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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
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Updated: Sep 25, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Classification of non-coding variants with high pathogenic impact.

Lambert Moyon1, Camille Berthelot1, Alexandra Louis1

  • 1Ecole Normale SupĂ©rieure, PSL Research University, CNRS, Inserm, Institut de Biologie de l'Ecole Normale SupĂ©rieure (IBENS), Paris, France.

Plos Genetics
|April 29, 2022
PubMed
Summary
This summary is machine-generated.

FINSURF is a new machine-learning tool that predicts the functional impact of non-coding DNA variants. This advance helps identify genetic causes of disease, improving diagnosis for patients receiving whole genome sequencing (WGS).

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Whole genome sequencing (WGS) is crucial for diagnosing genetic disorders.
  • Most WGS diagnoses focus on protein-coding mutations, leaving non-coding variants largely uninterpreted.
  • Unidentified non-coding variants limit WGS utility, failing to diagnose 20-80% of patients.

Purpose of the Study:

  • To develop a machine-learning approach, FINSURF, for predicting the functional impact of non-coding variants.
  • To improve the identification of disease-causing non-coding variants.
  • To enhance the diagnostic yield of whole genome sequencing.

Main Methods:

  • FINSURF utilizes machine learning to predict variant impact in regulatory regions.
  • The method incorporates optimized control variant selection for improved training.
  • Variant scores are decomposed into contributions from individual annotations for interpretability.

Main Results:

  • FINSURF demonstrates superior performance compared to existing state-of-the-art methods.
  • The tool successfully identified causative non-coding variants within the top 10 predictions in 22 out of 30 diverse diseases.
  • FINSURF provides interpretable scores, aiding functional relevance evaluation.

Conclusions:

  • FINSURF offers an efficient solution for prioritizing non-coding variants in clinical settings.
  • The tool enhances the ability to diagnose genetic diseases with non-coding mutations.
  • FINSURF is available as an online server and custom browser tracks for broad accessibility.