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Translation

Translation

Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of Life
Proteins are...

Leaky Scanning

Leaky Scanning

During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA. Marilyn Kozak discovered that the sequence RCCAUGG (where R...

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Related Experiment Video

Updated: Jun 10, 2025

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

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

Published on: January 16, 2019

Performance of a Protein Language Model for Variant Annotation in Cardiac Disease.

Aviram Hochstadt¹, Chirag Barbhaiya¹, Anthony Aizer¹

¹NYU Langone Health and the NYU Grossman School of Medicine New York NY.

Journal of the American Heart Association

|October 11, 2024

Summary

This summary is machine-generated.

Artificial intelligence, specifically AlphaMissense accessed via VarCard.io, significantly improves the interpretation of genetic variants in cardiac conditions. This tool effectively reclassifies many variants of unknown significance, enhancing diagnostic accuracy in cardiovascular genetics.

Keywords:

AlphaMissense VarCard.Io artificial intelligence genetic testing genotype phenotype variants of unknown significance

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Last Updated: Jun 10, 2025

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

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

Published on: January 16, 2019

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Area of Science:

Cardiovascular Genetics
Bioinformatics
Medical Artificial Intelligence

Background:

Genetic testing is crucial for diagnosing cardiac diseases, but many variants are of unknown significance (VUS).
The AlphaMissense AI model predicts missense variant pathogenicity, offering a potential solution to VUS challenges.
This study evaluates AlphaMissense's real-world performance in a cardiovascular genetics setting using the VarCard.io platform.

Purpose of the Study:

To assess the performance of AlphaMissense, via VarCard.io, in classifying genetic variants within a cardiovascular genetics program.
To compare the reclassification rate and genotype-phenotype concordance of AlphaMissense with existing classification systems like ClinVar and Franklin.
To determine the utility of AlphaMissense in reclassifying variants of unknown significance in inherited cardiac conditions.

Main Methods:

Evaluated 339 missense variants from 266 patients with heritable cardiac diseases using AlphaMissense via VarCard.io.
Compared AlphaMissense classifications with ClinVar and Franklin (Genoox) variant classification platforms.
Analyzed mutation reclassification rates and genotype-phenotype concordance.

Main Results:

230 out of 339 variants (67.8%) were initially classified as VUS or unclassified by ClinVar.
VarCard.io reclassified 86.1% of VUS, significantly higher than Franklin (34.8%, P<0.001).
Genotype-phenotype concordance using VarCard.io predictions was high at 95.9%, with 90.5% concordance for ClinVar-classified variants.

Conclusions:

AlphaMissense, accessed through VarCard.io, demonstrates high efficiency in interpreting cardiac genetic variants.
The tool effectively reclassifies variants of unknown significance, potentially improving the utility of cardiac genetic testing.
VarCard.io's performance suggests it can significantly aid clinicians in cardiovascular genetics.