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Bioinformatics Advance Access originally published online on July 24, 2009
Bioinformatics 2009 25(19):2473-2477; doi:10.1093/bioinformatics/btp462
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Relationship between gene co-expression and sharing of transcription factor binding sites in Drosophila melanogaster

Antonio Marco 1,*, Charlotte Konikoff 1,2, Timothy L. Karr 1,3 and Sudhir Kumar 1,2

1 Center for Evolutionary Functional Genomics, 2 School of Life Sciences and 3 Center for Infectious Diseases and Vaccinology, The Biodesign Institute, Arizona State University, Tempe, AZ 85287-5301, USA

* To whom correspondence should be addressed.


  Abstract

Motivation: In functional genomics, it is frequently useful to correlate expression levels of genes to identify transcription factor binding sites (TFBS) via the presence of common sequence motifs. The underlying assumption is that co-expressed genes are more likely to contain shared TFBS and, thus, TFBS can be identified computationally. Indeed, gene pairs with a very high expression correlation show a significant excess of shared binding sites in yeast. We have tested this assumption in a more complex organism, Drosophila melanogaster, by using experimentally determined TFBS and microarray expression data. We have also examined the reverse relationship between the expression correlation and the extent of TFBS sharing.

Results: Pairs of genes with shared TFBS show, on average, a higher degree of co-expression than those with no common TFBS in Drosophila. However, the reverse does not hold true: gene pairs with high expression correlations do not share significantly larger numbers of TFBS. Exception to this observation exists when comparing expression of genes from the earliest stages of embryonic development. Interestingly, semantic similarity between gene annotations (Biological Process) is much better associated with TFBS sharing, as compared to the expression correlation. We discuss these results in light of reverse engineering approaches to computationally predict regulatory sequences by using comparative genomics.

Contact: amarcoca{at}asu.edu

Associate Editor: Joaquin Dopazo

Received on March 15, 2009; revised on July 3, 2009; accepted on July 22, 2009

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