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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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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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Updated: Jun 15, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Comparing Sequence-Based and Literature-Based Pathogenicity Scoring Methods for Human Variants.

Luc Mottin1,2, Nona Naderi3, Anaïs Mottaz1,2

  • 1HES-SO/HEG Genève, Information Sciences, Geneva, Switzerland.

Studies in Health Technology and Informatics
|August 23, 2024
PubMed
Summary
This summary is machine-generated.

Accurately classifying genetic variants is crucial for genomic medicine. This study correlates literature-based variant characterizations with pathogenicity scores from SIFT and PolyPhen-2 tools.

Keywords:
PolyPhen-2SIFTText-miningVariant pathogenicity

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Area of Science:

  • Genomic Medicine
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate assessment of genetic variant pathogenicity is vital for clinical decision-making in genomic medicine and precision healthcare.
  • Advancements in high-throughput sequencing have simplified variant identification and characterization.
  • However, ensuring the quality of variant classification remains a challenge, impacting patient outcomes.

Purpose of the Study:

  • To investigate the relationship between genetic variant characterizations found in scientific literature and their predicted pathogenicity scores.
  • To evaluate the consistency of variant assessments using two prominent computational tools: SIFT (Sorting Intolerant From Tolerant) and PolyPhen-2 (Polymorphism Phenotyping v2).

Main Methods:

  • Literature mining to extract genetic variant characterizations.
  • Utilizing SIFT and PolyPhen-2 to compute pathogenicity scores for identified variants.
  • Employing correlation tests to analyze the association between literature-based characterizations and computed pathogenicity scores.

Main Results:

  • Correlation analyses revealed varying degrees of agreement between literature-based variant descriptions and pathogenicity predictions from SIFT and PolyPhen-2.
  • Specific variant features commonly cited in literature showed significant correlations with predicted pathogenicity, though not universally across both tools.
  • The study highlights potential discrepancies in variant interpretation between manual curation and automated prediction.

Conclusions:

  • The findings underscore the importance of robust validation for both literature-based variant information and computational pathogenicity prediction tools.
  • Improving the concordance between variant characterization in literature and computational predictions is essential for reliable clinical application.
  • Further research is needed to refine methods for assessing genetic variant pathogenicity and ensure consistency in genomic medicine.