Detecting microRNA activity from gene expression data

BMC Bioinformatics. 2010 May 18;11:257. doi: 10.1186/1471-2105-11-257.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Databases, Genetic
  • Gene Expression*
  • Genomics / methods*
  • Humans
  • Mice
  • MicroRNAs / genetics*
  • MicroRNAs / metabolism*
  • Oligonucleotide Array Sequence Analysis / methods
  • RNA, Messenger / genetics
  • RNA, Messenger / metabolism

Substances

  • MicroRNAs
  • RNA, Messenger