Bioinformatics Advance Access originally published online on May 6, 2004
Bioinformatics 2004 20(16):2636-2643; doi:10.1093/bioinformatics/bth295
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Bioinformatics vol. 20 issue 16 © Oxford University Press 2004; all rights reserved.
THEA: ontology-driven analysis of microarray data
Institute of Signaling, Developmental Biology and Cancer Research, Laboratory of Virtual Biology, CNRS UMR 6543 Parc Valnore, Nice 06108, Cedex 02, France
Received on February 11, 2004; revised on March 31, 2004; accepted on April 21, 2004
Advance Access Publication May 6, 2004
Motivation: Microarray technology makes it possible to measure thousands of variables and to compare their values under hundreds of conditions. Once microarray data are quantified, normalized and classified, the analysis phase is essentially a manual and subjective task based on visual inspection of classes in the light of the vast amount of information available. Currently, data interpretation clearly constitutes the bottleneck of such analyses and there is an obvious need for tools able to fill the gap between data processed with mathematical methods and existing biological knowledge.
Results: THEA (Tools for High-throughput Experiments Analysis) is an integrated information processing system allowing convenient handling of data. It allows to automatically annotate data issued from classification systems with selected biological information coming from a knowledge base and to either manually search and browse through these annotations or automatically generate meaningful generalizations according to statistical criteria (data mining).
Availability: The software is available on the website http://thea.unice.fr/
Supplementary information: Supplementary tables as well as files containing the biological data used in this publication can be downloaded from our website http://bioinfo.unice.fr/publications/thea_article/
Contact: claude.pasquier{at}unice.fr
* To whom correspondence should be addressed.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  B. Andreopoulos, A. An, X. Wang, and M. Schroeder A roadmap of clustering algorithms: finding a match for a biomedical application Brief Bioinform, May 1, 2009; 10(3): 297 - 314. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 D. W. Huang, B. T. Sherman, and R. A. Lempicki Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists Nucleic Acids Res., January 1, 2009; 37(1): 1 - 13. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Q. Zheng and X.-J. Wang GOEAST: a web-based software toolkit for Gene Ontology enrichment analysis Nucleic Acids Res., July 1, 2008; 36(suppl_2): W358 - W363. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 I. Rivals, L. Personnaz, L. Taing, and M.-C. Potier Enrichment or depletion of a GO category within a class of genes: which test? Bioinformatics, February 15, 2007; 23(4): 401 - 407. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. Khatri, S. Sellamuthu, P. Malhotra, K. Amin, A. Done, and S. Draghici Recent additions and improvements to the Onto-Tools Nucleic Acids Res., July 1, 2005; 33(suppl_2): W762 - W765. [Abstract] [Full Text] [PDF]
|
|