biostudies-literatureBridges YNational Human Genome Research Institute Phenomics First Resource, NIH-NHGRIUS Department of Energy, Office of Science, Office of Basic Energy SciencesNHGRI NIH HHSNLM NIH HHSOffice of the Director, National Institutes of HealthNIH HHS87https://www.ebi.ac.uk/biostudies/studies/S-EPMC11929307biostudies-literatureUnknown26(1)<h4>Background</h4>Computational approaches to support rare disease diagnosis are challenging to build, requiring the integration of complex data types such as ontologies, gene-to-phenotype associations, and cross-species data into variant and gene prioritisation algorithms (VGPAs). However, the performance of VGPAs has been difficult to measure and is impacted by many factors, for example, ontology structure, annotation completeness or changes to the underlying algorithm. Assertions of the capabilities of VGPAs are often not reproducible, in part because there is no standardised, empirical framework and openly available patient data to assess the efficacy of VGPAs-ultimately hindering the development of effective prioritisation tools.<h4>Results</h4>In this paper, we present our benchmarkBMC bioinformaticsTowards a standard benchmark for phenotype-driven variant and gene prioritisation algorithms: PhEval - Phenotypic inference Evaluation framework.PMC11929307RM1 HG010860#5R24 OD011883DE-AC02-05CH11231T15 LM009451R24 OD011883#5RM1 HG010860Smedley DOdell AMarinakis NMHarris NLCortes KGMungall CJSouza VJacobsen JOBMcLaughlin JARobinson PNKorn DRHaendel MBridges YMatentzoglu NOsumi-Sutherland DfalseTowards a standard benchmark for phenotype-driven variant and gene prioritisation algorithms: PhEval - Phenotypic inference Evaluation framework.<h4>Background</h4>Computational approaches to support rare disease diagnosis are challenging to build, requiring the integration of complex data types such as ontologies, gene-to-phenotype associations, and cross-species data into variant and gene prioritisation algorithms (VGPAs). However, the performance of VGPAs has been difficult to measure and is impacted by many factors, for example, ontology structure, annotation completeness or changes to the underlying algorithm. Assertions of the capabilities of VGPAs are often not reproducible, in part because there is no standardised, empirical framework and openly available patient data to assess the efficacy of VGPAs-ultimately hindering the development of effective prioritisation tools.<h4>Results</h4>In this paper, we present our benchmark2025-01-01T00:00:00Z2025 Mar2026-04-08T19:53:24.331Z2025-07-12T03:04:16.051ZS-EPMC119293074012147910.1186/s12859-025-06105-4