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Related Concept Videos

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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Viral Mutations00:36

Viral Mutations

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A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...
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Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
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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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Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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Synteny and Evolution02:31

Synteny and Evolution

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John H. Renwick first coined the term “synteny” in 1971, which refers to the genes present on the same chromosomes, even if they are not genetically linked. The species with common ancestry tend to show conserved syntenic regions. Therefore, the concept of synteny is nowadays used to describe the evolutionary relationship between species.
Around 80 million years ago, the human and mice lineages diverged from the common ancestor. During the course of evolution, the ancestral...
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Updated: Jun 12, 2025

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

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Variant evolution graph: Can we infer how SARS-CoV-2 variants are evolving?

Badhan Das1, Lenwood S Heath1

  • 1Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America.

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|June 9, 2025
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Summary
This summary is machine-generated.

A new Variant Evolution Graph (VEG) models SARS-CoV-2 evolution, revealing viral pathways and transmission networks. This graph-based approach offers a scalable alternative to traditional phylogenetics for analyzing diverse viral genomic data.

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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Following the Dynamics of Structural Variants in Experimentally Evolved Populations

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Area of Science:

  • Virology
  • Computational Biology
  • Genomics

Background:

  • SARS-CoV-2 exhibits extensive mutations, leading to genetic diversity that impacts transmissibility and disease severity.
  • Viral quasispecies, a cloud of variants produced by rapid mutation, face loss due to transmission bottlenecks.
  • Analyzing vast viral genomic datasets, like those in GISAID, challenges traditional phylogenetic methods.

Purpose of the Study:

  • To introduce a novel graph-based framework, the Variant Evolution Graph (VEG), inspired by quasispecies theory for modeling viral evolution.
  • To compare the computational efficiency of different methods for constructing the VEG.
  • To demonstrate the utility of VEG in uncovering evolutionary patterns and inferring transmission networks.

Main Methods:

  • Developed a graph-based framework (VEG) that models viral evolution, accommodating multiple ancestors and all evolutionary pathways.
  • Derived a Disease Transmission Network (DTN) from the VEG to infer host transmission pathways.
  • Compared computational performance of sourmash, pyani, edit distance, and Maximum Likelihood (ML) for VEG construction using genomic data from five countries.

Main Results:

  • VEG reveals critical evolutionary patterns, including recombination, mutation hotspots, and intra-host evolution.
  • The derived DTN aids in identifying transmission pathways and super-spreader events.
  • Sourmash and edit distance methods are computationally efficient for VEG construction, outperforming ML.

Conclusions:

  • The Variant Evolution Graph (VEG) provides a powerful and scalable alternative for analyzing large viral genomic datasets.
  • VEG offers deeper insights into viral adaptation, spread, and transmission dynamics compared to traditional phylogenetic trees.
  • Computational efficiency of VEG construction methods like sourmash is crucial for real-time analysis of evolving viral populations.