Fun&Co: identification of key functional differences in transcriptomes.
BACKGROUND
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.


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


BACKGROUND
Supplementary data are available at Bioinformatics online.
Gamberoni
Giacomo
G
Data Mining for Analysis of DNA microarrays and GebbaLab, Department of Morphology and Embryology, Via Fossato di Mortara 64/b-44100 Ferrara, Italy.
Lamma
Evelina
E
Lodo
Gianluca
G
Marchesini
Jlenia
J
Mascellani
Nicoletta
N
Rossi
Simona
S
Storari
Sergio
S
Tagliavini
Luca
L
Volinia
Stefano
S
D000465:Algorithms; D030562:Databases, Protein; D020869:Gene Expression Profiling / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D020543:Proteome / Q000378:metabolism*; D014157:Transcription Factors / Q000378:metabolism*
D020543:Proteome; D014157:Transcription Factors