Skip to Main content Skip to Navigation
Journal articles

New strategy for the representation and the integration of biomolecular knowledge at a cellular scale

Abstract : 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.
Document type :
Journal articles
Complete list of metadata

https://hal.inria.fr/inria-00202722
Contributor : David James Sherman <>
Submitted on : Tuesday, January 8, 2008 - 3:30:14 AM
Last modification on : Thursday, May 9, 2019 - 2:56:06 PM

Links full text

Identifiers

Citation

Roland Barriot, Jerome Poix, Alexis Groppi, Aurélien Barré, Nicolas Goffard, et al.. New strategy for the representation and the integration of biomolecular knowledge at a cellular scale. Nucleic Acids Research, Oxford University Press, 2004, 32 (12), pp.3581-9. ⟨10.1093/nar/gkh681⟩. ⟨inria-00202722⟩

Share

Metrics

Record views

239