Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Evolutionary Relationships through Genome Comparisons

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...

Viral Recombination

Viral Recombination

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.

Gene Evolution - Fast or Slow?

Gene Evolution - Fast or Slow?

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...

Phylogenetic Trees

Phylogenetic Trees

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.

Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

You might also read

Related Articles

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

Sort by

Same author

Improving the vaccine efficacy gap with microbial-derived therapies.

Cell host & microbe·2026

Same author

Differences in HIV testing may determine the first pillar of the HIV care continuum in a cross-border setting.

HIV medicine·2026

Same author

Biased estimates of phylogenetic branch lengths resulting from the discretised Gamma model of site rate heterogeneity.

Systematic biology·2026

Same author

Global approaches to infectious disease surveillance and modeling.

Nature medicine·2026

Same author

Moderately age-disparate relationships dominate phylogenetically linked pairs involving young women with HIV in KwaZulu-Natal, South Africa.

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases·2026

Same author

HIV Transmission in a Declining African Epidemic.

medRxiv : the preprint server for health sciences·2026

Search research articles

Related Experiment Video

Updated: May 28, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Bayesian mixture models for phylogenetic source attribution from consensus sequences and time since infection

Alexandra Blenkinsop¹, Lysandros Sofocleous¹, Francesco Di Lauro²

¹Department of Mathematics, Imperial College London, London, UK.

Statistical Methods in Medical Research

|February 12, 2025

Summary

This summary is machine-generated.

Integrating pathogen genomics with infection time estimates improves identification of infectious disease transmission sources. This approach enhances accuracy in understanding population-level transmission networks, particularly for viruses with rapid evolution.

Keywords:

HIV prevention evolutionary clock phylodynamics

More Related Videos

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

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

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

Published on: December 7, 2021

Related Experiment Videos

Last Updated: May 28, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

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

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

Published on: December 7, 2021

Area of Science:

Epidemiology
Genomic Epidemiology
Biostatistics

Background:

Pathogen genomic data is crucial for reconstructing infectious disease transmission events.
Existing methods often neglect the time since pathogen variant divergence, limiting accuracy for rapidly evolving viruses.
Incorporating clinical biomarkers for time since infection estimates can refine transmission pair identification.

Purpose of the Study:

To develop a Bayesian mixture model that integrates phylogenetic data with time since infection estimates.
To improve the classification of potential transmission pairs by accounting for evolutionary rates and clinical data.
To enhance the accuracy of identifying population-level transmission sources.

Main Methods:

Development of Bayesian mixture models incorporating an evolutionary clock.
Inclusion of mixed effects or covariate random functions to model mixing weights.
Application of the model to estimate age-specific sources of HIV infection in men who have sex with men networks.

Main Results:

The proposed model accurately classifies potential transmission pairs by combining phylogenetic and time-since-infection data.
Population-level transmission source inferences are more accurate than using only phylogenetic data.
Age-specific sources of HIV infection were estimated in a key population network.

Conclusions:

Time since infection estimates provide valuable data for characterizing transmission sources.
Multi-dimensional mixture models offer a robust framework for phylogenetic source attribution.
The approach enhances understanding of transmission dynamics in infectious diseases.