{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:10:19Z","timestamp":1771467019405,"version":"3.50.1"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T00:00:00Z","timestamp":1771027200000},"content-version":"vor","delay-in-days":13,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"UNSW School of Biomedical Engineering"},{"name":"UNSW BioMedical Machine Learning Laboratory"},{"name":"Australian Research Council Discovery Early Career Researcher","award":["DE220101210"],"award-info":[{"award-number":["DE220101210"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>The American College of Medical Genetics and Genomics\/Association for Molecular Pathology (ACMG\/AMP) guidelines represent the gold standard for clinical variant interpretation. Despite the widespread adoption of ACMG\/AMP guidelines, a comprehensive comparison of the software tools designed to implement them has been lacking. This represents a significant gap, as clinicians require evidence-based guidance on which tools to use in their practice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We benchmarked four ACMG\/AMP-based tools (Franklin, InterVar, TAPES, Genebe) selected from 22 tools, and compared their performance with LIRICAL, a top-performing phenotype-driven tool, using 151 expert-curated datasets from Mendelian disorders. Selection criteria included free availability, VCF compatibility, operational reliability, and not being disease-specific. Our evaluation framework assessed top-N accuracy (N\u2009=\u20091, 5, 10, 20, 50), retention rates, precision, recall, F1 scores, and area under the curve (AUC). Statistical validation employed bootstrap confidence intervals (n\u2009=\u20091000) and Friedman tests. LIRICAL (68.21%) and Franklin (61.59%) demonstrated superior top-10 variant prioritization accuracy in Mendelian disorders, significantly outperforming other tools (P\u2009=\u2009.0000). Results demonstrate that tools with advanced phenotypic integration significantly outperform those relying primarily on genomic features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>All data and source code required to reproduce the findings of this study are openly available in the Code Ocean repository at https:\/\/doi.org\/10.24433\/CO.6562438.v1.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf623","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T12:42:09Z","timestamp":1770295329000},"source":"Crossref","is-referenced-by-count":0,"title":["Comprehensive evaluation of ACMG\/AMP-based variant classification tools"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4746-2320","authenticated-orcid":false,"given":"Tohid","family":"Ghasemnejad","sequence":"first","affiliation":[{"name":"UNSW BioMedical Machine Learning Lab (BML), School of Biomedical Engineering, UNSW Sydney , Sydney, NSW 2052,","place":["Australia"]}]},{"given":"Yuheng","family":"Liang","sequence":"additional","affiliation":[{"name":"UNSW BioMedical Machine Learning Lab (BML), School of Biomedical Engineering, UNSW Sydney , Sydney, NSW 2052,","place":["Australia"]}]},{"given":"Khadijeh Hoda","family":"Jahanian","sequence":"additional","affiliation":[{"name":"Center of Excellence in Precision Medicine and Digital Health, Department of Physiology, Faculty of Dentistry, Chulalongkorn University , Bangkok 10330,","place":["Thailand"]}]},{"given":"Milad","family":"Eidi","sequence":"additional","affiliation":[{"name":"The International ImMunoGeneTics Information System (IMGT), National Center for Scientific Research (CNRS), Institute of Human Genetics (IGH), University of Montpellier (UM) , Montpellier 34396,","place":["France"]}]},{"given":"Arash","family":"Salmaninejad","sequence":"additional","affiliation":[{"name":"Center for Individualized Medicine, Mayo Clinic , Rochester, MN 55905,","place":["United States"]}]},{"given":"Seyedeh Sedigheh","family":"Abedini","sequence":"additional","affiliation":[{"name":"UNSW BioMedical Machine Learning Lab (BML), School of Biomedical Engineering, UNSW Sydney , Sydney, NSW 2052,","place":["Australia"]}]},{"given":"Fabrizzio","family":"Horta","sequence":"additional","affiliation":[{"name":"Fertility & Research Centre, Discipline of Women\u2019s health, School of Clinical Medicine and the Royal Hospital for Women, University of New South Wales , Sydney, NSW 2031,","place":["Australia"]},{"name":"Dept O&G, Monash University , Melbourne, VIC 3168,","place":["Australia"]},{"name":"City Fertility, Research Support Unit , Sydney, NSW 2000,","place":["Australia"]}]},{"given":"Nigel H","family":"Lovell","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, UNSW Sydney , Sydney, NSW 2052,","place":["Australia"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0145-9801","authenticated-orcid":false,"given":"Thantrira","family":"Porntaveetus","sequence":"additional","affiliation":[{"name":"Center of Excellence in Precision Medicine and Digital Health, Department of Physiology, Faculty of Dentistry, Chulalongkorn University , Bangkok 10330,","place":["Thailand"]}]},{"given":"Mark","family":"Grosser","sequence":"additional","affiliation":[{"name":"23Strands Unit 82\/26-32, , Pirrama Rd , Pyrmont, NSW 2009,","place":["Australia"]}]},{"given":"Mahmoud","family":"Aarabi","sequence":"additional","affiliation":[{"name":"Departments of Pathology, and Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of Medicine , Pittsburgh, PA 15213,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2189-9153","authenticated-orcid":false,"given":"Hamid","family":"Alinejad-Rokny","sequence":"additional","affiliation":[{"name":"UNSW BioMedical Machine Learning Lab (BML), School of Biomedical Engineering, UNSW Sydney , Sydney, NSW 2052,","place":["Australia"]},{"name":"Center of Excellence in Precision Medicine and Digital Health, Chulalongkorn University Visiting Scholar (Collaborative Projects), , Bangkok 10330,","place":["Thailand"]}]}],"member":"286","published-online":{"date-parts":[[2026,2,13]]},"reference":[{"key":"2026021820511278900_btaf623-B1","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s00439-017-1860-1","article-title":"Importance of complete phenotyping in prenatal whole exome sequencing","volume":"137","author":"Aarabi","year":"2018","journal-title":"Hum 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