W837
Pathway/Genome Databases of the Solanaceae: Capturing Metabolic Data in Taxon - Specific Databases

Date: Tuesday, January 17, 2017
Time: 12:10 PM
Room: Towne - Meeting House
Hartmut Foerster , Boyce Thompson Institute, Ithaca, NY
Lukas Mueller , Boyce Thompson Institute, Ithaca, NY
The SOL Genomics Network (SGN; http://solgenomics.net) is a hub of biological information about the Solanaceae family and related Asterids. That includes biochemical pathways of selected Solanaceae species which are stored in metabolic encyclopedias and constitute the SolCyc family at SGN. Those species-specific databases, commonly referred to as pathway/genome databases (PGDBs), are generated by utilizing the annotated genomic sequence of the respective Solanaceae species and a curated metabolic reference database. The latter is MetaCyc (http://metacyc.org), a manually curated multi-species database that stores representative samples of experimentally elucidated pathways which include up-to-date information about enzymes, genes and compounds. Recently the collection of Solanacea PGDBs has been extended with new Nicotiana species. Moreover, all the databases of the Solanum and Nicotiana genera are now manually curated which allows the integration of verified genomics, proteomics and metabolomics data and significantly improves on the analysis and visualization of existing biochemical networks in those species. In addition to the species specific databases of Solanum lycopersicum, Solanum tuberosum, Nicotiana tabacum, Nicotiana benthamiana, Nicotiana sylvestris and Nicotiana tomentosiformis, two new databases have been established. SolanaCyc is a family database which contains all pertinent proteomics, genetics and metabolomics data of all members from that family which has been captured from the relevant literature. The other new database is NicotianaCyc which holds the same metabolic information from all the species of the Nicotiana genus. Both databases also include pathways of known pathogens of the nightshade family which may help to identify overlapping metabolic areas in the host/pathogen interface.