Inferring data-specific micro-RNA function through the joint ranking of micro-RNA and pathways from matched micro-RNA and gene expression data

In practice, identifying and interpreting the functional impacts of the regulatory relationships between micro-RNA and messenger-RNA is non-trivial. The sheer scale of possible micro-RNA and messenger-RNA interactions can make the interpretation of results difficult. We propose a supervised framewor...

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Bibliographic Details
Published in:Bioinformatics 2015-09, Vol.31 (17), p.2822-2828
Main Authors: Patrick, Ellis, Buckley, Michael, Müller, Samuel, Lin, David M, Yang, Jean Y H
Format: Article
Language:English
Subjects:
Algorithms
Bioinformatics
Computational Biology - methods
Control
Databases, Factual
Gene expression
Gene Expression Profiling - methods
Gene Expression Regulation
Gene Regulatory Networks
Humans
MicroRNAs - genetics
MicroRNAs - metabolism
Online
Original Papers
Packages
Pathways
Ranking
RNA, Messenger - genetics
RNA, Messenger - metabolism
Software
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Summary:In practice, identifying and interpreting the functional impacts of the regulatory relationships between micro-RNA and messenger-RNA is non-trivial. The sheer scale of possible micro-RNA and messenger-RNA interactions can make the interpretation of results difficult. We propose a supervised framework, pMim, built upon concepts of significance combination, for jointly ranking regulatory micro-RNA and their potential functional impacts with respect to a condition of interest. Here, pMim directly tests if a micro-RNA is differentially expressed and if its predicted targets, which lie in a common biological pathway, have changed in the opposite direction. We leverage the information within existing micro-RNA target and pathway databases to stabilize the estimation and annotation of micro-RNA regulation making our approach suitable for datasets with small sample sizes. In addition to outputting meaningful and interpretable results, we demonstrate in a variety of datasets that the micro-RNA identified by pMim, in comparison to simpler existing approaches, are also more concordant with what is described in the literature. This framework is implemented as an R function, pMim, in the package sydSeq available from http://www.ellispatrick.com/r-packages. jean.yang@sydney.edu.au Supplementary data are available at Bioinformatics online.
ISSN:1367-4803
1367-4811
1460-2059
DOI:10.1093/bioinformatics/btv220