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Large-scale cancer genomics discovery projects such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) have systematically characterized the molecular lesions in human cancer genomes, thereby laying the foundation for precision cancer medicine. However, a curated set of somatic variants with established relevance to cancer biology is essential for clinical annotation and for use in computational data analysis. We have created a database of curated mutations in cancer.

Manually curated variant knowledgebases and their associated knowledge models are serving an increasingly important role in distributing and interpreting variants in cancer. These knowledgebases vary in their level of public accessibility, and the complexity of the models used to capture clinical knowledge. CIViC (Clinical Interpretation of Variants in Cancer - www.civicdb.org ) is a fully open, free-to-use cancer variant interpretation knowledgebase that incorporates highly detailed curation of evidence obtained from peer-reviewed publications and meeting abstracts, and currently holds over 6300 Evidence Items for over 2300 variants derived from over 400 genes. CIViC has seen increased adoption by, and also undertaken collaboration with, a wide range of users and organizations involved in research. To enhance CIViC’s clinical value, regular submission to the ClinVar database and pursuit of other regulatory approvals is necessary. For this reason, a formal peer reviewed curation guideline and discussion of the underlying principles of curation is needed. We present here the CIViC knowledge model, standard operating procedures (SOP) for variant curation, and detailed examples to support community-driven curation of cancer variants.

CIViC is an expert-crowdsourced knowledgebase for Clinical Interpretation of Variants in Cancer describing the therapeutic, prognostic, diagnostic and predisposing relevance of inherited and somatic variants of all types. CIViC is committed to open-source code, open-access content, public application programming interfaces (APIs) and provenance of supporting evidence to allow for the transparent creation of current and accurate variant interpretations for use in cancer precision medicine.

A database of known drug-gene interactions, with information derived from many public sources, allows the identification of genes that are currently targeted by a drug and the membership of genes in a category, such as kinase genes, that have a high potential for drug development. The Drug-Gene Interaction database (DGIdb) mines existing resources that generate hypotheses about how mutated genes might be targeted therapeutically or prioritized for drug development. It provides an interface for searching lists of genes against a compendium of drug-gene interactions and potentially 'druggable' genes. DGIdb can be accessed at http://dgidb.org/ .

The druggable genome encompasses the genes that are known or predicted to interact with drugs. The Drug-Gene Interaction Database (DGIdb) provides an integrated resource for discovering and contextualizing these interactions, supporting a broad range of research and clinical applications. DGIdb is currently accessed through structured web interfaces and API calls, requiring users to translate natural-language questions into database-specific query patterns. To allow for the use of DGIdb through natural language, we developed the DGIdb Model Context Protocol (MCP) server, which allows large language models (LLMs) access to up-to-date information through the DGIdb API. We demonstrate that the MCP server greatly enhances an LLM's ability to answer questions requiring accurate, up-to-date biomedical knowledge drawn from structured external resources. The DGIdb MCP server is detailed at https://github.com/griffithlab/dgidb-mcp-server and includes instructions for accessing the server through the Claude desktop app.

The Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase provides a community-driven, open-source platform for discussing the biological and Clinical Significance of molecular variants in cancer. To enable users to make complex connections between CIViC information, we developed the CIViC Model Context Protocol (MCP) server, which allows large language models (LLMs) to directly interface with the CIViC API through natural language, facilitating the rapid summarization of expertly curated cancer variant interpretations. The CIViC MCP server is detailed at https://github.com/griffithlab/civic-mcp-server with archived code and evaluation data deposited in Zenodo (DOI: 10.5281/zenodo.17344050). The repository is a fork of https://github.com/QuentinCody/civic-mcp-server (QuentinCody 2025) and includes instructions for accessing the server through the Claude desktop app and hosting it locally with GPT-5. We also provide a Python script for directly querying the MCP server. Supplementary data are available at online.

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) consolidates, organizes, and presents drug-gene interactions and gene druggability information from papers, databases, and web resources. DGIdb normalizes content from more than thirty disparate sources and allows for user-friendly advanced browsing, searching and filtering for ease of access through an intuitive web user interface, application programming interface (API), and public cloud-based server image. DGIdb v3.0 represents a major update of the database. Nine of the previously included twenty-eight sources were updated. Six new resources were added, bringing the total number of sources to thirty-three. These updates and additions of sources have cumulatively resulted in 56,309 interaction claims. This has also substantially expanded the comprehensive catalogue of druggable genes and antineoplastic drug-gene interactions included in the DGIdb. Along with these content updates, v3.0 has received a major overhaul of its codebase, including an updated user interface, preset interaction search filters, consolidation of interaction information into interaction groups, greatly improved search response times, and upgrading the underlying web application framework. In addition, the expanded API features new endpoints which allow users to extract more detailed information about queried drugs, genes, and drug-gene interactions, including listings of PubMed IDs (PMIDs), interaction type, and other interaction metadata.

CIViC (Clinical Interpretation of Variants in Cancer; civicdb.org) is a crowd-sourced, public domain knowledgebase composed of literature-derived evidence characterizing the clinical utility of cancer variants. As clinical sequencing becomes more prevalent in cancer management, the need for cancer variant interpretation has grown beyond the capability of any single institution. With nearly 300 contributors, CIViC contains peer-reviewed, published literature curated and expert-moderated into structured data units (Evidence Items) that can be accessed globally and in real time, reducing barriers to clinical variant knowledge sharing. We have extended CIViC’s functionality to support emergent variant interpretation guidelines, increase interoperability with other variant resources, and promote widespread dissemination of structured curated data. To support the full breadth of variant interpretation from basic to translational, including integration of somatic and germline variant knowledge and inference of drug response, we have enabled curation of three new evidence types (predisposing, oncogenic and functional). The growing CIViC knowledgebase distributes clinically-relevant cancer variant data currently representing >2500 variants in >400 genes from >2800 publications.

As guidelines, therapies and literature on cancer variants expand, the lack of consensus variant interpretations impedes clinical applications. CIViC is a public-domain, crowd-sourced and adaptable knowledgebase of evidence for the clinical interpretation of variants in cancer, designed to reduce barriers to knowledge sharing and alleviate the variant-interpretation bottleneck.

ABSTRACT The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that provides information on drug-gene interactions and druggable genes from various sources including publications, databases, and other web-based sources in one resource. These drug, gene, and interaction claims are normalized and grouped to identify aliases, merge concepts, and reduce redundancy. The information contained in this resource is available to users through a straightforward search interface, an application programming interface (API), and TSV data downloads. DGIdb 4.0 is the latest major update of this database. Seven new sources have been added, bringing the total number of sources included to 41. Of the previously aggregated sources, 15 have been updated. DGIdb 4.0 also includes improvements to the process of drug normalization and grouping of imported sources. Other notable updates include further development of automatic jobs for routine data updates, more sophisticated query scores for interaction search results, extensive manual curation of interaction source link outs, and the inclusion of interaction directionality. A major focus of this update was integration with crowd-sourced efforts, including leveraging the curation activities of Drug Target Commons, using Wikidata to facilitate term normalization, and integrating into NDEx for producing network representations. Competing Interest Statement The authors have declared no competing interest. Footnotes * ↵† The authors wish it to be known that, in their opinion, the first three authors should be regarded as joint First Authors