Detecting microRNA activity from gene expression data

Research output: Contribution to journalArticle

Abstract

Background: MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression by binding to the messenger RNA (mRNA) of protein coding genes. They control gene expression by either inhibiting translation or inducing mRNA degradation. A number of computational techniques have been developed to identify the targets of miRNAs. In this study we used predicted miRNA-gene interactions to analyse mRNA gene expression microarray data to predict miRNAs associated with particular diseases or conditions. Results: Here we combine correspondence analysis, between group analysis and co-inertia analysis (CIA) to determine which miRNAs are associated with differences in gene expression levels in microarray data sets. Using a database of miRNA target predictions from TargetScan, TargetScanS, PicTar4way PicTar5way, and miRanda and combining these data with gene expression levels from sets of microarrays, this method produces a ranked list of miRNAs associated with a specified split in samples. We applied this to three different microarray datasets, a papillary thyroid carcinoma dataset, an in-house dataset of lipopolysaccharide treated mouse macrophages, and a multi-tissue dataset. In each case we were able to identified miRNAs of biological importance. Conclusions: We describe a technique to integrate gene expression data and miRNA target predictions from multiple sources.

Details

Authors
  • Stephen F. Madden
  • Susan B. Carpenter
  • Ian B. Jeffery
  • Harry Björkbacka
  • Katherine A. Fitzgerald
  • Luke A. O'Neill
  • Desmond G. Higgins
Organisations
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Bioinformatics and Systems Biology
Original languageEnglish
JournalBMC Bioinformatics
Volume11
Publication statusPublished - 2010
Publication categoryResearch
Peer-reviewedYes