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FLAVIdB: A data mining system for knowledge discovery in flaviviruses with direct applications in immunology and vac-cinology
Background The flavivirus genus is unusually large, comprising more than 70 species, of which more than half are known human pathogens. It includes a set of clinically relevant infectious agents such as dengue, West Nile, yellow fever, and Japanese encephalitis viruses. Although these pathogens have been studied exten-sively, safe and efficient vaccines lack for the majority of the flaviviruses. Results We have assembled a database that combines antigenic data of flaviviruses, specialized analysis tools, and workflows for automated complex analyses focusing on applications in immunology and vaccinology. FLAVIdB contains 12,858 entries of flavivirus antigen sequences, 184 verified T-cell epitopes, 201 verified B-cell epitopes, and 4 representative molecular structures of the dengue virus envelope protein. FLAVIdB was assembled by collection, annotation, and integration of data from GenBank, GenPept, UniProt, IEDB, and PDB. The data were subject to extensive quality control (redundancy elimination, error detection, and vocabulary consolidation). Further annotation of selected functionally relevant features was performed by organizing information extracted from the literature. The database was incorporated into a web-accessible data mining system, combining specialized data analysis tools for integrated analysis of relevant data cate-gories (protein sequences, macromolecular structures, and immune epitopes). The data mining system in-cludes tools for variability and conservation analysis, T-cell epitope prediction, and characterization of neu-tralizing components of B-cell epitopes. FLAVIdB is accessible at cvc.dfci.harvard.edu/flavi/ Conclusion FLAVIdB represents a new generation of databases in which data and tools are integrated into a data min-ing infrastructures specifically designed to aid rational vaccine design by discovery of vaccine targets.