Guide Magia (Italian Edition)

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Mutual information is a measure of the mutual dependence of two variables. Intuitively, it captures the information that a variable X a gene expression profile and a variable Y a miRNA expression profile share: how much the knowledge of one of these variables reduces our uncertainty about the other. Thus, the mutual information can be interpreted as a generalized measure of correlation, analogous to the Pearson correlation, but sensitive to any functional relationship, not just to linear dependencies.

There are several possible strategies for the reliable estimation of the mutual information in case of finite data, each of them characterized by a systematic error due to the finite size sample [see 23 for a review]. Mutual information, identifying any functional relationship between miRNA and gene expression profiles, does not allow the identification of the sign of such relationship. An estimate of such posterior probability is calculated through an EM algorithm. The meta-analysis approach is suggested only in the case of non-matched biological samples. Given the diverse nature and number of samples between miRNA and gene profiles, neither correlation coefficients nor mutual information or posterior probabilities can be computed.

MAGIA adopts in their place a meta-analyses approach based on P -value combination allowing, unlike other web tools, the presence of more than two groups. In particular, in the case of a two classes experimental design, P- values of over-expressed miRNAs e. In the case of more than two classes the tool combines P- values derived from miRNAs, genes and from the test on Spearman correlation coefficient computed between vectors representing the average expression values of miRNAs and genes within each class.

MAGIA reports results in a web page containing different sections. For the top most probable functional miRNA—mRNA interactions according to the association measure selected by the user, the interactive bipartite regulatory network obtained through the analysis is reported along with the corresponding browsable table of relationships. It gives a hyperlink allowing the functional enrichment analysis by the DAVID web tool 27 on the desired number of target genes. The tool also provides the complete list of the predicted interactions, ranked by statistical significance computed from the integrated expression data analysis.

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Such information is given as HTML tables and as two Cytoscape-compliant flat files for network reconstruction. The complete list of significant interactions can be downloaded as a tab-delimited text file that can be imported into Excel or Cytoscape, to allow further processing. ALL is a heterogeneous disease comprising several subentities that differ for both immunophenotypic and molecular characteristics. In particular, T-lineage and B-lineage harboring specific molecular lesions have been considered by expression analyses.


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Figure 2 shows, for the top miRNA—target relationships most supported by expression data, the bipartite network and the corresponding list with hyperlinks to mirBase, EntrezGene, PubFocus, EbiMed and mir2disease, whereas for all predicted interactions, a link to an html table and to a tab delimited flat file Cytoscape compliant are given, as well as the link to the DAVID annotation tool for a number of interactions that can be defined by the user default is set to The summary page reports the regulatory network corresponding to the relations most supported by expression data and the corresponding details, as genes and miRNAs involved, with links to databases and text-mining tools.

This is also the entry point to reach gene- or miRNA-centered pages, to carry out functional enrichment analysis and to download complete results. In this example, the top interactions include a total number of 81 different miRNAs and of different genes. Pathways enrichment analysis, conducted on target genes and aiming at clarifying the role of miRNAs in terms of cell activities under post-transcriptional regulation, leads to highly relevant and interesting results: chronic myeloid leukemia is the KEGG most enriched pathway according to DAVID, followed by Wnt-signaling pathway, pancreatic cancer and ubiquitin mediated proteolysis.

On the other hand, the Wnt family of secreted glycoproteins regulate early B cell growth and survival 34 and aberrant activation of the Wnt-signaling pathway has major oncogenic effects Finally, the ubiquitin pathway plays a central role in the regulation of cell growth and cell proliferation controlling the abundance of key cell-cycle proteins. Increasing evidence indicates that unscheduled proteolysis of many cell-cycle regulators contributes significantly to tumorigenesis and is indeed found in many types of human cancers Recently RALA and RALB have shown to collaborate to maintain tumorigenicity through regulation of both proliferation and survival 37 while both let-7d and let-7c have been shown to be involved in the human acute promyelocytic leukemia Indeed, repeating the sample analysis with the same expression data and settings indicated above, but only for the 12 miRNAs reportedly differentially expressed across samples 22 an interaction biologically relevant and validated according to Diana Tarbase and miRecords , regarding hsa-let-7e and HMGA2 high mobility group AT-hook 2 gene is indicated by MAGIA at the first ranked position.

While a complete investigation of biological relevance of all interactions reported by MAGIA is beyond the scope of this work, they validate the MAGIA integrative approach, the usefulness of the display of results and the discovery power of data analysis with this tool.


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  7. The integrative analysis of target prediction, miRNA and gene expression profiles is not straightforward for most experimental researchers, not only for problems regarding miRNA and targets annotations, but also for the many-to-many nature of predicted relationships to be considered and the extensive time requirements of computations.

    However, there is an increasing amount of experimental studies aiming at gaining molecular understanding of biological processes or diseases from the computation and the visualization of high-throughput systems biology analyses results. Available tools are not adequate to the rapidly increasing amount of matched miRNA—gene profiles, the analysis of which could gain a remarkable advantage from target predictions and miRNA—gene expression profiles integration.

    In this context, MAGIA is a useful, timely and easy-to-use web tool that will facilitate users in the investigation of the post-transcriptional regulatory networks and in the discovery of biologically relevant regulatory circuits. Funding for open access charges: University of Padova. Oxford University Press is a department of the University of Oxford.

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