Evidence map›Paper›PMID 41688871›Full record

ArticleBioinformatics (Oxford, England)2026

Comprehensive evaluation of ACMG/AMP-based variant classification tools.

Tohid Ghasemnejad, Yuheng Liang, Khadijeh Hoda Jahanian, Milad Eidi, Arash Salmaninejad, Seyedeh Sedigheh Abedini, Fabrizzio Horta, Nigel H Lovell, Thantrira Porntaveetus, Mark Grosser and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Pathogenicity assessment of genetic variants in reproductive disorders.Journal of assisted reproduction and genetics · 2026
    Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Tohid GhasemnejadUNSW BioMedical Machine Learning Lab (BML), School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia.ORCID 0000-0002-4746-2320
Yuheng LiangUNSW BioMedical Machine Learning Lab (BML), School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia.
Khadijeh Hoda JahanianCenter of Excellence in Precision Medicine and Digital Health, Department of Physiology, Faculty of Dentistry, Chulalongkorn University, Bangkok 10330, Thailand.
Milad EidiThe International ImMunoGeneTics Information System (IMGT), National Center for Scientific Research (CNRS), Institute of Human Genetics (IGH), University of Montpellier (UM), Montpellier 34396, France.
Arash SalmaninejadCenter for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, United States.
Seyedeh Sedigheh AbediniUNSW BioMedical Machine Learning Lab (BML), School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia.
Fabrizzio HortaFertility & Research Centre, Discipline of Women's health, School of Clinical Medicine and the Royal Hospital for Women, University of New South Wales, Sydney, NSW 2031, Australia.
Nigel H LovellSchool of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia.
Thantrira PorntaveetusCenter of Excellence in Precision Medicine and Digital Health, Department of Physiology, Faculty of Dentistry, Chulalongkorn University, Bangkok 10330, Thailand.ORCID 0000-0003-0145-9801
Mark GrosserUnit 82/26-32, 23Strands, Pirrama Rd, Pyrmont, NSW 2009, Australia.
Mahmoud AarabiDepartments of Pathology, and Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, United States.
Hamid Alinejad-RoknyUNSW BioMedical Machine Learning Lab (BML), School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia.ORCID 0000-0002-2189-9153

Funding

Australian Research Council Discovery Early Career Researcher DE220101210UNSW BioMedical Machine Learning LaboratoryUNSW School of Biomedical Engineering
6 · The paper itself

Abstract

motivationThe 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.

resultsWe 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 = 1, 5, 10, 20, 50), retention rates, precision, recall, F1 scores, and area under the curve (AUC). Statistical validation employed bootstrap confidence intervals (n = 1000) 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 = .0000). Results demonstrate that tools with advanced phenotypic integration significantly outperform those relying primarily on genomic features. AVAILABILITY AND IMPLEMENTATION: 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.

Indexed as

Genetic VariationGenomicsSoftwareDatabases, GeneticHumans

Identifiers

PMID41688871
PMCPMC12916173

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.