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PreprintCited by 3Genomics and Rare DiseasesGenetic Associations and EpidemiologyGenomics and Phylogenetic Studies

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A predictive framework for stop-loss variants with C-terminal extensions

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Yoon, Jihoon G., Lee, Sojin, Kim, Yoonjung, Gee, Heon Yung, Lee, Min Goo, Lee, Kyung-A·2025·Nucleic Acids Research
DOI 10.1101/2025.09.01.673407

Preprint — published as 10.1093/nar/gkag031 · Paper page

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Abstract

Stop codons dictate translation termination, and variants occurring at these sites can result in stop-loss variants, leading to C-terminal extensions with potentially significant functional consequences. Despite their clinical relevance, existing prediction tools—primarily developed for missense variants—lack sufficient accuracy for assessing stop-loss variants, mainly due to their insufficiency in accounting for the sequence features of the extended peptide. To address this gap, we developed TAILVAR (Terminal codon Analysis and Improved prediction of Lengthened VARiants), a machine-learning classifier that integrates multi-omics features spanning transcript- and protein-level properties, along with variant effect annotations. Our analyses showed that transcripts lacking downstream stop codons in the 3’ untranslated region exhibit lower evolutionary constraints. Additionally, we observed that deleterious variants exhibit greater C-terminal hydrophobicity, which is associated with reduced protein stability and increased degradation, as well as a higher aggregation propensity. TAILVAR outperformed existing benchmarks, demonstrating the highest correlation with functional experiments and establishing thresholds to classify variants as benign or pathogenic. This work offers a systematic framework for interpreting stop-loss variants, providing precise predictions of elongated protein effects that may aid genetic diagnosis and facilitate the discovery of novel disease-associated genes. Graphical abstract:

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Metadata

Authors
Yoon, J. G., Lee, S., Kim, Y., Gee, H. Y., Lee, M. G., & Lee, K.-A.
Affiliations
Yonsei University (KR)
Corresponding author
  • Yoon, Jihoon G.
  • Lee, Kyung-A
ORCID
Venue
Nucleic Acids Research
Publisher
openRxiv
Published
2025-09-04
Volume
54
Issue
2
ISSN
0305-1048 (ISSN-L)
DOI
10.1101/2025.09.01.673407
OpenAlex
W7124912955
Cited by
3
References
1
Citations by year
2026: 3
FWCI
11.78
Type
journal article
Language
en
Open access
Licence
Locations
  • Nucleic Acids Research (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
the Korea Health Industry Development Institute (RS-2025-02214700)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
C-terminal extension, variant effect prediction, machine learning classifiers, 3' untranslated region, evolutionary constraints, protein stability, protein aggregation, hydrophobicity, loss of function, gain-of-function, genetic diagnosis, disease-associated genes
MeSH terms
Machine Learning, Prediction Algorithms, Humans, Peptide Chain Termination, Translational, Codon, Terminator, 3' Untranslated Regions, Hydrophobic and Hydrophilic Interactions

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