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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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Genetic Variation01:25

Genetic Variation

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles,...
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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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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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Sanger Sequencing01:57

Sanger Sequencing

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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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Leaky Scanning02:28

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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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Updated: Sep 22, 2025

Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency
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NGS data vectorization, clustering, and finding key codons in SARS-CoV-2 variations.

Juhyeon Kim1,2,3, Saeyeon Cheon4, Insung Ahn5,6,7

  • 1Department of Data-Centric Problem Solving Research, Korea Institute of Science and Technology Information, Yuseong-gu, Daejeon, Korea.

BMC Bioinformatics
|May 17, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning methods effectively analyzed SARS-CoV-2 sequence data, distinguishing variants like Omicron and Delta. This approach identifies key genetic changes in the spike protein, offering a powerful tool for tracking viral evolution and disease analysis.

Keywords:
ClusteringDensity based spatial clustering of applications with noiseFeature selectionProtein sequence analysisRandom forestSARS-CoV-2Sequence data pre-processShapely valuet-Stochastic neighbour embedding

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Area of Science:

  • Genomics
  • Computational Biology
  • Virology

Background:

  • The rapid global spread of SARS-CoV-2 has led to the emergence of numerous variants.
  • Understanding the degree and location of these genetic variations is crucial for public health.
  • Existing methods for analyzing large-scale sequence data can be cumbersome.

Purpose of the Study:

  • To propose machine learning-based methods for vectorizing, clustering, and visualizing SARS-CoV-2 sequence data.
  • To identify specific genetic variations, particularly in the spike protein, that differentiate viral variants.
  • To develop a scalable and efficient approach for analyzing large genomic datasets.

Main Methods:

  • Collected 224,073 SARS-CoV-2 sequences from NCBI and GISAID.
  • Utilized dimensionality reduction (t-SNE) and clustering (DBSCAN) for data visualization.
  • Employed feature importance models (Random Forest, Shapely Value) to pinpoint critical codon changes.

Main Results:

  • Clustering analysis successfully differentiated the original SARS-CoV-2 strain from variants like Delta and Omicron.
  • Identified specific codon changes within the spike protein responsible for variant distinctions.
  • The proposed method demonstrated superior efficiency in analyzing and visualizing large datasets compared to traditional tree-based methods.

Conclusions:

  • The developed machine learning framework effectively visualizes and analyzes SARS-CoV-2 genetic variations.
  • This method accurately distinguishes between viral variants and identifies key genetic drivers of these changes.
  • The approach is adaptable for analyzing sequence data from various infectious diseases, including influenza and other coronaviruses.