- Article -
| In Silico Biology 8, 0014 (2008); ©2008, Bioinformation Systems e.V. |
Constraint-based knowledge discovery from SAGE data
Jiří Kléma11,3, Sylvain Blachon2, Arnaud Soulet4, Bruno Crémilleux1 and Olivier Gandrillon2*
1 GREYC, CNRS UMR 6072, Université de Caen, Campus Côte de Nacre, F-14032 Caen Cédex France
2 Université de Lyon, Lyon, F-69003, France; Université Lyon 1, Lyon, F-69003, France; Centre de Génétique Moléculaire et Cellulaire, CNRS UMR 5534, F-69622 Villeurbanne Cédex France
3 Department of Cybernetics, Czech Technical University in Prague, Technická 2, Prague 166 27, Czech Republic
4 LI, Université de Tours, 3 place Jean Jaure's, F-41029 Blois France
* Corresponding author
Email: gandrillon@cgmc.univ-lyon1.fr
Edited by H. Michael; received October 02, 2007; revised January 30, 2008; accepted February 11, 2008; published April 05, 2008
Abstract
Current analyses of co-expressed genes are often based on global approaches such as clustering or bi-clustering. An alternative way is to employ local methods and search for patterns - sets of genes displaying specific expression properties in a set of situations. The main bottleneck of this type of analysis is twofold - computational costs and an overwhelming number of candidate patterns which can hardly be further exploited. A timely application of background knowledge available in literature databases, biological ontologies and other sources can help to focus on the most plausible patterns only. The paper proposes, implements and tests a flexible constraint-based framework that enables the effective mining and representation of meaningful over-expression patterns representing intrinsic associations among genes and biological situations. The framework can be simultaneously applied to a wide spectrum of genomic data and we demonstrate that it allows to generate new biological hypotheses with clinical implications.
Keywords: functional genomics, SAGE, local pattern, background knowledge, gene ontology, biomedical literature, constraint