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Bioinformatics Advance Access originally published online on November 24, 2006
Bioinformatics 2007 23(3):298-305; doi:10.1093/bioinformatics/btl597
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Integrating transcription factor binding site information with gene expression datasets

Ian B. Jeffery 1,*, Stephen F. Madden 1, Paul A. McGettigan 1, Guy Perrière 2, Aedín C. Culhane 3 and Desmond G. Higgins 1

1 UCD Conway Institute, University College Dublin Belfield, Dublin 4, Ireland
2 Laboratoire de Biométrie et de Biologie Évolutive, UMR CNRS 5558 Université Claude Bernard Lyon 1, 43 boulevard du 11 Novembre, 1918, 69622 Villeurbanne Cedex, France
3 Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute Mayer 232, 44 Binney Street, Boston, MA 02115, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Microarrays are widely used to measure gene expression differences between sets of biological samples. Many of these differences will be due to differences in the activities of transcription factors. In principle, these differences can be detected by associating motifs in promoters with differences in gene expression levels between the groups. In practice, this is hard to do.

Results: We combine correspondence analysis, between group analysis and co-inertia analysis to determine which motifs, from a database of promoter motifs, are strongly associated with differences in gene expression levels. Given a database of motifs and gene expression levels from a set of arrays, the method produces a ranked list of motifs associated with any specified split in the arrays. We give an example using the Gene Atlas compendium of gene expression levels for human tissues where we search for motifs that are associated with expression in central nervous system (CNS) or muscle tissues. Most of the motifs that we find are known from previous work to be strongly associated with expression in CNS or muscle. We give a second example using a published prostate cancer dataset where we can simply and clearly find which transcriptional pathways are associated with differences between benign and metastatic samples.

Availability: The source code is freely available upon request from the authors.

Contact: Ian.Jeffery{at}ucd.ie

Associate Editor: David Rocke

Received on June 15, 2006; revised on September 12, 2006; accepted on November 21, 2006

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