fyi http://www.nature.com/nmeth/journal/v10/n2/full/nmeth.2353.html An approach to cluster and organize systems biology data yields NeXO, a data-driven ontology. Where biology was small before, it now is frequently large. Data points have become data sets. Research studies now commonly examine not just one gene or protein but all of them. It has become a truism to say that this situation produces formidable challenges when it comes to analysis of the resulting vast data sets. The Gene Ontology (GO) has proven to be a widely used resource for analyzing systems biology data. Built manually from the literature by expert curators, GO attempts to synthesize available biological knowledge into an organized hierarchy of biological terms, linking genes to function. Large-scale data sets are frequently analyzed in light of GO to assess whether they capture meaningful patterns of gene function. In a recently published study, Trey Ideker and Janusz Dutkowski at the University of California, San Diego, and their colleagues, effectively turn this way of thinking on its head. They explore, in other words, whether 'omics' data sets themselves have meaningful ontological structure, akin to that of GO, and whether methods can be found to reveal it. The result is a parallel ontology to GO, which they call the network-extracted ontology, or NeXO. -- ========================================================== Bram Slabbinck, PhD Bioinformatics & Systems Biology Division VIB Department of Plant Systems Biology, UGent Technologiepark 927, 9052 Gent, BELGIUM Tel:+32 (0)9 33 13 537 Fax:+32 (0)9 33 13 809 Email: Bram.Slabbinck@psb.ugent.be WWW: http://bioinformatics.psb.ugent.be ========================================================== Services and consulting in bioinformatics http://www.arctix.be ========================================================== Please consider the environment before printing this email