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This article is part of the supplement: Selected articles from the IEEE International Conference on Bioinformatics and Biomedicine 2012: Bioinformatics

Open Access Research

Using context-specific effect of miRNAs to identify functional associations between miRNAs and gene signatures

Mohammed Alshalalfa12* and Reda Alhajj13

Author Affiliations

1 Department of Computer Science, University of Calgary, Calgary, Alberta, Canada

2 Biotechnology Research Center, Palestine Polytechnic University, Hebron, Palestine

3 Computer Science Department, Global University, Beirut, Lebanon

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BMC Bioinformatics 2013, 14(Suppl 12):S1  doi:10.1186/1471-2105-14-S12-S1

Published: 24 September 2013

Abstract

Background

MicroRNAs are a class of short regulatory RNAs that act as post-transcriptional fine-tune regulators of a large host of genes that play key roles in many cellular processes and signaling pathways. A useful step for understanding their functional role is characterizing their influence on the protein context of the targets. Using miRNA context-specific influence as a functional signature is promising to identify functional associations between miRNAs and other gene signatures, and thus advance our understanding of miRNA mode of action.

Results

In the current study we utilized the power of regularized regression models to construct functional associations between gene signatures. Genes that are influenced by miRNAs directly(computational miRNA target prediction) or indirectly (protein partners of direct targets) are defined as functional miRNA gene signature. The combined direct and indirect miRNA influence is defined as context-specific effects of miRNAs, and is used to identify regulatory effects of miRNAs on curated gene signatures. Elastic-net regression was used to build functional associations between context-specific effect of miRNAs and other gene signatures (disease, pathway signatures) by identifying miRNAs whose targets are enriched in gene lists. As a proof of concept, elastic-net regression was applied on lists of genes downregulated upon pre-miRNA transfection, and successfully identified the treated miRNA. This model was then extended to construct functional relationships between miRNAs and disease and pathway gene lists. Integrating context-specific effects of miRNAs on a protein network reveals more significant miRNA enrichment in prostate gene signatures compared to miRNA direct targets. The model identified novel list of miRNAs that are associated with prostate clinical variables.

Conclusions

Elastic-net regression is used as a model to construct functional associations between miRNA signatures and other gene signatures. Defining miRNA context-specific functional gene signature by integrating the downstream effect of miRNAs demonstrates better performance compared to the miRNA signature alone (direct targets). miRNA functional signatures can greatly facilitate miRNA research to uncover new functional associations between miRNAs and diseases, drugs or pathways.