Dataset Information

PDBe aggregated API: programmatic access to an integrative knowledge graph of molecular structure data.

ABSTRACT: The PDBe aggregated API is an open-access and open-source RESTful API that provides programmatic access to a wealth of macromolecular structural data and their functional and biophysical annotations through 80+ API endpoints. The API is powered by the PDBe graph database (https://pdbe.org/graph-schema), an open-access integrative knowledge graph that can be used as a discovery tool to answer complex biological questions. The PDBe aggregated API provides up-to-date access to the PDBe graph database, which has weekly releases with the latest data from the Protein Data Bank, integrated with updated annotations from UniProt, Pfam, CATH, SCOP and the PDBe-KB partner resources. The complete list of all the available API endpoints and their descriptions are available at https://pdbe.org/graph-api. The source code of the Python 3.6+ API application is publicly available at https://gitlab.ebi.ac.uk/pdbe-kb/services/pdbe-graph-api. Supplementary data are available at Bioinformatics online.

SUBMITTER: Nair S

PROVIDER: S-EPMC8570819 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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PDBe aggregated API: programmatic access to an integrative knowledge graph of molecular structure data.

Nair Sreenath S Váradi Mihály M Nadzirin Nurul N Pravda Lukáš L Anyango Stephen S Mir Saqib S Berrisford John J Armstrong David D Gutmanas Aleksandras A Velankar Sameer S

Bioinformatics (Oxford, England) 20211101 21

<h4>Summary</h4>The PDBe aggregated API is an open-access and open-source RESTful API that provides programmatic access to a wealth of macromolecular structural data and their functional and biophysical annotations through 80+ API endpoints. The API is powered by the PDBe graph database (https://pdbe.org/graph-schema), an open-access integrative knowledge graph that can be used as a discovery tool to answer complex biological questions.<h4>Availability and implementation</h4>The PDBe aggregated ...[more]

PMID: 34081107

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Project description:BackgroundAlthough many efforts have been made to develop comprehensive disease resources that capture rare disease information for the purpose of clinical decision making and education, there is no standardized protocol for defining and harmonizing rare diseases across multiple resources. This introduces data redundancy and inconsistency that may ultimately increase confusion and difficulty for the wide use of these resources. To overcome such encumbrances, we report our preliminary study to identify phenotypical similarity among genetic and rare diseases (GARD) that are presenting similar clinical manifestations, and support further data harmonization.ObjectiveTo support rare disease data harmonization, we aim to systematically identify phenotypically similar GARD diseases from a disease-oriented integrative knowledge graph and determine their similarity types.MethodsWe identified phenotypically similar GARD diseases programmatically with 2 methods: (1) We measured disease similarity by comparing disease mappings between GARD and other rare disease resources, incorporating manual assessment; 2) we derived clinical manifestations presenting among sibling diseases from disease classifications and prioritized the identified similar diseases based on their phenotypes and genotypes.ResultsFor disease similarity comparison, approximately 87% (341/392) identified, phenotypically similar disease pairs were validated; 80% (271/392) of these disease pairs were accurately identified as phenotypically similar based on similarity score. The evaluation result shows a high precision (94%) and a satisfactory quality (86% F measure). By deriving phenotypical similarity from Monarch Disease Ontology (MONDO) and Orphanet disease classification trees, we identified a total of 360 disease pairs with at least 1 shared clinical phenotype and gene, which were applied for prioritizing clinical relevance. A total of 662 phenotypically similar disease pairs were identified and will be applied for GARD data harmonization.ConclusionsWe successfully identified phenotypically similar rare diseases among the GARD diseases via 2 approaches, disease mapping comparison and phenotypical similarity derivation from disease classification systems. The results will not only direct GARD data harmonization in expanding translational science research but will also accelerate data transparency and consistency across different disease resources and terminologies, helping to build a robust and up-to-date knowledge resource on rare diseases.

Dataset Information

PDBe aggregated API: programmatic access to an integrative knowledge graph of molecular structure data.

Publications

PDBe aggregated API: programmatic access to an integrative knowledge graph of molecular structure data.

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