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Background: The complexity of genome-scale metabolic models makes them quite difficult for human users to read, since they contain thousands of reactions that must be included for accurate computer simulation. Interestingly, hidden similarities between groups of reactions can be discovered, and generalized to reveal higher-level patterns. Results: The web-based navigation system Mimoza allows a human expert to explore metabolic network models in a semantically zoomable manner: The most general view represents the compartments of the model; the next view shows the generalized versions of reactions and metabolites in each compartment; and the most detailed view represents the initial network with the generalization-based layout (where similar metabolites and reactions are placed next to each other). It allows a human expert to grasp the general structure of the network and analyze it in a top-down manner Conclusions: Mimoza can be installed standalone, or used on-line at http://mimoza.bordeaux.inria.fr/, or installed in a Galaxy server for use in workflows. Mimoza views can be embedded in web pages, or downloaded as COMBINE archives. [ABSTRACT FROM AUTHOR]

Background: The search for enriched features has become widely used to characterize a set of genes or proteins. A key aspect of this technique is its ability to identify correlations amongst heterogeneous data such as Gene Ontology annotations, gene expression data and genome location of genes. Despite the rapid growth of available data, very little has been proposed in terms of formalization and optimization. Additionally, current methods mainly ignore the structure of the data which causes results redundancy. For example, when searching for enrichment in GO terms, genes can be annotated with multiple GO terms and should be propagated to the more general terms in the Gene Ontology. Consequently, the gene sets often overlap partially or totally, and this causes the reported enriched GO terms to be both numerous and redundant, hence, overwhelming the researcher with non-pertinent information. This situation is not unique, it arises whenever some hierarchical clustering is performed (e.g. based on the gene expression profiles), the extreme case being when genes that are neighbors on the chromosomes are considered. Results: We present a generic framework to efficiently identify the most pertinent overrepresented features in a set of genes. We propose a formal representation of gene sets based on the theory of partially ordered sets (posets), and give a formal definition of target set pertinence. Algorithms and compact representations of target sets are provided for the generation and the evaluation of the pertinent target sets. The relevance of our method is illustrated through the search for enriched GO annotations in the proteins involved in a multiprotein complex. The results obtained demonstrate the gain in terms of pertinence (up to 64% redundancy removed), space requirements (up to 73% less storage) and efficiency (up to 98% less comparisons). Conclusion: The generic framework presented in this article provides a formal approach to adequately represent available data and efficiently search for pertinent over-represented features in a set of genes or proteins. The formalism and the pertinence definition can be directly used by most of the methods and tools currently available for feature enrichment analysis. [ABSTRACT FROM AUTHOR]