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Related Concept Videos

Infection01:20

Infection

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When a pathogen enters the body and reproduces, it can cause an infection, damage body cells, and cause illness symptoms that eventually lead to disease. Therefore, its prevention requires breaking the chain of infection.
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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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Viral Recombination00:57

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Cells are sometimes infected by more than one virus at once. When two viruses disassemble to expose their genomes for replication in the same cell, similar regions of their genomes can pair together and exchange sequences in a process called recombination. Alternatively, viruses with segmented genomes can swap segments in a process called reassortment.
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Viruses with RNA Genomes01:29

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RNA viruses are categorized into positive-strand, negative-strand, or double-stranded groups based on their genomic structure and replication mechanisms. This classification dictates how they exploit host cellular machinery for protein synthesis and replication. Some RNA viruses also utilize reverse transcription as part of their life cycle, further diversifying their replication strategies.Positive-Strand RNA VirusesPositive-strand RNA viruses have genomes that function directly as messenger...
Viral Replication: Lytic Cycle01:20

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Retroviruses have a single-stranded RNA genome that undergoes a special form of replication. Once the retrovirus has entered the host cell, an enzyme called reverse transcriptase synthesizes double-stranded DNA from the retroviral RNA genome. This DNA copy of the genome is then integrated into the host’s genome inside the nucleus via an enzyme called integrase. Consequently, the retroviral genome is transcribed into RNA whenever the host’s genome is transcribed, allowing the...
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Updated: Jun 6, 2025

Protocols for Investigating the Host-tissue Distribution, Transmission-mode, and Effect on the Host Fitness of a Densovirus in the Cotton Bollworm
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How fast are viruses spreading in the wild?

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Genomic data helps reconstruct viral spread. The diffusion coefficient and isolation-by-distance (IBD) metrics accurately capture viral dispersal patterns, even with varying sample sizes.

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Area of Science:

  • * Phylogeography and evolutionary biology
  • * Computational biology and bioinformatics
  • * Epidemiology and public health

Background:

  • * Genomic data from viral outbreaks enables reconstruction of viral lineage dispersal in 2D space using continuous phylogeographic inference.
  • * Spatially explicit reconstructions estimate dispersal metrics, providing insights into viral spread dynamics and host-to-host transmission.
  • * Heterogeneous genomic sequence sampling can affect the accuracy of phylogeographic dispersal metrics, with sampling intensity being a key factor.

Purpose of the Study:

  • * To evaluate the robustness of three dispersal metrics (lineage dispersal velocity, diffusion coefficient, isolation-by-distance signal) to varying sampling intensities in continuous phylogeographic reconstructions.
  • * To identify which dispersal metrics are most reliable for characterizing viral spread patterns under different sampling scenarios.
  • * To compare the dispersal patterns and capacities of various viruses in animal populations using robust phylogeographic metrics.

Main Methods:

  • * Utilized simulation studies to systematically assess the impact of sampling intensity (number of samples) on the accuracy of selected dispersal metrics.
  • * Performed continuous phylogeographic inference on simulated genomic data under varying sampling schemes.
  • * Calculated and compared lineage dispersal velocity, diffusion coefficient, and isolation-by-distance (IBD) signal metrics across different sampling intensities.

Main Results:

  • * The diffusion coefficient and isolation-by-distance (IBD) signal metrics demonstrated the highest robustness to changes in sampling intensity.
  • * Lineage dispersal velocity was found to be more sensitive to the number of samples included in the analysis.
  • * Comparative analysis of real viral data revealed diverse IBD patterns and diffusion coefficients, reflecting host dispersal capacities and human-mediated trade impacts.

Conclusions:

  • * Diffusion coefficient and IBD signal metrics are recommended for robust phylogeographic analysis of viral dispersal, particularly when sampling is uneven.
  • * These metrics can effectively compare viral spread dynamics across different viruses and hosts.
  • * Findings highlight the influence of host mobility and human activities on viral dispersal patterns, offering valuable insights for future epidemiological studies.