A bioinformatics tool for linking gene expression profiling results with public databases of microRNA target predictions

  1. Chad J. Creighton1,2,
  2. Ankur K. Nagaraja3,4,
  3. Samir M. Hanash5,
  4. Martin M. Matzuk3,4,6, and
  5. Preethi H. Gunaratne3,7
  1. 1The Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, Texas 77030, USA
  2. 2Department of Medicine, Baylor College of Medicine, Houston, Texas 77030, USA
  3. 3Department of Pathology, Baylor College of Medicine, Houston, Texas 77030, USA
  4. 4Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
  5. 5Molecular Diagnostics, Fred Hutchinson Cancer Research Center, Seattle, Wshington 98109, USA
  6. 6Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas 77030, USA
  7. 7Department of Biology and Biochemistry, University of Houston, Houston, Texas 77204, USA

Abstract

MicroRNAs are short (∼22 nucleotides) noncoding RNAs that regulate the stability and translation of mRNA targets. A number of computational algorithms have been developed to help predict which microRNAs are likely to regulate which genes. Gene expression profiling of biological systems where microRNAs might be active can yield hundreds of differentially expressed genes. The commonly used public microRNA target prediction databases facilitate gene-by-gene searches. However, integration of microRNA–mRNA target predictions with gene expression data on a large scale using these databases is currently cumbersome and time consuming for many researchers. We have developed a desktop software application which, for a given target prediction database, retrieves all microRNA:mRNA functional pairs represented by an experimentally derived set of genes. Furthermore, for each microRNA, the software computes an enrichment statistic for overrepresentation of predicted targets within the gene set, which could help to implicate roles for specific microRNAs and microRNA-regulated genes in the system under study. Currently, the software supports searching of results from PicTar, TargetScan, and miRanda algorithms. In addition, the software can accept any user-defined set of gene-to-class associations for searching, which can include the results of other target prediction algorithms, as well as gene annotation or gene-to-pathway associations. A search (using our software) of genes transcriptionally regulated in vitro by estrogen in breast cancer uncovered numerous targeting associations for specific microRNAs—above what could be observed in randomly generated gene lists—suggesting a role for microRNAs in mediating the estrogen response. The software and Excel VBA source code are freely available at http://sigterms.sourceforge.net.

Keywords

Footnotes

  • Reprint requests to: Chad J. Creighton, Dan L. Duncan Cancer Center Division of Biostatistics, Baylor College of Medicine, One Baylor Plaza M.S. 305, Houston, TX 77030, USA; e-mail: creighto{at}bcm.edu; fax: (713) 798-2716.

  • Abbreviations: miRNA, microRNA; E2, 17β-estradiol.

  • Article published online ahead of print. Article and publication date are at http://www.rnajournal.org/cgi/doi/10.1261/rna.1188208.

    • Received May 19, 2008.
    • Accepted July 26, 2008.
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  1. Published in Advance September 23, 2008, doi: 10.1261/rna.1188208 RNA 14: 2290-2296 Copyright © 2008 RNA Society

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