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Current high-throughput technology in genomics creates a large amount of biological data. Bioinformatics approaches are directed towards understanding such data on a systems biology level. Advanced mathematical methods like principal component analysis, clustering, neural networks, support vector machine (SVM) approaches and neural networks can help to find patterns in the data. However, to really understand the data the patterns must be combined with existing knowledge. One of the approaches to do so is to associate these data to functional classifications such as can be found in the Gene Ontology. Other methods focus on using biological pathways coming from both public and private pathway databases like KEGG, WikiPathways, Reactome, and MetaCore. Some of these pathway databases contain rather aspecific information about which genes are involved in reactions. A single identifier, like an Enzyme Code, may correspond to a full family of enzymes, whereas only one family member is responsible for the reaction in the given biological context. Another problem arises in the availability of relevant biological pathways for both rodents and humans. This is especially true in the field of toxicology, where many pathways are still lacking. The focus of this project was to create phase I and phase II detoxification pathways using PathVisio, the pathway editor of WikiPathways. New pathway content was generated using information obtained from several trusted resources: a) handbooks, b) manual/automated PubMed literature searches; c) online pathway resources; d) UniProt Knowledgebase; e) EnsEMBL; f) GeneCards. In addition, the content of some existing toxicology pathways was improved. To illustrate the usefulness of the new pathways data from experiments with carcinogenic compounds were retrieved from the main online microarray data repositories Gene Expression Omnibus (GEO) and ArrayExpress. The resulting data were visualized on the new pathways using PathVisio. All these pathways are made available to the community at WikiPathways, where they can be further used for statistical pathway analysis and visualization in the (toxico)genomics field

Creating and improving detoxification pathways for interpretation of toxicogenomics data