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Bioinformatics Advance Access originally published online on September 23, 2007
Bioinformatics 2007 23(20):2725-2732; doi:10.1093/bioinformatics/btm425
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Fun&Co: identification of key functional differences in transcriptomes

Giacomo Gamberoni 1, Evelina Lamma 2, Gianluca Lodo 2, Jlenia Marchesini 1, Nicoletta Mascellani 1, Simona Rossi 1, Sergio Storari 2, Luca Tagliavini 1 and Stefano Volinia 1,*

1DAMA - Data Mining for Analysis of DNA microarrays and GebbaLab - Department of Morphology and Embryology, Via Fossato di Mortara 64/b - 44100 Ferrara and 2ENDIF, Department of Engineering, Via Saragat 1 - 44100 Ferrara, Italy

*To whom correspondence should be addressed.


  Abstract

Motivation: Microarray and other genome-wide technologies allow a global view of gene expression that can be used in several ways and whose potential has not been yet fully discovered. Functional insight into expression profiles is routinely obtained by using gene ontology terms associated to the cellular genes. In this article, we deal with functional data mining from expression profiles, proposing a novel approach that studies the correlations between genes and their relations to Gene Ontology (GO). We implemented this approach in a public web-based application named Fun&Co. By using Fun&Co, the user dissects in a pair-wise manner gene expression patterns and links correlated pairs to gene ontology terms. The proof of principle for our study was accomplished by dissecting molecular pathways in muscles. In particular, we identified specific cellular pathways by comparing the three different types of muscle in a pairwise fashion. In fact, we were interested in the specific molecular mechanisms regulating the cardiovascular system (cardiomyocytes and smooth muscle cells).

Results: We applied here Fun&Co to the molecular study of cardiovascular system and the identification of the specific molecular pathways in heart, skeletal and smooth muscles (using 317 microarrays) and to reveal functional differences between the three different kinds of muscle cells.

Availability: Application is online at http://tommy.unife.it.

Contact: s.volinia{at}unife.it

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: John Quackenbush

Received on June 29, 2007; revised on August 13, 2007; accepted on August 14, 2007

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