple gene nomenclatures or queries based on gene involvement in specific biological pathways.The analysis of predicted interactions is supported by significance evaluation measures, extensive linkage to several online biological resources and automated bibliographic searches in Pub Med.

Cases where a predicted interaction is registered as experimentally supported or predicted by other programs are also noted.

The server also supports prediction requests based on user-defined mi RNA sequences and is integrated in a platform with two further mi RNA functional analysis tools: , a mi RNA analysis based on differential expressed m RNA profiles.

The web server may be accessed through a search engine with several options.

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 mi RNA: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.

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