{"doi":"10.1101/2025.05.16.654507","title":"acmgscaler: An R package and Colab for standardised gene-level variant effect score calibration within the ACMG/AMP framework","abstract":"<jats:title>Abstract</jats:title>\n                <jats:sec>\n                  <jats:title>Summary</jats:title>\n                  <jats:p>\n                    A genome-wide variant effect calibration method was recently developed under the guidelines of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP), following ClinGen recommendations for variant classification. While genome-wide approaches offer clinical utility, emerging evidence highlights the need for gene- and context-specific calibration to improve accuracy. Building on previous work, we have developed an algorithm tailored to converting functional scores from both multiplexed assays of variant effects (MAVEs) and computational variant effect predictors (VEPs) into ACMG/AMP evidence strengths. Our method is designed to deliver consistent performance across different genes and score distributions, with all variables adaptively determined from the input data, preventing selective adjustments or overfitting that could inflate evidence strengths beyond empirical support. To facilitate adoption, we introduce\n                    <jats:monospace>acmgscaler</jats:monospace>\n                    , a lightweight R package and a plug-and-play Google Colab notebook for the calibration of custom datasets. This algorithmic framework bridges the gap between MAVEs/VEPs and clinically actionable variant classification.\n                  </jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Availability</jats:title>\n                  <jats:p>\n                    The package and the Colab notebook are available on GitHub:\n                    <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://github.com/badonyi/acmgscaler\">https://github.com/badonyi/acmgscaler</jats:ext-link>\n                    .\n                  </jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Supplementary data</jats:title>\n                  <jats:p>\n                    Supplementary data can be downloaded from the OSF repository\n                    <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://osf.io/7hjnm\">https://osf.io/7hjnm</jats:ext-link>\n                    .\n                  </jats:p>\n                </jats:sec>","journal":null,"year":null,"id":633108,"datarank":0.24141568686511508,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"self_citation_contribution":0.24141568686511508,"citation_network_contribution":0.0,"self_endowment_contribution":0.24141568686511508,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1091732,"name":"Joseph A. Marsh","orcid":"0000-0003-4132-0628","position":1,"is_corresponding":false},{"id":1365063,"name":"Mihaly Badonyi","orcid":"0000-0002-8305-5618","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"acmgscaler: An R package and Colab for standardised gene-level variant effect score calibration within the ACMG/AMP framework","abstract":"<jats:title>Abstract</jats:title>\n                <jats:sec>\n                  <jats:title>Summary</jats:title>\n                  <jats:p>\n                    A genome-wide variant effect calibration method was recently developed under the guidelines of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP), following ClinGen recommendations for variant classification. While genome-wide approaches offer clinical utility, emerging evidence highlights the need for gene- and context-specific calibration to improve accuracy. Building on previous work, we have developed an algorithm tailored to converting functional scores from both multiplexed assays of variant effects (MAVEs) and computational variant effect predictors (VEPs) into ACMG/AMP evidence strengths. Our method is designed to deliver consistent performance across different genes and score distributions, with all variables adaptively determined from the input data, preventing selective adjustments or overfitting that could inflate evidence strengths beyond empirical support. To facilitate adoption, we introduce\n                    <jats:monospace>acmgscaler</jats:monospace>\n                    , a lightweight R package and a plug-and-play Google Colab notebook for the calibration of custom datasets. This algorithmic framework bridges the gap between MAVEs/VEPs and clinically actionable variant classification.\n                  </jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Availability</jats:title>\n                  <jats:p>\n                    The package and the Colab notebook are available on GitHub:\n                    <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://github.com/badonyi/acmgscaler\">https://github.com/badonyi/acmgscaler</jats:ext-link>\n                    .\n                  </jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Supplementary data</jats:title>\n                  <jats:p>\n                    Supplementary data can be downloaded from the OSF repository\n                    <jats:ext-link xmlns:xlink=\"http://www.w3.org/1999/xlink\" ext-link-type=\"uri\" xlink:href=\"https://osf.io/7hjnm\">https://osf.io/7hjnm</jats:ext-link>\n                    .\n                  </jats:p>\n                </jats:sec>","is_dataset_classified":null,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19910364","pmcid":null,"openalex_id":"https://openalex.org/W4410580128","authors":[],"funders":[{"funder_name":"European Research Council","grant_id":"101001169","title":"Protein Structure, Molecular Mechanisms and Human Genetic Disease: Beyond the Loss-of-function Paradigm"},{"funder_name":"Medical Research Council, Human Genetics Unit","grant_id":"MC_UU_00035/9","title":"Protein Variant Interpretation"}],"total_grants":2,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2025,"count":4}],"oa_status":"green","license":"cc-by-nc","oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2025/05/21/2025.05.16.654507.full.pdf","host_type":"repository"},{"url":"https://www.biorxiv.org/content/biorxiv/early/2025/05/21/2025.05.16.654507.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2025.05.16.654507","host_type":"publisher"},{"url":"https://doi.org/10.1101/2025.05.16.654507","host_type":"repository"},{"url":"https://osf.io/7hjnm","host_type":"repository"},{"url":"https://europepmc.org/article/PPR/PPR1023919","host_type":"Europe_PMC"},{"url":"https://europepmc.org/api/fulltextRepo?pprId=PPR1023919&type=FILE&fileName=EMS205782-pdf.pdf&mimeType=application/pdf","host_type":"Europe_PMC"},{"url":"https://doi.org/10.1093/bioinformatics/btaf503","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/40929116","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/40929116/","host_type":""},{"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC12496131/","host_type":""},{"url":"https://www.pure.ed.ac.uk/ws/files/563816580/btaf503_1_.pdf","host_type":""},{"url":"https://hdl.handle.net/20.500.11820/2a3fbdee-27be-4676-b755-feeaa5d4d0ca","host_type":""},{"url":"https://www.research.ed.ac.uk/en/publications/2a3fbdee-27be-4676-b755-feeaa5d4d0ca","host_type":""},{"url":"http://dx.doi.org/10.1101/2025.05.16.654507","host_type":""}],"fields_of_study":["Genomics and Rare Diseases","Genetics and Neurodevelopmental Disorders","Genetic Associations and Epidemiology","0206 medical engineering","02 engineering and technology"],"mesh_terms":[],"keywords":["Calibration","R package","Computer science","Internal medicine","Medicine","Statistics","Mathematics","Original Paper","Humans","Genetic Variation","Genomics","Software","Algorithms","Genomics/methods"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T11:00:38.369943Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}