BACKGROUND: The adoption of whole genome and exome sequencing (WGS/WES) is expanding rapidly, yet variant interpretation remains a critical bottleneck, limiting diagnostic yield and delaying precision care. We present ACVI-Med (Accelerated Candidate Variant Interpretation in Medicine), an open-source, on-premise platform for comprehensive analysis of genomic data. RESULTS: ACVI-Med integrates phenotype-aware filtering, ACMG-guided variant classification, and secondary findings detection into a unified, containerized system built for reproducibility and deployment across diverse institutional environments. Designed for accessibility, ACVI-Med enables users without extensive bioinformatics expertise to interpret variants from VCF (Variant Call Format) files through a flexible interface supporting phenotype-driven filtering, ACMG-guided variant classification, and integration of population, clinical, and predictive annotations. The software incorporates modules for secondary findings detection, aligned with ACMG Secondary Findings v3.2 recommendations, extending its utility beyond primary variant analysis. ACVI-Med’s customizable filtering logic, real-time access to curated knowledge bases, and interactive visualization tools support transparent and reproducible genomic interpretation. CONCLUSION: ACVI-Med is an open-source software for genomic variant interpretation. Unlike commercial solutions, ACVI-Med is cost-effective, containerized, and can be institutionally self-hosted, reducing barriers to adoption in research settings.

MOTIVATION: ReactomeGSA is part of the Reactome knowledgebase and one of the leading multi-omics pathway analysis platforms. ReactomeGSA provides access to quantitative pathway analysis methods supporting different 'omics data types. Additionally, ReactomeGSA can process different datasets simultaneously, leading to a comparative pathway analysis that can also be performed across different species. RESULTS: We present a major update to the ReactomeGSA analysis platforms that greatly simplifies the reuse and direct integration of public data. In order to increase the number of available datasets, we developed the new grein_loader Python application that can directly fetch experiments from the GREIN resource. This enabled us to support both EMBL-EBI's Expression Atlas and GEO RNA-seq Experiments Interactive Navigator within ReactomeGSA. To further increase the visibility and simplify the reuse of public datasets, we integrated a novel search function into ReactomeGSA that enables users to search for public datasets across both supported resources. Finally, we completely re-developed ReactomeGSA's web-frontend and R/Bioconductor package to support the new search and loading features, and greatly simplify the use of ReactomeGSA. AVAILABILITY AND IMPLEMENTATION: The new ReactomeGSA web frontend is available at https://www.reactome.org/gsa with an built-in, interactive tutorial. The ReactomeGSA R package (https://bioconductor.org/packages/release/bioc/html/ReactomeGSA.html) is available through Bioconductor and shipped with detailed documentation and vignettes. The grein_loader Python application is available through the Python Package Index (pypi). The complete source code for all applications is available on GitHub at https://github.com/grisslab/grein_loader and https://github.com/reactome.