Evidence map›Paper›PMID 41551927›Full record

ArticleNAR genomics and bioinformatics2026

SyMetrics: an integrated machine learning model for evaluating the pathogenicity of synonymous variants in the human genome.

Linnaeus Bundalian, Martina Schmidt Strnadová, Felix Garten, Susanne Horn, Udo Stenzel, Denny Popp, Johannes R Lemke, Saskia Biskup, Björn Schulte, Patrick May and 8 more

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Article in NAR genomics and bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors.

Linnaeus BundalianInstitute of Human Genetics, University of Leipzig Medical Center, Leipzig, Saxony 04103, Germany.ORCID https://orcid.org/0009-0004-7460-4330
Martina Schmidt StrnadováRudolf Schönheimer Institute of Biochemistry, Medical Faculty, University of Leipzig, Leipzig, Saxony 04103, Germany.
Felix GartenInstitute of Human Genetics, University of Leipzig Medical Center, Leipzig, Saxony 04103, Germany.
Susanne HornRudolf Schönheimer Institute of Biochemistry, Medical Faculty, University of Leipzig, Leipzig, Saxony 04103, Germany.ORCID https://orcid.org/0000-0002-2902-0377
Udo StenzelRudolf Schönheimer Institute of Biochemistry, Medical Faculty, University of Leipzig, Leipzig, Saxony 04103, Germany.
Denny PoppInstitute of Human Genetics, University of Leipzig Medical Center, Leipzig, Saxony 04103, Germany.
Johannes R LemkeInstitute of Human Genetics, University of Leipzig Medical Center, Leipzig, Saxony 04103, Germany.
Saskia BiskupCeGaT GmbH, Tübingen, Baden-Württemberg 72076, Germany.
Björn SchulteCeGaT GmbH, Tübingen, Baden-Württemberg 72076, Germany.
Patrick MayLuxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette 4365, Luxembourg.ORCID https://orcid.org/0000-0001-8698-3770
Frank BösebeckAgaplesion Diakonie Clinic Rottenburg, Rottenburg, Lower Saxony 27356, Germany.
Antje GartenHospital for Children and Adolescents and Center for Pediatric Research (CPL), University of Leipzig, Leipzig, Saxony 04103, Germany.
Doreen ThorRudolf Schönheimer Institute of Biochemistry, Medical Faculty, University of Leipzig, Leipzig, Saxony 04103, Germany.
Angela SchulzRudolf Schönheimer Institute of Biochemistry, Medical Faculty, University of Leipzig, Leipzig, Saxony 04103, Germany.
Julia HentschelInstitute of Human Genetics, University of Leipzig Medical Center, Leipzig, Saxony 04103, Germany.
Janet KelsoDepartment of Evolutionary Genetics, Max Planck Institute for Evolutionary Anthropology, Leipzig, Saxony 04103, Germany.ORCID https://orcid.org/0000-0002-3618-322X
Torsten SchönebergRudolf Schönheimer Institute of Biochemistry, Medical Faculty, University of Leipzig, Leipzig, Saxony 04103, Germany.
Diana Le DucInstitute of Human Genetics, University of Leipzig Medical Center, Leipzig, Saxony 04103, Germany.ORCID https://orcid.org/0000-0001-7289-2552

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Synonymous single nucleotide variants (sSNVs), traditionally seen as neutral, are now recognized for their biological impact. To assess their relevance, we developed SyMetrics, a framework that integrates predictors of splicing, RNA stability, evolutionary conservation, codon usage, synonymous variation effects, sequence properties, and allele frequency. We analyzed all possible sSNVs across the human genome, and our machine-learning model achieved 97% accuracy in distinguishing deleterious from benign variants, with a ROC-AUC of 0.89, outperforming individual predictors. Our estimates indicate that about 1.98 ± 0.17% of sSNVs absent from population databases are damaging (roughly 900 000 sSNVs), with an odds ratio of 3.87 for deleteriousness compared to common sSNVs (

Indexed as

Genome, HumanMachine LearningPolymorphism, Single NucleotideSoftwareHumans

Identifiers

PMID41551927
PMCPMC12805901

What Socratic holds

Textmetadata
LicenceCC BY-NC
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.