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?

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.

Speciation Rates

Speciation Rates

Gene Duplication and Divergence

Gene Duplication and Divergence

The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are...

You might also read

Related Articles

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

Sort by

Same author

Tree House Explorer: A Novel Genome Browser for Phylogenomics.

Molecular biology and evolution·2022

Same author

An efficient and extensible approach for compressing phylogenetic trees.

BMC bioinformatics·2011

Same author

A new support measure to quantify the impact of local optima in phylogenetic analyses.

Evolutionary bioinformatics online·2011

Same author

Impacts of the Cretaceous Terrestrial Revolution and KPg extinction on mammal diversification.

Science (New York, N.Y.)·2011

Same author

Big cat phylogenies, consensus trees, and computational thinking.

Journal of computational biology : a journal of computational molecular cell biology·2011

Same author

Large-scale analysis of phylogenetic search behavior.

Advances in experimental medicine and biology·2010

Search research articles

Related Experiment Video

Updated: Feb 16, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Fast algorithms for computing phylogenetic divergence time.

Ralph W Crosby¹, Tiffani L Williams²

¹Department of Computer Science, College of Charleston, Charleston, SC, USA. crosbyrw@cofc.edu.

BMC Bioinformatics

|December 16, 2017

Summary

This summary is machine-generated.

AncestralAge significantly speeds up species divergence time inference for large phylogenetic studies. This new algorithm improves computation time for phylogenetic likelihood and Bayesian node age priors, enabling analysis of massive datasets.

Keywords:

Divergence time MCMC Phylogenetics

More Related Videos

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

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: Feb 16, 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 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:

Phylogenetics
Computational Biology
Evolutionary Biology

Background:

Species divergence time inference is crucial for phylogenetics.
Existing methods struggle with large datasets (hundreds of taxa, thousands of DNA base pairs), requiring extensive computation time.
A study of 349 primate taxa was estimated to take over 9 months.

Purpose of the Study:

To present AncestralAge, a novel algorithm designed to enhance the performance of divergence time inference.
To address the computational limitations of current phylogenetic methods for large-scale studies.

Main Methods:

Development of a new algorithm, AncestralAge.
Implementation of an improved method for phylogenetic likelihood computation.
Introduction of a novel method for computing Bayesian priors on node ages.

Main Results:

A 90% improvement in phylogenetic likelihood computation time for a dataset of 349 primate taxa (over 60,000 DNA base pairs).
A 99% reduction in running time for Bayesian prior computation on the same dataset.
Demonstrated significant performance gains in divergence time inference.

Conclusions:

The developed algorithms enable divergence time inference for large phylogenetic studies.
AncestralAge overcomes previous computational bottlenecks, facilitating more extensive evolutionary analyses.