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Open Access Highly Accessed Methodology article

MMpred: functional miRNA – mRNA interaction analyses by miRNA expression prediction

Przemyslaw A Stempor12*, Michael Cauchi1 and Paul Wilson2

Author Affiliations

1 Cranfield Health, Cranfield University, Vincent Building, Cranfield, UK

2 Computational Biology, GlaxoSmithKline Medicine Research Centre, Gunnels Wood Road, Stevenage, UK

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BMC Genomics 2012, 13:620  doi:10.1186/1471-2164-13-620

Published: 14 November 2012

Abstract

Background

MicroRNA (miRNA) directed gene repression is an important mechanism of posttranscriptional regulation. Comprehensive analyses of how microRNA influence biological processes requires paired miRNA-mRNA expression datasets. However, a review of both GEO and ArrayExpress repositories revealed few such datasets, which was in stark contrast to the large number of messenger RNA (mRNA) only datasets. It is of interest that numerous primary miRNAs (precursors of microRNA) are known to be co-expressed with coding genes (host genes).

Results

We developed a miRNA-mRNA interaction analyses pipeline. The proposed solution is based on two miRNA expression prediction methods – a scaling function and a linear model. Additionally, miRNA-mRNA anti-correlation analyses are used to determine the most probable miRNA gene targets (i.e. the differentially expressed genes under the influence of up- or down-regulated microRNA). Both the consistency and accuracy of the prediction method is ensured by the application of stringent statistical methods. Finally, the predicted targets are subjected to functional enrichment analyses including GO, KEGG and DO, to better understand the predicted interactions.

Conclusions

The MMpred pipeline requires only mRNA expression data as input and is independent of third party miRNA target prediction methods. The method passed extensive numerical validation based on the binding energy between the mature miRNA and 3’ UTR region of the target gene. We report that MMpred is capable of generating results similar to that obtained using paired datasets. For the reported test cases we generated consistent output and predicted biological relationships that will help formulate further testable hypotheses.