Published online 7 July 2004
Nucleic Acids Research, Vol. 32 No. 12 © Oxford University Press 2004; all rights reserved
New strategy for the representation and the integration of biomolecular knowledge at a cellular scale
1 Centre de Bioinformatique de Bordeaux, Université V. Segalen Bordeaux 2, 146 rue Léo Saignat, 33076 Bordeaux, France, 2 LaBRI, Laboratoire Bordelais de Recherche en Informatique, UMR CNRS 5800, 351 cours de la Libération, 33405 Talence Cedex, France and 3 Laboratoire Statistique Mathématique et ses Applications, EA 2961, Université V. Segalen Bordeaux 2, 146 rue Léo Saignat, 33076 Bordeaux, France
* To whom correspondence should be addressed. Tel: +33 5 57 57 12 47; Fax: +33 5 57 57 12 47; Email: antoine.daruvar{at}pmtg.u-bordeaux2.fr
Received March 8, 2004; Revised and Accepted June 4, 2004
The combination of sequencing and post-sequencing experimental approaches produces huge collections of data that are highly heterogeneous both in structure and in semantics. We propose a new strategy for the integration of such data. This strategy uses structured sets of sequences as a unified representation of biological information and defines a probabilistic measure of similarity between the sets. Sets can be composed of sequences that are known to have a biological relationship (e.g. proteins involved in a complex or a pathway) or that share similar values for a particular attribute (e.g. expression profile). We have developed a software, BlastSets, which implements this strategy. It exploits a database where the sets derived from diverse biological information can be deposited using a standard XML format. For a given query set, BlastSets returns target sets found in the database whose similarity to the query is statistically significant. The tool allowed us to automatically identify verified relationships between correlated expression profiles and biological pathways using publicly available data for Saccharomyces cerevisiae. It was also used to retrieve the members of a complex (ribosome) based on the mining of expression profiles. These first results validate the relevance of the strategy and demonstrate the promising potential of BlastSets.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  S. Yilmaz, P. Jonveaux, C. Bicep, L. Pierron, M. Smail-Tabbone, and M.D. Devignes Gene-disease relationship discovery based on model-driven data integration and database view definition Bioinformatics, January 15, 2009; 25(2): 230 - 236. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A.M. Willemsen, G.A. Jansen, J.C. Komen, S. van Hooff, H.R. Waterham, P.M.T. Brites, R.J.A. Wanders, and A.H.C. van Kampen Organization and integration of biomedical knowledge with concept maps for key peroxisomal pathways Bioinformatics, August 15, 2008; 24(16): i21 - i27. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 K. De Preter, R. Barriot, F. Speleman, J. Vandesompele, and Y. Moreau Positional gene enrichment analysis of gene sets for high-resolution identification of overrepresented chromosomal regions Nucleic Acids Res., April 1, 2008; 36(7): e43 - e43. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
N. Goffard and G. Weiller PathExpress: a web-based tool to identify relevant pathways in gene expression data Nucleic Acids Res., July 13, 2007; 35(suppl_2): W176 - W181. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Van Vooren, B. Thienpont, B. Menten, F. Speleman, B. D. Moor, J. Vermeesch, and Y. Moreau Mapping biomedical concepts onto the human genome by mining literature on chromosomal aberrations Nucleic Acids Res., April 3, 2007; 35(8): 2533 - 2543. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
N. Goffard and G. Weiller Extending MapMan: application to legume genome arrays Bioinformatics, December 1, 2006; 22(23): 2958 - 2959. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
G. Wrobel, F. Chalmel, and M. Primig goCluster integrates statistical analysis and functional interpretation of microarray expression data Bioinformatics, September 1, 2005; 21(17): 3575 - 3577. [Abstract] [Full Text] [PDF]
|
|