PanelAppRex aggregates disease gene panels and facilitates sophisticated search

Motivation Gene panel data are essential for variant interpretation and genomic diagnostics, but existing resources are fragmented, inconsistently annotated, and not easily accessible for programmatic use. We developed PanelAppRex, a harmonised dataset and interactive search tool that integrates over 58,000 curated gene-disease panel associations. It supports natural language-style queries by gene, phenotype, disease group, and mode of inheritance (MOI), with results returned in machine-readable export formats.

The resulting dataset includes standardised gene identifiers, disease annotations, MOI, and literature support, enabling seamless integration into bioinformatic pipelines. We benchmarked fifteen case studies spanning immunology, neurology, and additional disease areas. Under the recommended usage, in which the union of returned panels is considered, the causal gene was recovered in every case. Across all returned panels, the causal gene was present in 85.6% of panels. For manual interface interpretation, the causal gene was present in the user-selected best-fit panel(s) in all fifteen benchmarked cases. Availability The platform data is openly available at PanelAppRex base [Data set], Zenodo https://doi.org/10.5281/zenodo.15736689, with source code at https://github.com/DylanLawless/PanelAppRex, and demonstration page at https://panelapprex.github.io/landing_page. The dataset is maintained for a minimum of two years following publication. Competing Interest Statement The authors have declared no competing interest. Funding Statement This project was supported through the grant Swiss National Science Foundation (SNF) 320030_201060, and NDS-2021-911 (SwissPedHealth) from the Swiss Personalized Health Network and the Strategic Focal Area 'Personalized Health and Related Technologies' of the ETH Domain (Swiss Federal Institutes of Technology). Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes ↵1 The quantitative omic epidemiology group. Expanded from 5 to 15 case studies. Added the complete RAG AI demo. Changed the demo link to a standalone, neutral URL. Refined claims and wording. Acronyms - AD - Autosomal dominant - AR - Autosomal recessive - API - Application Programming Interface - CSV - comma-separated values - GE - Genomics England - HGNC - Human Genome Organisation Gene Nomenclature Committee - IEI - Inborn Errors of Immunity - IEM - Inborn Errors of Metabolism - HTML - HyperText Markup Language - JACI - Journal of Allergy and Clinical Immunology - MOI - Mode of Inheritance - OMIM - Online Mendelian Inheritance in Man - Portable Document Format - PID - Primary Immunodeficiency - RAG - Retrieval-Augmented Generation - RDS - R Data Serialization format - TSV - Tab-Separated Values