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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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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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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.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Genome-wide Association Studies-GWAS01:11

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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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Evolutionary Relationships through Genome Comparisons02:54

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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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Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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Updated: Oct 29, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Classifying single nucleotide polymorphisms in humans.

Shima Azizzadeh-Roodpish1, Max H Garzon2, Sambriddhi Mainali1

  • 1Computer Science, The University of Memphis, Memphis, TN, 38152, USA.

Molecular Genetics and Genomics : MGG
|July 14, 2021
PubMed
Summary
This summary is machine-generated.

New tests accurately classify single nucleotide polymorphisms (SNPs), a common genetic variation, distinguishing disease-causing from benign types. This advances personalized medicine by improving SNP assessment in coding and noncoding regions.

Keywords:
Digital genomic signatureGibbs free energyHybridizationMachine learningNonpathogenic/benign SNPPathogenic/malign SNPh-Distance

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Single nucleotide polymorphisms (SNPs) represent the most prevalent form of human genetic variation.
  • SNPs are crucial for advancing personalized medicine and understanding disease etiology.
  • Distinguishing pathogenic from benign SNPs is essential for clinical applications.

Purpose of the Study:

  • To develop and evaluate novel computational tests for classifying SNPs.
  • To differentiate pathogenic/malignant SNPs from nonpathogenic/benign SNPs.
  • To assess SNP pathogenicity irrespective of their location in coding or noncoding genomic regions.

Main Methods:

  • Utilizing the nearest neighbor (NN) model of Gibbs free energy landscapes for DNA hybridization.
  • Employing an approximating metric (h-distance) to analyze deep structural properties of DNA.
  • Applying machine learning models, including a feed-forward neural network, for SNP classification.

Main Results:

  • The newly developed PNPG test demonstrates approximately 73% accuracy in SNP classification.
  • A feed-forward neural network achieved a fivefold cross-validation accuracy of at least 73%.
  • These methods provide a valuable tool for assessing the disease-causing potential of unclassified SNPs.

Conclusions:

  • The developed tests offer a significant advancement in solving the SNP classification problem.
  • Hybridization chemistry plays a critical role in SNP assessment and classification.
  • These findings can enhance research in genomics and metabolomics, particularly in personalized medicine.