Unifying the Identification of Biomedical Entities with the Bioregistry

Abstract

The standardized identification of biomedical entities is a cornerstone of interoperability, reuse, and data integration in the life sciences. Several registries have been developed to catalog resources maintaining identifiers for biomedical entities such as small molecules, proteins, cell lines, and clinical trials. However, existing registries have struggled to provide sufficient coverage and metadata standards that meet the evolving needs of modern life sciences researchers. Here, we introduce the Bioregistry, an integrative, open, community-driven metaregistry that synthesizes and substantially expands upon 20 existing registries. The Bioregistry addresses the need for a sustainable registry by leveraging public infrastructure and automation, and employing a progressive governance model centered around open code and open data to foster community contribution. The Bioregistry can be used to support the standardized annotation of data, models, ontologies, and scientific literature, thereby promoting their interoperability and reuse. The Bioregistry can be accessed through https://bioregistry.io and its source code and data are available under the MIT and CC0 Licenses at https://github.com/biopragmatics/bioregistry.

Prediction and Curation of Missing Biomedical Identifier Mappings with Biomappings

“…by assigning more precise predicates and including provenance information). Second, we hope to see these resources converging on external standards for the syntax and semantics used to communicate the entities and predicates appearing in mappings, such as the Bioregistry (Hoyt et al ., 2022c) in order to improve interoperability. Third, we hope to see large-scale efforts to aggregate, store, and redistribute mappings with more general scope than existing mapping services.…”

“…The first step of the cycle comprises retrieval and preprocessing of target identifier resources, including any existing mappings between the resources. We automate this process for ontologies by using the Bioregistry (Hoyt et al ., 2022c) to locate the ontology (i.e., with a URL) and ROBOT (Jackson et al ., 2019) to parse it. Similarly, we use custom automated preprocessing workflows in PyOBO (Hoyt et al ., 2022b) for other identifier resource types (e.g., databases like HGNC).…”

“…Second, it uses GitHub as technical platform to host and distribute the project’s code and data openly and as a social platform to enable discussion and external contribution through pull requests. Third, it uses GitHub Actions as a continuous integration and continuous delivery system to apply data quality checks (e.g., all mappings’ prefixes and local unique identifiers are compliant with the Bioregistry (Hoyt et al ., 2022c)). Further, several summaries (e.g., charts, tables, website), analyses (see Section 2.4.2), and artifacts (see Section 3.1) are automatically re-generated when a pull request is merged and archived on Zenodo (Hoyt et al ., 2022a).…”

The ProteomeXchange consortium at 10 years: 2023 update

“…Additionally, some PX resources have their own identifiers for datasets, that can also be used in parallel to the PXD identifiers. Furthermore, Digital Object Identifiers (DOIs) can also be issued for ‘Complete’ submissions (see below for more details about submission types) and PXD identifiers are resolved by the identifier resolution services identifiers.org ( 15 ) and Bioregistry ( 16 ). In terms of data license, all PX resources moved to a default Creative Commons CC0 license as the basis in 2020.…”