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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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...
Stratified Sampling Method01:16

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures 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 stratified sample, divide the population into groups called strata and then take a...
Sampling Methods: Overview01:06

Sampling Methods: Overview

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 sampling...
Cluster Sampling Method01:20

Cluster Sampling Method

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...
Sampling Plans01:23

Sampling Plans

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.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Updated: May 13, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

SAASI: Sampling Aware Ancestral State Inference.

Yexuan Song1, Ivan Gill2, Ailene MacPherson3,4

  • 1Department of Mathematics, Simon Fraser University, Burnaby, BC, Canada. yexuan_song@sfu.ca.

Nature Communications
|May 11, 2026
PubMed
Summary
This summary is machine-generated.

Sampling bias can distort viral transmission history. Our new method, sampling-aware ancestral state inference (SAASI), accurately reconstructs viral origins and spread, even with uneven data. This improves phylogeographic analyses.

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Optimized Bone Sampling Protocols for the Retrieval of Ancient DNA from Archaeological Remains
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Last Updated: May 13, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Optimized Bone Sampling Protocols for the Retrieval of Ancient DNA from Archaeological Remains
06:18

Optimized Bone Sampling Protocols for the Retrieval of Ancient DNA from Archaeological Remains

Published on: November 30, 2021

Area of Science:

  • Evolutionary biology
  • Computational phylogenetics
  • Epidemiology

Background:

  • Ancestral state inference is crucial for understanding pathogen transmission dynamics.
  • Existing methods are susceptible to sampling bias, potentially leading to inaccurate historical reconstructions.
  • The H5N1 virus presents a complex multi-host transmission network.

Purpose of the Study:

  • To develop a novel method for ancestral state inference that explicitly accounts for sampling bias.
  • To assess the impact of sampling bias on H5N1 virus transmission history in the US.
  • To evaluate the accuracy and scalability of the new method.

Main Methods:

  • Introduction of sampling-aware ancestral state inference (SAASI).
  • Application of SAASI to H5N1 virus transmission data in the United States (2024).
  • Comparative analysis with standard inference methods using simulations.

Main Results:

  • SAASI corrects for sampling differences, providing more accurate ancestral state reconstructions.
  • Under-sampling of wild birds in H5N1 data led to an underestimation of the transmission event to cattle.
  • SAASI demonstrates superior accuracy over standard methods when sampling bias is present.

Conclusions:

  • SAASI is a robust and computationally efficient tool for phylogeographic analysis.
  • Accounting for sampling bias is essential for accurate reconstruction of pathogen transmission histories.
  • The method is scalable to large phylogenetic datasets, facilitating broader applications.