Your browser has javascript turned off or blocked. This will lead to some parts of our website to not work properly or at all. Turn on javascript for best performance.

The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

ludc webb

Harry Björkbacka

Researcher

ludc webb

Detecting microRNA activity from gene expression data

Author

  • Stephen F. Madden
  • Susan B. Carpenter
  • Ian B. Jeffery
  • Harry Björkbacka
  • Katherine A. Fitzgerald
  • Luke A. O'Neill
  • Desmond G. Higgins

Summary, in English

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.

Department/s

  • Cardiovascular Research - Immunity and Atherosclerosis
  • EXODIAB: Excellence in Diabetes Research in Sweden

Publishing year

2010

Language

English

Publication/Series

BMC Bioinformatics

Volume

11

Document type

Journal article

Publisher

BioMed Central (BMC)

Topic

  • Bioinformatics and Systems Biology

Status

Published

Research group

  • Cardiovascular Research - Immunity and Atherosclerosis

ISBN/ISSN/Other

  • ISSN: 1471-2105