A behind-the-scenes tour of the IEDB curation process: an optimized process empirically integrating automation and human curation efforts

Abstract

The Immune Epitope Database and Analysis Resource (IEDB) provides the scientific community with open access to epitope data, as well as epitope prediction and analysis tools. The IEDB houses the most extensive collection of experimentally validated B-cell and T-cell epitope data, sourced primarily from published literature by expert curation. The data procurement requires systematic identification, categorization, curation and quality-checking processes. Here, we provide insights into these processes, with particular focus on the dividends they have paid in terms of attaining project milestones, as well as how objective analyses of our processes have identified opportunities for process optimization. These experiences are shared as a case study of the benefits of process implementation and review in biomedical big data, as well as to encourage idea-sharing among players in this ever-growing space.

Keywords: B cell; T cell; curation; database; epitope.

Figure 1

The IEDB curation process consists of four high‐level steps, each with its own specific sub‐steps designed to ensure data quality and consistency.

Figure 2

The assay‐centric, contextual representation of epitope data is rooted in an ontologically based curation system enabling the Curator to extract and input data from text and figures into defined data fields.

Figure 3

The distribution of effort required for the major steps in the curation workflow, based on compiled input from IEDB staff. This analysis was used to target steps for process optimization.