Since the introduction of Edward Jenner's smallpox vaccine in 1796, vaccines have proven invaluable for their ability to stimulate the immune system and to confer protection against pathogenic organisms. Progress in modern vaccine research has been accompanied by a dramatic increase in the number of vaccine-related papers in the published literature. It has become increasingly challenging to identify and annotate vaccine data from this large and diverse literature which no one scientist or team can fully master. Although vaccine databases exist that emphasize commercialized vaccines, no public central repository is available to store research data concerning commercial vaccines, vaccines in clinical trials, or vaccine candidates in early stages of development, in a fashion that render such data available for advanced analyses. To fill this need, we have developed VIOLIN (http://www.violinet.org), a web-based database system for annotation, storage, and analysis of published vaccine data. An ontology represents consensus-based controlled vocabularies of terms and relations, with associated definitions which are logically formulated in such a way as to promote automated reasoning. A bottleneck of vaccine research and further VIOLIN development is the lack of a vaccine ontology, which in turn makes a significant obstacle for vaccine data standardization, retrieval, integration, and advanced analysis and prediction. Our goal is to develop the community-based Vaccine Ontology (VO) and apply it to efficient vaccine literature mining and analysis of protective immune mechanisms. We will focus on two model pathogens: Escherichia coli and Brucella species. This project contains three specific aims: (1) develop a community-based Vaccine Ontology (VO), and apply it to establish a vaccine knowledgebase and to promote vaccine data integration and query through Semantic Web. The VO development will be achieved through collaboration with vaccine researchers, the Infectious Disease Ontology (IDO) Initiative, and the National Center for Biomedical Ontology (NCBO);(2) develop a VO-based natural language processing (NLP) system and apply it for more efficient retrieval of Brucella and E. coli vaccine information, automated annotation of journal articles with VO terms, and VO improvement. This task will be achieved by collaboration with the National Center for Integrative Biomedical Informatics (NCIBI). (3) analyze and predict vaccine targets and protective immune networks attributable to the interactions between host and vaccine. This will be achieved mainly by VO-based literature mining and a novel genome- and literature-based statistical methodology. This project will be implemented by a strong collaborative team and supported from a large user community. The Vaccine Ontology and its applications to literature mining and for studying protective immunity against Brucella spp. and E. coli will lay a strong foundation for further advanced informatics research on vaccines against infectious diseases in the post-genomics and information era.