ClinGen’s Variant Curation Interface (VCI) imports evidence data in standard formats via the Linked Data Hub (LDH)

Le résumé fourni par la source

The NIH-funded Clinical Genome Resource Consortium (ClinGen) has developed a suite of tools that support variant classification. Several of these tools focus on the application of evidence criteria and classification of variants based on the ACMG/AMP sequence variant classification guidelines, and then the dissemination of those classifications to the global community. Among these tools is the Variant Curation Interface (VCI: https://curation.clinicalgenome.org ), an open-source variant classification platform that supports the FDA-recognized human variant curation process by ClinGen Variant Curation Expert Panels (VCEPs). The Linked Data Hub (LDH: https://ldh.genome.network/srvc ) also supports variant curation, by providing efficient access to collated and standardized variant information from different data sources, data provenance information, and links to data sources made available through RESTful APIs. The LDH ingests evidence from key resources in non-standard formats and provides the VCI with this evidence in a structured, machine-readable standardized format to facilitate evidence data incorporation into variant curation pipelines. One challenge for variant curation platforms such as the VCI involves identifying these critically valuable resources, accurately presenting the relevant evidence for specific variants, and providing evidence relevant to a given disease in a structured format for variant curators to review and then use in variant classification. Here we present how the LDH and VCI have addressed this challenge. Specifically we describe two cases where the LDH acquired data from external resources (in this case the pathogenicity predictor REVEL, and the Genome Aggregation Database (gnomAD) version 4.1 release), and provided it such that the VCI can appropriately access and display the data to curators within the curation interface. We describe how these use cases highlight the need for data standardization, version control, and user-driven feedback.

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