MAGIA

MAGIA (miRNA and genes integrated analysis) is a novel web tool for the integrative analysis of target predictions, miRNA and gene expression data. MAGIA is divided into two parts: the query section allows the user to retrieve and browse updated miRNA target predictions computed with a number of different algorithms (PITA, miRanda and Target Scan) and Boolean combinations thereof. The analysis section comprises a multistep procedure for (i) direct integration through different functional measures (parametric and non-parametric correlation indexes, a variational Bayesian model, mutual information and a meta-analysis approach based on P-value combination) of mRNA and miRNA expression data, (ii) construction of bipartite regulatory network of the best miRNA and mRNA putative interactions and (iii) retrieval of information available in several public databases of genes, miRNAs and diseases and via scientific literature text-mining. MAGIA is freely available for Academic users at http://gencomp.bio.unipd.it/magia.[1]

MAGIA(2) (http://gencomp.bio.unipd.it/magia2) is an update, extension and evolution of the MAGIA web tool. It is dedicated to the integrated analysis of in silico target prediction, microRNA (miRNA) and gene expression data for the reconstruction of post-transcriptional regulatory networks. miRNAs are fundamental post-transcriptional regulators of several key biological and pathological processes. As miRNAs act prevalently through target degradation, their expression profiles are expected to be inversely correlated to those of the target genes. Low specificity of target prediction algorithms makes integration approaches an interesting solution for target prediction refinement. MAGIA(2) performs this integrative approach supporting different association measures, multiple organisms and almost all target predictions algorithms. Nevertheless, miRNAs activity should be viewed as part of a more complex scenario where regulatory elements and their interactors generate a highly connected network and where gene expression profiles are the result of different levels of regulation. The updated MAGIA(2) tries to dissect this complexity by reconstructing mixed regulatory circuits involving either miRNA or transcription factor (TF) as regulators. Two types of circuits are identified: (i) a TF that regulates both a miRNA and its target and (ii) a miRNA that regulates both a TF and its target.[2]

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