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As the amount of biomedical information available in the literature continues to increase, databases that aggregate this information continue to grow in importance and scope. The population of databases can occur either through fully automated text mining approaches or through manual curation by human subject experts. We here report our experiences in populating the National Institute of Allergy and Infectious Diseases sponsored Immune Epitope Database and Analysis Resource ( IEDB, http://iedb.org), which was created in 2003, and as of 2012 captures the epitope information from approximately 99% of all papers published to date that describe immune epitopes (with the exception of cancer and HIV data). This was achieved using a hybrid model based on automated document categorization and extensive human expert involvement. This task required automated scanning of over 22 million PubMed abstracts followed by classification and curation of over 13 000 references, including over 7000 infectious disease-related manuscripts, over 1000 allergy-related manuscripts, roughly 4000 related to autoimmunity, and 1000 transplant/alloantigen-related manuscripts. The IEDB curation involves an unprecedented level of detail, capturing for each paper the actual experiments performed for each different epitope structure. Key to enabling this process was the extensive use of ontologies to ensure rigorous and consistent data representation as well as interoperability with other bioinformatics resources, including the Protein Data Bank, Chemical Entities of Biological Interest, and the NIAID Bioinformatics Resource Centers. A growing fraction of the IEDB data derives from direct submissions by research groups engaged in epitope discovery, and is being facilitated by the implementation of novel data submission tools. The present explosion of information contained in biological databases demands effective query and display capabilities to optimize the user experience. Accordingly, the development of original ways to query the database, on the basis of ontologically driven hierarchical trees, and display of epitope data in aggregate in a biologically intuitive yet rigorous fashion is now at the forefront of the IEDB efforts. We also highlight advances made in the realm of epitope analysis and predictive tools available in the IEDB. [ABSTRACT FROM AUTHOR]

In this paper we describe the first in vitro response to the liver alloantigen F. The anti-F response serves as a valuable model for autoimmune phenomena since priming appropriate strains of mice (responders) with allogeneic but not syngeneic type F leads to autoantibody production. The in vitro system is based on the proliferation of T cells, from mice primed in vivo with F, when coincubated with splenic adherent cells (SAC) prepulsed with F in vivo. The system displays two important correlates of the in vivo antibody response to F:1.T cells from mice primed with syngeneic F do not proliferate when incubated with SAC prepulsed with syngeneic F and 2. Mice that do not make antibody responses to allo F in vivo (DBA/2) do not show in vitro proliferative responses. These findings indicate that the proliferative assay is a good in vitro model for the F response. [ABSTRACT FROM AUTHOR]