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

Phylogeny

Phylogeny

Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire kingdom.

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...

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

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.

Applications of Molecular Taxonomy

Applications of Molecular Taxonomy

Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...

Microbial Phylogeny

Microbial Phylogeny

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,...

You might also read

Related Articles

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

Sort by

Same author

Evolving view on phylogenetic networks.

Systematic biology·2026

Same author

tanggle: An R package for the visualization of phylogenetic networks.

Applications in plant sciences·2026

Same author

Activity-Informed Network Analysis Reveals Keystone Microbes Shaping Freshwater Ecosystem Function.

Environmental microbiology reports·2026

Same author

Ecological constraints and evolutionary trade-offs shape nitrogen fixation across habitats.

ISME communications·2026

Same author

Bayesian chain graph models to characterize microbe-environment dynamics.

Mathematical biosciences and engineering : MBE·2026

Same author

A hybrid framework for disease biomarker discovery in microbiome research combining Bayesian networks, machine learning, and network-based methods.

Biology methods & protocols·2026

Search research articles

Related Experiment Video

Updated: May 13, 2026

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

SNaQ.jl: Improved scalability for level-1 phylogenetic network inference.

Nathan Kolbow^1,2, Sungsik Kong^1,3, Tyler Chafin^4,5

¹Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, 53706, United States.

Bioinformatics (Oxford, England)

|May 11, 2026

Summary

The new SNaQ.jl package significantly speeds up phylogenetic network inference. This computational tool enhances efficiency for analyzing complex evolutionary histories like hybridization and horizontal gene transfer.

More Related Videos

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

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Related Experiment Videos

Last Updated: May 13, 2026

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

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Area of Science:

Computational Biology
Evolutionary Biology
Bioinformatics

Background:

Phylogenetic networks model complex evolutionary events (hybridization, horizontal gene transfer) often missed by traditional trees.
Existing phylogenetic network inference methods face scalability limitations due to computational demands and vast network search spaces.
Composite likelihood methods like SNaQ offer improved tractability but are still insufficient for large datasets.

Purpose of the Study:

Introduce SNaQ.jl, a Julia package enhancing the scalability of composite likelihood phylogenetic network inference.
Improve computational efficiency for inferring complex evolutionary histories.

Main Methods:

Implemented SNaQ.jl as a standalone Julia package.
Integrated new scalability features: parallel quartet likelihood calculations, weighted random quartet selection, and probabilistic network search.
Utilized composite likelihood inference.

Main Results:

SNaQ.jl (version 1.1) demonstrates significant improvements in computational efficiency.
Average runtimes were reduced by up to 499% compared to previous implementations.
Accuracy and functional parameters remained unchanged, indicating enhanced performance without compromising results.

Conclusions:

SNaQ.jl offers a computationally efficient solution for phylogenetic network inference.
The package provides a scalable tool for analyzing complex evolutionary scenarios using composite likelihood methods.
This advancement addresses the limitations of existing methods for large-scale phylogenetic analyses.