Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Microbial Phylogeny01:28

Microbial Phylogeny

22
Understanding the evolutionary relationships among microorganisms is fundamental to microbial ecology and taxonomy. Phylogenetic trees are essential tools for inferring these relationships, relying primarily on comparative analyses of molecular sequences such as DNA, RNA, or proteins. In microbial studies, these trees typically depict the evolutionary paths of diverse bacterial and archaeal species by mapping genetic differences accumulated over time.Phylogenetic trees are composed of tips,...
22
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

8.4K
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...
8.4K
Phylogenetic Trees03:21

Phylogenetic Trees

51.7K
Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
51.7K
Sampling Plans01:23

Sampling Plans

1.3K
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...
1.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Smoothly Time-Varying Continuous Time Markov Chains in Phylogenetics.

ArXiv·2026
Same author

Reconciling fast Hepatitis B evolutionary rates with ancient co-divergence.

bioRxiv : the preprint server for biology·2026
Same author

Heterogeneity of Treatment Effects Across Nine Glucose-Lowering Drug Classes in Type 2 Diabetes: Extension of the LEGEND-T2DM Network Study.

Diabetes, obesity & metabolism·2026
Same author

Semaglutide and Neovascular Age-Related Macular Degeneration Among Adults with Type 2 Diabetes: An OHDSI Network Study.

Ophthalmology·2026
Same author

Global approaches to infectious disease surveillance and modeling.

Nature medicine·2026
Same author

Generalizing Matrix Representations to Fully Heterochronous Ranked Tree Shapes.

Bulletin of mathematical biology·2026
Same journal

Combinatorial multiomic analysis from a pedigree of Sox10Dom Hirschsprung mice identifies multiple high confidence candidate modifiers of Enteric Nervous System development.

PLoS computational biology·2026
Same journal

Extracting host-specific developmental signatures from longitudinal microbiome data.

PLoS computational biology·2026
Same journal

Population sparseness determines strength of Hebbian plasticity for maximal memory lifetime in associative networks.

PLoS computational biology·2026
Same journal

Predictive coding explains asymmetric connectivity in the brain: A neural network study.

PLoS computational biology·2026
Same journal

Zooplankton feeding behavioral signatures in the morphology of macroscale prey spatial distribution.

PLoS computational biology·2026
Same journal

A brief overview of 20 years of neuroscience in PLoS Computational Biology.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Mar 24, 2026

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
10:23

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

Published on: July 11, 2025

740

Quantifying and Mitigating the Effect of Preferential Sampling on Phylodynamic Inference.

Michael D Karcher1, Julia A Palacios2,3,4, Trevor Bedford5

  • 1Department of Statistics, University of Washington, Seattle, Washington, United States of America.

Plos Computational Biology
|March 4, 2016
PubMed
Summary
This summary is machine-generated.

Phylodynamic methods can be biased when sampling times depend on population size. A new model using an inhomogeneous Poisson process corrects this bias and improves effective population size estimation.

More Related Videos

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples
11:23

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

Published on: December 22, 2014

37.8K
Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
07:21

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

13.6K

Related Experiment Videos

Last Updated: Mar 24, 2026

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
10:23

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles

Published on: July 11, 2025

740
Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples
11:23

Purifying the Impure: Sequencing Metagenomes and Metatranscriptomes from Complex Animal-associated Samples

Published on: December 22, 2014

37.8K
Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
07:21

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

Published on: August 25, 2018

13.6K

Area of Science:

  • Evolutionary biology
  • Computational biology
  • Epidemiology

Background:

  • Phylodynamics estimates population size changes using molecular sequences.
  • Current methods assume fixed or independent sampling times, potentially causing bias.
  • Serial sampling through time is common in epidemiological studies.

Purpose of the Study:

  • To address bias in phylodynamic inference caused by non-random sampling.
  • To develop a new model accounting for sampling time dependencies.
  • To improve the accuracy and precision of effective population size estimation.

Main Methods:

  • Simulated sequence data under varying sampling schemes.
  • Developed a novel phylodynamic model incorporating preferential sampling.
  • Modeled sampling times as a size-dependent inhomogeneous Poisson process.
  • Compared proposed model against existing methods using simulations and real data.

Main Results:

  • Demonstrated systematic bias in current methods when sampling is size-dependent.
  • The new model significantly reduced estimation bias.
  • The proposed model enhanced the precision of effective population size estimates.
  • Performance was validated using seasonal human influenza data.

Conclusions:

  • Preferential sampling is a critical factor in phylodynamic analyses.
  • The proposed inhomogeneous Poisson process model corrects for sampling bias.
  • This improved model offers more accurate insights into population dynamics.
  • Applicable to various pathogens with seasonal or time-dependent sampling.