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Related Concept Videos

Viral Mutations00:36

Viral Mutations

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 for adaptive...

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Related Experiment Video

Updated: Jun 16, 2026

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples
09:36

Unbiased Deep Sequencing of RNA Viruses from Clinical Samples

Published on: July 2, 2016

Protocols for sampling viral sequences to study epidemic dynamics.

J Conrad Stack1, J David Welch, Matt J Ferrari

  • 1Department of Biology, Pennsylvania State University, University Park, PA, USA.

Journal of the Royal Society, Interface
|February 12, 2010
PubMed
Summary
This summary is machine-generated.

Strategic viral sequencing improves infectious disease monitoring. Purposeful sampling protocols offer a clearer view of viral population dynamics than random collection, enhancing phylogenetic analysis.

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Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency
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Isolation of Fidelity Variants of RNA Viruses and Characterization of Virus Mutation Frequency

Published on: June 16, 2011

Area of Science:

  • Epidemiology
  • Virology
  • Computational Biology

Background:

  • Global infectious disease monitoring relies heavily on viral genetic data.
  • Advancements in statistical techniques aid in analyzing genetic sequence data.
  • The impact of data collection methods on phylogenetic analysis efficacy is understudied.

Purpose of the Study:

  • To investigate how viral sequence data collection affects phylogenetic algorithms.
  • To assess the influence of sampling strategies on understanding viral population dynamics.
  • To determine optimal sampling protocols for accurate phylogenetic inference.

Main Methods:

  • Utilized epidemic simulations to model viral transmission and sequence collection.
  • Compared phylogenetic analysis results from different sampling protocols.
  • Evaluated the clarity and distortion of population dynamics inferred from simulated data.

Main Results:

  • Purposefully designed sampling protocols, like those for seasonal influenza, yield superior insights into population dynamics.
  • Temporal distribution of samples significantly impacts the inferences drawn from genetic data.
  • Less-focused collection protocols provide a less accurate representation of underlying viral dynamics.

Conclusions:

  • Sampling strategies critically influence the interpretation of viral genetic data.
  • Optimizing the temporal distribution of samples is crucial for effective disease surveillance.
  • Consideration of sampling distribution is vital when designing and evaluating data collection protocols.