Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

From a phylogenetic tree to a reticulated network.

Vladimir Makarenkov1, Pierre Legendre

  • 1Département d'informatique, Université du Québec à Montréal, C.P. 8888, Succ. Centre-Ville, Montréal (Québec), Canada, H3C 3P8. vladimir.makarenkov@uqam.ca

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|April 10, 2004
PubMed
Summary

Reticulate phylogenetic models, which account for hybridization and gene transfer, are crucial for understanding complex evolutionary histories. This study introduces a new algorithm and software (T-Rex) to infer these reticulate phylogenies from evolutionary distances.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Metagenome-assembled genomes from the temperate forest phyllosphere in Eastern Canada.

Access microbiology·2026
Same author

Soil microbiome prediction using traditional machine learning and deep learning models.

Scientific reports·2026
Same author

Correction: Assessing the emergence time of SARS-CoV-2 zoonotic spillover.

PloS one·2025
Same author

Similarity-based transfer learning with deep learning networks for accurate CRISPR-Cas9 off-target prediction.

PLoS computational biology·2025
Same author

Towards an interpretable machine learning model for predicting antimicrobial resistance.

Journal of global antimicrobial resistance·2025
Same author

Predicting gene distribution in ammonia-oxidizing archaea using phylogenetic signals.

ISME communications·2025

Area of Science:

  • Evolutionary biology
  • Bioinformatics
  • Computational biology

Background:

  • Phylogenetic analyses often assume simple branching (tree-like) evolutionary models.
  • Reticulate evolution, involving processes like hybridization and lateral gene transfer, is common but difficult to model.
  • Existing analytical tools for reticulate phylogenies are limited, hindering research in this area.

Purpose of the Study:

  • To develop a novel algorithm for inferring reticulate phylogenies from evolutionary distances.
  • To provide a computational tool for detecting evolutionary contradictions and identifying reticulate events.
  • To offer a method for visualizing complex evolutionary histories beyond simple tree structures.

Main Methods:

  • Development of a new algorithm that refines phylogenetic tree models to incorporate reticulate events.

Related Experiment Videos

  • Inference of reticulate phylogenies using evolutionary distance data.
  • Comparison of the new algorithm with the SplitsGraph method using a case study on photosynthetic organisms.

    • Main Results:

    • The new algorithm successfully infers reticulate phylogenies by iteratively improving initial tree-based solutions.
    • The method can detect conflicting evolutionary signals within data, suggesting reticulate events.
    • A comparative analysis with SplitsGraph demonstrated the utility of the new approach.

    Conclusions:

    • The developed algorithm provides a robust method for inferring reticulate phylogenies.
    • The T-Rex software facilitates the construction and visualization of complex evolutionary relationships.
    • This work addresses the limitations of traditional phylogenetic models by incorporating reticulate evolutionary mechanisms.