The Immune Epitope Database and Analysis Resource (IEDB, iedb.org) and its sibling resource, the Cancer Epitope Database and Analysis Resource (CEDAR, cedar.iedb.org), are platforms created and maintained for the benefit of the scientific community, allowing open access to organized epitope data that is then utilized to power the associated epitope prediction tools.
The foundation of the curation process is in staying current with published research literature, our primary source of epitope data, and in structuring the data according to associated data ontologies. The process for inputting data includes identification, categorization, curation, peer review, and systemic quality control and validation steps.
The curation process results in databases that reflect the epitope literature in the areas of infectious disease, autoimmunity, allergy, transplantation, and cancer. Here, we detail the data input process with a focus on the manual curation efforts of the expert curation team, including the recent addition of CAR-T cell epitopes, updates to non-peptidic curation, and new quality-control processes for cell lines.
These improvements help ensure end-users are getting the greatest benefit possible from the IEDB and CEDAR.
NIH/NIAID (75N93019C00001), and NIH/NCI (U24CA248138)
Technological Innovations in Immunology (TECH)
Sara Coleman, Nina Blazeka, Bjoern Peters et al.· Journal of Immunology· 0 citations
ImmuneSpace (immunespace.org) is a freely accessible database that hosts curated human immune-profiling data from a wide range of studies. It was created as the central data repository of the Human Immunology Project Consortium (HIPC), a multi-center NIH-funded program to characterize the diverse states of the human immune system and its regulation, using consistently formatted data [PMID: 23648045]. The overarching goal is to investigate human immune perturbations using state-of-the-art systems-level profiling technologies and innovative methodologies, and to make these data available to the scientific community, accessible for both humans and machines.
ImmuneSpace hosts data related to immunological exposure, demographics, cytokine profiling, cytometry, and neutralizing antibody assays. It builds on the ImmPort data model, a long-term archive of research and clinical data for the NIH [PMID: 29485622], but implements additional standardization and normalization rules, following the HIPC Data Standards initiative [PMID: 22343568, 26861911, 31272390, 32283555].
ImmuneSpace is continuously updated with data and features. Each study undergoes enhanced curation to ensure consistent use of ontology-based terminology, enabling more efficient queries both within and across studies. Study components stored in different repositories, unparsed raw data, and computationally inaccessible elements are integrated through manual curation, such as study timelines and author-determined ‘immune signatures’. Recently, we have added the ‘Finder Feature’ which simplifies complex searches through a hierarchical, tree-based interface that allows users to visually browse, search with autocomplete, or select entire branches of categorized filters while providing definitions, synonyms, and ontology links.
The HIPC Project and the ImmuneSpace platform demonstrate the feasibility and benefits of a structured approach to representing human immunological studies to elucidate system-level phenomena.
U01 AI167892
Computational and Systems Immunology (COMP)
Kerstin Westendorf, M. Kojima, James A. Overton et al.· Journal of Immunology· 0 citations
Revising all immune receptor records to produce resolved, standardized, and analysis-ready receptor data will enable researchers to seamlessly query large-scale repertoires for receptors with experimentally verified specificity in the IEDB, link orphan sequences to known targets, and support cross-repository studies of receptor-epitope pairs and their relationship to health and disease.
Lonneke Scheffer, Eve Richardson, R. Vita et al.· Journal of Immunology· 0 citations
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