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Amongst our approach several alternative tools for the functional analysis of pre-processed microarray data exist, that are also based on Gene Ontology (GO) as functional classification of gene products.
Most of them require a cut-off to be defined for the gene expression values by the user in the run-up.
Thus, for example ratio of > 2 for up-regulated and < 0.5 for down-regulated genes or > 0.9 as threshold for probabilities of differential expression.
More tools can be found on the corresponding section of Gene Ontology home page.
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| Tool Name | Statistical analysis | Cut-off free | Multiple test. corr. | Implementation | Access | Ref. |
| GO:TermFinder | hypergeometric test | no | yes | Perl | local installation | [1] |
| GOTM | hypergeometric test | no | yes | PHP | web interface |
| FatiGO | Fisher's exact test | no | yes | ? | web interface | [1] |
| GoMiner | Fisher's exact test | no | yes | Java | local installation |
| GOAL | permutation-based | yes | yes | Perl | web interface |
| GO-Mapper | expression quotient | yes | ? | Perl | local installation |
| structured permutation | permutation-based | yes | yes | R | local installation |
| JGOGO (this tool) | Kolmogorov-Smirnov &
Wilcoxon's test | yes | yes | Java and R | web interface |
[1]F. Al-Shahrour, R. Diaz-Uriarte, and J. Dopazo. FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics, 20:578–80, 2004.
[4]W.T. Barry, A.B. Nobel, and F.A. Wright. Significance analysis of functional
categories in gene expression studies: a structured permutation approach.
Bioinformatics, 2005.
[6]E.I. Boyle, S. Weng, J. Gollub, H. Jin, D. Botstein, J.M. Cherry, and G. Sherlock.
GO::TermFinder–open source software for accessing Gene Ontology information
and finding significantly enriched Gene Ontology terms associated with a list of
genes. Bioinformatics, 20:3710–5, 2004.
[7]S.W. Doniger, N. Salomonis, K.D. Dahlquist, K. Vranizan, S.C. Lawlor, and B.R.
Conklin. MAPPFinder: using Gene Ontology and GenMAPP to create a global
gene-expression profile from microarray data. Genome Biol, 4:R7, 2003.
[9]D. Martin, C. Brun, E. Remy, P. Mouren, D. Thieffry, and B. Jacq. GOTool-
Box: functional analysis of gene datasets based on Gene Ontology. Genome Biol,
5:R101, 2004.
[11]C. Pasquier, F. Girardot, K. de Jevardat, and R. Christen. THEA: ontology-driven
analysis of microarray data. Bioinformatics, 20:2636–43, 2004.
[16]M. Smid and L.C. Dorssers. GO-Mapper: functional analysis of gene expression
data using the expression level as a score to evaluate Gene Ontology terms.
Bioinformatics, 20:2618–25, 2004.
[17]S. Volinia, R. Evangelisti, F. Francioso, D. Arcelli, M. Carella, and P. Gasparini.
GOAL: automated Gene Ontology analysis of expression profiles. Nucleic Acids
Res, 32:W492–9, 2004.
|19]B.R. Zeeberg, W. Feng, G. Wang, M.D. Wang, A.T. Fojo, M. Sunshine, S. Narasimhan,
D.W. Kane,W.C. Reinhold, S. Lababidi, K.J. Bussey, J. Riss, J.C. Barrett,
and J.N. Weinstein. GoMiner: a resource for biological interpretation of genomic
and proteomic data. Genome Biol, 4:R28, 2003.
[20]B. Zhang, D. Schmoyer, S. Kirov, and J. Snoddy. GOTree machine (GOTM): a
web-based platform for interpreting sets of interesting genes using Gene Ontology
hierarchies. BMC Bioinformatics, 5:16, 2004.
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