JProGO RELATED TOOLS

Tools

Amongst our tool, several other tools for the functional analysis of pre-processed microarray data exist, that use information from Gene Ontology (GO) functional classification system. So far, none of them focusses on the immediate online analysis of microarray data from prokaroytes.

Some of the tools use a threshold value in order to select the differentially expressed genes. Here, for example, for the expression ratios a cut-off ratio of > 2 for up-regulated and< 0.5 for down-regulated genes is common, or > 0.9 as cut-off for probabilities of differential expression. Commonly used statistical test for this purpose are the hypergeometric test and Fisher's exact test. The problem with the cut-off value is its arbitrariness and the reduction of information content due to the implicite binarization of the data (present or absent).
Other tools were made available which do not require the definition of such a threshold value. They are either based on a permutation approach or on Student's t-test or Kolmogorov-Smirnov test.
With our tool JProGO, we offer an integrated platform of the above mentioned two threshold value-based and two threshold value independent methods. In addition, we provide use of the threshold free unpaired Wilcoxon's test, which is rank-based.
A complete list of tools for GO-based gene expression analysis can be found on the corresponding section of the Gene Ontology home page.

Tools for the Functional Interpretation of Gene Expression Data:

(alphabetically sorted by tool's name)

Name	Statistical analysis	Multiple test. corr.	Implementation	Access	Ref.
FatiGO	Fisher's exact test	yes	?	web service	[1]
GOAL	permutation-based	yes	Perl	web service	[2]
GOdist	Fisher's exact test, Kolmogorov-Smirnov test	yes	Matlab	local installation (Matlab required)	[3]
GO-Mapper	expression quotient	---	Perl	local installation	[4]
GoMiner	Fisher's exact test	yes	Java	local installation	[5]
GO:TermFinder	hypergeometric test	yes	Perl	local installation	[6]
GOTM	hypergeometric test	yes	PHP	web service	[7]
GOTool-Box	hypergeometric test	yes	Perl	web service	[8]
JProGO (this tool)	hypergeometric test, Fisher's exact test, Student's t-test, Kolmogorov-Smirnov test & Wilcoxon's test	yes	Java and R	web service	[13]
GSEA (Gene Set Enrichment Analysis)	GSEA method (related to Kolmogorov-Smirnov test)	yes	Java / R	local installation	[9]
MAPPfinder	z-score calculation	no	?	local installation	[10]
Significance Analysis using Structured Permutations	permutation-based	yes	R	local installation	[11]
T-profiler	Student's t-test	yes	?	web service	[12]

References

[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.

[2] 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.

[3] Y. Ben-Shaul, H. Bergman and H. Soreq. Identifying subtle interrelated changes in functional gene categories using continuous measures of gene expression. Bioinformatics, 21:1129-37, 2005

[4] 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.

[5] 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.

[6] E.I. Boyle, S. Weng, J. Gollub, H. Jin, D. Botstein, J.M. Cherry, and G. Sherlock. GO::TermFinderâopen 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] 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.

[8] 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.

[9] A. Subramanian, P. Tamayo, V.K. Mootha, S. Mukherjee, B.L. Ebert, M.A. Gillette, A. Paulovich A, S.L. Pomeroy, T.R. Golub, E.S. Lander and J.P. Mesirov.
Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U S A, 102:15545-50, 2005.

[10] 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.

[11] W.T. Barry, A.B. Nobel, and F.A. Wright. Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics, 21:1943-49, 2005.

[12] A. Boorsma, B.C. Foat, D. Vis, F. Klis and H.J. Bussemaker. T-profiler: Scoring the activity of pre-defined groups of genes using gene expression data. Nucleic Acids Res., 33:W592-5, 2005.

[13] M. Scheer, F. Klawonn, R. Münch, A. Grote, K. Hiller, C. Choi, I. Koch, M. Schobert, E. Härtig, U. Klages and D. Jahn. JProGO: a novel tool for the functional interpretation of prokaryotic microarray data using Gene Ontology information. Nucleic Acids Res., 34:W510-5, 2006.