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...

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...

Gene Evolution - Fast or Slow?

Gene Evolution - Fast or Slow?

Speciation Rates

Speciation Rates

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.

Phylogenetic Trees

Phylogenetic Trees

You might also read

Related Articles

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

Sort by

Same author

Metatranscriptomics as a tool to identify fungal species and subspecies in mixed communities - a proof of concept under laboratory conditions.

IMA fungus·2020

Same author

Correction: Bat IFITM3 restriction depends on S-palmitoylation and a polymorphic site within the CD225 domain.

Life science alliance·2020

Same author

CCMetagen: comprehensive and accurate identification of eukaryotes and prokaryotes in metagenomic data.

Genome biology·2020

Same author

Author Correction: A new coronavirus associated with human respiratory disease in China.

Nature·2020

Same author

Newly identified viral genomes in pangolins with fatal disease.

Virus evolution·2020

Same author

An emergent clade of SARS-CoV-2 linked to returned travellers from Iran.

Virus evolution·2020

Search research articles

Related Experiment Video

Updated: Mar 17, 2026

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Estimating evolutionary rates using time-structured data: a general comparison of phylogenetic methods.

Sebastián Duchêne^1,2,3, Jemma L Geoghegan^1,2, Edward C Holmes^1,2

¹Marie Bashir Institute of Infectious Diseases and Biosecurity, Charles Perkins Centre, Sydney Medical School, University of Sydney, Sydney, NSW 2006, Australia.

Bioinformatics (Oxford, England)

|July 15, 2016

Summary

This summary is machine-generated.

Estimating pathogen evolutionary rates using molecular clocks is crucial. Different methods yield varying results, highlighting the importance of selecting appropriate clock models for accurate evolutionary rate estimation in viruses and bacteria.

More Related Videos

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

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

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

Published on: July 11, 2025

Related Experiment Videos

Last Updated: Mar 17, 2026

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

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

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

Published on: July 11, 2025

Area of Science:

Evolutionary biology
Virology
Genomics

Background:

Rapid genetic evolution in pathogens like viruses necessitates accurate evolutionary rate estimation.
Molecular clock methods utilize pathogen sampling times to infer evolutionary rates.
Existing methods for rate estimation differ in handling phylogenetic uncertainty and lineage-specific rate variation.

Purpose of the Study:

To compare different molecular clock methods for estimating evolutionary rates in pathogens.
To investigate the impact of clock models and data characteristics on rate estimates.
To identify factors influencing the reliability of evolutionary rate estimates from time-structured sequence data.

Main Methods:

Compiled 81 virus sequence datasets.
Estimated nucleotide substitution rates using root-to-tip regression, least-squares dating, and Bayesian inference.
Evaluated the influence of different molecular clock models (e.g., strict vs. relaxed clocks).

Main Results:

Rate estimates were often congruent across methods but highly dependent on the chosen clock model.
Relaxed-clock models generally yielded higher evolutionary rate estimates compared to strict-clock models.
Discrepancies were linked to among-lineage rate variation, phylogenetic uncertainty, and temporal clustering.

Conclusions:

The choice of molecular clock model significantly impacts evolutionary rate estimates in pathogens.
Testing and selecting appropriate clock models are critical for reliable evolutionary rate inference.
Understanding factors like rate variation and data structure improves the accuracy of molecular clock analyses.