Journal Article

mimiRNA: a microRNA expression profiler and classification resource designed to identify functional correlations between microRNAs and their targets

William Ritchie, Stephane Flamant and John E. J. Rasko

in Bioinformatics

Volume 26, issue 2, pages 223-227
Published in print January 2010 | ISSN: 1367-4803
Published online November 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp649
mimiRNA: a microRNA expression profiler and classification resource designed to identify functional correlations between microRNAs and their targets

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Motivation: microRNAs (miRNAs) are short non-coding RNAs that regulate gene expression by inhibiting target mRNA genes. Their tissue- and disease-specific expression patterns have immense therapeutic and diagnostic potential. To understand these patterns, a reliable compilation of miRNA and mRNA expression data is required to compare multiple tissue types. Moreover, with the appropriate statistical tools, such a resource could be interrogated to discover functionally related miRNA–mRNA pairs.

Results:We have developed mimiRNA, an online resource that integrates expression data from 1483 samples and permits visualization of the expression of 635 human miRNAs across 188 different tissues or cell types. mimiRNA incorporates a novel sample classification algorithm, ExParser, that groups identical miRNA or mRNA experiments from separate sources. This enables mimiRNA to provide reliable expression profiles and to discover functional relations between miRNAs and mRNAs such as miRNA targets. Additionally, mimiRNA incorporates a decision tree algorithm to discover distinguishing miRNA features between two tissue or cell types. We validate the efficacy of our resource on independent experimental data and through biologically relevant analyses.

Availability: http://mimirna.centenary.org.au

Contact: j.rasko@centenary.org.au

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  3169 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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