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

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?

Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).

Speciation Rates

Speciation Rates

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...

Genetic Drift

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.

You might also read

Related Articles

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

Sort by

Same author

Phylograms better fit neutrally evolving traits than chronograms.

Evolution letters·2026

Same author

DNA-based identification of plants and the genomic nature of plant species differences.

Communications biology·2026

Same author

Genomic basis of the evolution of vertical bars in clownfishes.

BMC genomics·2026

Same author

Tarsal Tunnel Syndrome: Outcomes and Predictive Factors After a Minimum of 5-Year Follow-up Post-Treatment.

Foot & ankle specialist·2026

Same author

Habitat Specialisation Impacts Clownfish Demographic Resilience to Pleistocene Sea-Level Fluctuations.

Molecular ecology·2025

Same author

A Model for the Formation of Beliefs and Social Norms Based on the Satisfaction Problem (SAT).

Entropy (Basel, Switzerland)·2025

Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026

Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026

Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026

Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026

Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026

Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 9, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Accelerating Bayesian inference for evolutionary biology models.

Xavier Meyer^1,2,3, Bastien Chopard^2,3, Nicolas Salamin^1,2

¹Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland.

Bioinformatics (Oxford, England)

|December 28, 2016

Summary

This summary is machine-generated.

A new parallel Metropolis-Hastings framework significantly speeds up Bayesian inference for complex evolutionary models. This method offers up to 35x faster sampling and 20x faster convergence for phylogenetic tree analysis compared to sequential methods.

More Related Videos

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Related Experiment Videos

Last Updated: Mar 9, 2026

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

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

Evolutionary biology
Computational biology
Bioinformatics

Background:

Bayesian inference is crucial in modern science, heavily relying on Markov chain Monte Carlo (MCMC) methods.
Advancements in MCMC have benefited evolutionary biology, but complex models and data growth strain current methods.
There is a need for more efficient computational tools to handle increasingly sophisticated evolutionary models.

Purpose of the Study:

To develop a novel parallel Metropolis-Hastings (M-H) framework for Bayesian inference.
To enhance sampling speed and convergence for parameter-rich and complex evolutionary models.
To provide a more efficient computational solution for phylogenetic tree inference.

Main Methods:

Implementation of a parallel Metropolis-Hastings (M-H) framework.
Integration of novel enhancements for handling complex models.
Comparison with sequential M-H processes and existing software (MrBayes).

Main Results:

Achieved up to 35 times increase in sampling speed on a parameter-rich macroevolutionary model using 32 processors.
Demonstrated up to a twentyfold faster convergence for estimating posterior probabilities of phylogenetic trees.
Outperformed the well-known software MrBayes in Bayesian inference of phylogenetic trees.

Conclusions:

The developed parallel M-H framework significantly accelerates Bayesian inference for complex evolutionary models.
This approach enhances computational efficiency for phylogenetic tree analysis.
The framework offers a powerful tool for researchers dealing with large datasets and intricate models in evolutionary biology.