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Related Concept Videos

Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...
Nuclear Export of mRNA02:31

Nuclear Export of mRNA

Before mRNAs are exported to the cytoplasm, it is crucial to check each mRNA for structural and functional integrity. Eukaryotic cells use several different mechanisms, collectively known as mRNA surveillance, to look for irregularities in mRNAs. Irregular or aberrant mRNA are rapidly degraded by various enzymes. If a defective mRNA escapes the surveillance, it would be translated into a protein which would either be non-functional or not function properly. One of the primary irregularities in...

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Related Experiment Video

Updated: Jul 12, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Unveiling the Hidden Rules: Enhancing NMD Prediction for Protein-Truncating Variants.

Iman Egab, Jacob Schmidt, Michael Cortázar

    Biorxiv : the Preprint Server for Biology
    |July 10, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Nonsense-mediated decay (NMD) prediction for premature termination codons is improved by a new classifier, TrunCat. This tool enhances the interpretation of genetic variants, aiding in understanding disease mechanisms.

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    Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
    07:15

    Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

    Published on: January 16, 2019

    Area of Science:

    • Genetics
    • Molecular Biology
    • Bioinformatics

    Background:

    • Nonsense-mediated decay (NMD) is a crucial RNA quality control pathway.
    • Premature termination codons (PTCs) trigger NMD, degrading aberrant transcripts.
    • Accurate prediction of NMD is vital for interpreting genetic variants, especially those causing truncated proteins.

    Purpose of the Study:

    • To develop a predictive model for NMD sensitivity of transcripts with PTCs.
    • To improve upon the limitations of the canonical 50-55 nucleotide rule in predicting NMD outcomes.
    • To create a scalable resource for interpreting the functional impact of protein-truncating variants.

    Main Methods:

    • Utilized paired whole-genome and RNA-sequencing data from 10,306 individuals in the Trans-Omics for Precision Medicine (TOPMed) program.
    • Quantified NMD efficiency for 5,749 germline truncating variants using allele-specific expression analysis.
    • Trained a gradient-boosting classifier (TrunCat) incorporating features like SHAP values to predict NMD sensitivity.

    Main Results:

    • The TrunCat classifier achieved approximately 78% ROC-AUC in distinguishing NMD-sensitive from NMD-escape transcripts.
    • A simplified model using top SHAP features demonstrated comparable predictive performance.
    • Application to variant databases and a rare-disease cohort showed differential NMD predictions for pathogenic versus uncertain significance variants.

    Conclusions:

    • The developed TrunCat model significantly enhances the prediction of NMD outcomes for PTC-containing transcripts.
    • This framework validates the canonical NMD rule while uncovering novel determinants of NMD sensitivity.
    • Provides a valuable, scalable tool for genetic variant interpretation and understanding disease associations.