ACMG Validator

Review the evidence,
not just the verdict.

Variant interpretation often ends with a classification. Clinical review needs the record behind it. ACMG Validator helps you check every ACMG/AMP criterion, record evidence, confirm non-applicable criteria, and export a structured report.

Private by design. Nothing leaves your browser.

Examples

0Pathogenic points

0Benign points

0Total points

—Point-based category

Incomplete review

Validation status

No criteria addressed yet.

✓ Applied — contributes points ∅ No contribution — reviewed, adds nothing ! Incomplete — something missing ⊘ Conflict — needs resolving – Not assessed — not yet reviewed

Manual entry

Batch upload entry · Load ACMG Validator JSON

Optional import for saved ACMG Validator JSON reports. Most users can complete the form manually. Files are read locally in your browser and are not uploaded. Multi-variant JSON imports are limited to the first five variants; contact admin@switzerlandomics.ch for a licence for unlimited joint variant input.

Choose JSON file

Drag and drop an ACMG Validator JSON file here, or click to select a local file.

Supports exported single-variant reports, multi-variant case reports, and example JSON files.

No JSON file selected.

1 · Variant and case context

Use HGVS notation. Missing transcript or genome build is flagged because interpretation can change with reference sequence and assembly.

Gene Transcript cDNA (HGVS c.) Protein (HGVS p.) Genome build Genomic coordinate Variant type Zygosity Disease / condition Inheritance model Phenotype summary / HPO Reviewer Institution Review date Guideline basis

Sample provenance and sequencing metadata

Optional case-level metadata for the sample, sequencing run, instrument, analysis files and prior sharing events. Use structured SPHN WGS keys where available, or paste JSON / TSV from an upstream system. You can inspect VCF header metadata separately in the VCFheader browser.

Paste SPHN gv_wgs JSON, or TSV/CSV key-value lines

Case and sample

Identifier Genome ID Sample ID Sample primary sample type Sample q30

Sequencing assay

Assay identifier Assay code Assay intended read depth Assay intended read length Assay library preparation Assay source system Assay sop Assay start datetime Assay subject pseudo identifier

Sequencing run and instrument

Run identifier Run datetime Run average insert size Run average read length Run mean read depth Run read count Instrument code Instrument label

Sequencing analysis

Analysis identifier Analysis code Analysis reference sequence Analysis software Analysis source system Analysis sop Analysis start datetime Analysis subject pseudo identifier

Files, checksums and sharing

Files fastq MD5 fastq Size fastq Files vcf MD5 vcf Size vcf Share date Share type Share recipient

Non-standard imported provenance keys

2 · Criteria review

Record the evidence finding first, then the status — the status auto-fills from the evidence and you can override it. Each criterion also carries a default strength fixed by ACMG/AMP; you may modulate it when justified (e.g. PVS1 per the ClinGen decision tree, PM2 to Supporting), and any deviation is recorded in ClinGen notation (PM2_Supporting). "Rejected, absent after review" is not "Not assessed": use the status pill and the completeness bar to confirm nothing was skipped. Many criteria will not apply to a given variant — use Mark untouched as N/A to record that deliberately.

Pathogenic evidence

Benign evidence

3 · Classification and sign-off

Entered classification Reviewer sign-off completed — evidence reviewed and recorded as above

4 · Genotype-level review

Use this when a case has more than one variant. Variant-level ACMG evidence remains separate; this section records whether the variants were reviewed together, phase, recessive evidence and phenotype fit. Do not sum ACMG scores across variants.

Gene reviewed together Disease / condition Inheritance model Phase Variant combination Phase evidence Genotype-level conclusion Phenotype fit Residual uncertainty

Download the report

A structured, shareable record of this review — PDF for sign-off, HTML or JSON for your records.

About the ACMG criteria and this validator

The ACMG/AMP framework classifies sequence variants into five categories — pathogenic, likely pathogenic, uncertain significance, likely benign and benign — using weighted evidence criteria such as PVS1, PS1–PS4, PM1–PM6, PP1–PP4 on the pathogenic side and BA1, BS1–BS4, BP1–BP7 on the benign side. Each criterion carries a default strength: very strong, strong, moderate or supporting (BA1 is stand-alone benign).

