Abstract
The Immune Epitope Database and Analysis Resource (IEDB) contains information related to antibodies and T cells across an expansive scope of research fields (infectious diseases, allergy, autoimmunity, and transplantation). Capture and representation of the data to reflect growing scientific standards and techniques have required continual refinement of our rigorous curation and query and reporting processes beginning with the automated classification of over 28 million PubMed abstracts, and resulting in easily searchable data from over 20,000 published manuscripts. Data related to MHC binding and elution, nonpeptidics, natural processing, receptors, and 3D structure is first captured through manual curation and subsequently maintained through recuration to reflect evolving scientific standards. Upon promotion to the free, public database, users can query and export records of specific relevance via the online web portal which undergoes iterative development to best enable efficient data access. In parallel, the companion Analysis Resource site hosts a variety of tools that assist in the bioinformatic analyses of epitopes and related structures, which can be applied to IEDB-derived and independent datasets alike. Available tools are classified into two categories: analysis and prediction. Analysis tools include epitope clustering, sequence conservancy, and more, while prediction tools cover T and B cell epitope binding, immunogenicity, and TCR/BCR structures. In addition to these tools, benchmarking servers which allow for unbiased performance comparison are also offered. In order to expand and support the user-base of both the database and Analysis Resource, the research team actively engages in community outreach through publication of ongoing work, conference attendance and presentations, hosting of user workshops, and the provision of online help. This review provides a description of the IEDB database infrastructure, curation and recuration processes, query and reporting capabilities, the Analysis Resource, and our Community Outreach efforts, including assessment of the impact of the IEDB across the research community.
Highlights
Overview and introductionOur initial focus in the 2003–2011 period was to design and render operational the Immune Epitope Database (IEDB) and associated Analysis Resource (IEDB-AR) (Peters et al 2005a; Peters et al 2005b; Vita et al 2010; Zhang et al 2008)
The Immune Epitope Database and Analysis Resource (IEDB) contains information related to antibodies and T cells across an expansive scope of research fields
We engage postdoctoral fellows from LJI and other local research institutions, presenting volunteers with user interface (UI) and monitoring them as we ask to solve a scientific question. We have found it beneficial to perform reviews of the entire website by professional, external usability consultants focused on ensuring that the navigation structure and UI design is consistent with best practices for human-computer interaction
Summary
Our initial focus in the 2003–2011 period was to design and render operational the Immune Epitope Database (IEDB) and associated Analysis Resource (IEDB-AR) (Peters et al 2005a; Peters et al 2005b; Vita et al 2010; Zhang et al 2008). The LJI team was awarded support for the IEDB for a new period, spanning years 2019–2025 Throughout this period, our vision will be to meet the challenge of data growth and complexity, and to offer the best available bioinformatics tools to the epitope community. A key component of both design and outreach activities is to connect the epitope data in the IEDB with other knowledge resources such as the BRCs, ImmPort, IMGT, PDB, UniProt, and NCBI Realizing this vision requires meeting significant challenges in terms of infrastructure. In the last 5 years alone, the number of published references per year remains fairly constant, the average number of epitopes published per reference has increased 12-fold, and the number of unique visitors to the IEDB websites has doubled These trends are expected to continue and would put a static IEDB design under pressure. Keeping up with exponential increases in data content and user-base will require constant enhancement of IEDB systems and operations
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