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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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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Sampling Methods: Sample Types01:18

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Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
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Sampling Methods: Overview01:06

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Sampling Plans01:23

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
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Related Experiment Video

Updated: Aug 16, 2025

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covSampler: A subsampling method with balanced genetic diversity for large-scale SARS-CoV-2 genome data sets.

Yexiao Cheng1,2,3, Chengyang Ji1,2, Na Han1,2

  • 1Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, 9 Dongdan Santiao, Dongcheng District, Beijing 100730, China.

Virus Evolution
|December 19, 2022
PubMed
Summary

A new method, covSampler, addresses challenges in analyzing large SARS-CoV-2 genome datasets by considering both spatiotemporal distribution and genetic diversity. This tool ensures more accurate phylogenetic analysis of viral evolution.

Keywords:
SARS-CoV-2phylogeneticssubsampling

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Area of Science:

  • Virology
  • Bioinformatics
  • Evolutionary Biology

Background:

  • Phylogenetic analysis is crucial for understanding viral evolution.
  • The rapid increase in SARS-CoV-2 genomes presents significant data analysis challenges.
  • Existing subsampling methods may introduce bias by neglecting genetic diversity.

Purpose of the Study:

  • To develop a novel subsampling method, covSampler, for SARS-CoV-2 genomes.
  • To address limitations of current methods by incorporating genetic diversity alongside spatiotemporal data.
  • To provide a user-friendly tool for efficient and unbiased viral genome subsampling.

Main Methods:

  • Developed covSampler, a method that clusters genomes based on spatiotemporal distribution and genetic variation into 'divergent pathways'.
  • Implemented two subsampling strategies: representative and comprehensive, with adjustable parameters.
  • Utilized performance and validation tests to assess the tool's efficiency and stability.

Main Results:

  • covSampler effectively subsamples large SARS-CoV-2 genome datasets.
  • The method accounts for both spatiotemporal distribution and genetic diversity, reducing potential bias.
  • Validation tests confirmed the tool's efficiency, stability, and customizability.

Conclusions:

  • covSampler offers an improved approach to subsampling viral genomes, particularly for rapidly evolving viruses like SARS-CoV-2.
  • The developed tool and associated webserver provide a valuable resource for researchers studying viral evolution.
  • This method facilitates more accurate and reliable phylogenetic analyses of large-scale genomic data.