The strength encoded in the code is the default, but it can be modulated on professional judgement, and modern practice does so in structured ways: the ClinGen Sequence Variant Interpretation working group grades PVS1 by null-variant context (PVS1 decision tree), recommends PM2 at supporting strength, and calibrates computational evidence (PP3/BP4) and functional evidence (PS3/BS3) to multiple strength levels. The Tavtigian et al. (2020) point system makes this coherent by scoring the applied strength rather than the code: supporting ±1, moderate ±2, strong ±4, very strong / stand-alone ±8, with categories of pathogenic ≥10, likely pathogenic 6–9, uncertain 0–5, likely benign −1 to −6, benign ≤−7.

This tool does not annotate variants and does not replace VarSome, ClinVar, gnomAD or VEP. It records how the evidence those tools provide was reviewed: which criteria apply, at what strength and why, which were checked and found absent, which were never assessed, whether caveats were completed, and whether the entered classification is internally consistent with the selected criteria. The output certifies process completion, not clinical correctness. A "validated" report means the review is internally consistent with the selected criteria and that caveats and sign-off were completed — it is not a clinical approval.

Feedback

Feedback on this page is important to catch errors in logic or to better meet user needs. Please contact us at admin@switzerlandomics.ch to report.

Variant evidence resources

Common resources used during variant review. These links are provided for convenience only. Switzerland Omics does not endorse, control, validate, or guarantee any external database or interpretation. For automated evidence quantification see QuantBayes.

ClinVar ClinGen Evidence Repository ClinGen Criteria Specifications gnomAD GeneBe VarSome OMIM dbSNP UCSC Genome Browser Ensembl VEP SpliceAI Lookup Mutalyzer LOVD PanelApp COSMIC OncoKB

References

Richards S, et al. (2015). Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genetics in Medicine, 17(5), 405–423. doi:10.1038/gim.2015.30
Tavtigian SV, Harrison SM, Boucher KM, Biesecker LG. (2020). Fitting a naturally scaled point system to the ACMG/AMP variant classification guidelines. Human Mutation, 41, 1734–1737. doi:10.1002/humu.24088
Wilcox EH, et al. (2022). Evaluating the impact of in silico predictors on clinical variant classification. Genetics in Medicine, 24(4), 924–930. doi:10.1016/j.gim.2021.11.018
Li MM, et al. (2017). Standards and guidelines for the interpretation and reporting of sequence variants in cancer: a joint consensus recommendation of AMP, ASCO and CAP. The Journal of Molecular Diagnostics, 19(1), 4–23. doi:10.1016/j.jmoldx.2016.10.002
Riggs ER, et al. (2020). Technical standards for the interpretation and reporting of constitutional copy-number variants: a joint consensus recommendation of ACMG and ClinGen. Genetics in Medicine, 22(2), 245–257. doi:10.1038/s41436-019-0686-8
Pedersen BS, et al. (2021). Effective variant filtering and expected candidate variant yield in studies of rare human disease. npj Genomic Medicine, 6(1), 1–8. doi:10.1038/s41525-021-00227-3
Li Q, Wang K. (2017). InterVar: clinical interpretation of genetic variants by the 2015 ACMG-AMP guidelines. The American Journal of Human Genetics, 100(2), 267–280. doi:10.1016/j.ajhg.2017.01.004
Xavier A, et al. (2019). TAPES: a tool for assessment and prioritisation in exome studies. PLoS Computational Biology, 15(10), e1007453. doi:10.1371/journal.pcbi.1007453
van der Horst, E.; Unni, D.; Kopmels, F.; Armida, J.; Touré, V.; Franke, W.; Crameri, K.; Cirillo, E.; Österle, S. (2023) “Bridging Clinical and Genomic Knowledge: An Extension of the SPHN RDF Schema for Seamless Integration and FAIRification of Omics Data.” Preprints.org.doi:10.20944/preprints202312.0373.v1

Public ACMG implementations such as InterVar and TAPES are listed for reference only; they are not implemented here and no assertion of their quality is made.