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Related Experiment Video

Updated: Jan 24, 2026

A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
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A Fast Likelihood Method to Reconstruct and Visualize Ancestral Scenarios.

Sohta A Ishikawa1,2,3, Anna Zhukova1, Wataru Iwasaki2

  • 1Unité Bioinformatique Evolutive, Institut Pasteur, C3BI USR 3756 IP & CNRS, Paris, France.

Molecular Biology and Evolution
|May 26, 2019
PubMed
Summary

This study introduces PastML, a novel method for reconstructing ancestral scenarios on phylogenetic trees. PastML accurately visualizes evolutionary uncertainty, improving upon existing methods for phylogeography and drug resistance evolution.

Keywords:
Brier scoring ruleDengueHIVancestral character reconstructiondrug resistance mutationsmarginal and joint posterior probabilitiesmaximum a posteriorimaximum likelihoodphylogeneticsphylogeographysimulations

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Area of Science:

  • Evolutionary biology
  • Phylogenetics
  • Computational biology

Background:

  • Ancestral state reconstruction is crucial for understanding character evolution on phylogenetic trees.
  • Current methods like marginal and joint reconstructions have limitations in interpreting uncertainty and visualization.
  • There is a need for a method that balances accuracy with clear representation of inference uncertainty.

Purpose of the Study:

  • To develop a novel, fast, and accurate method for ancestral state reconstruction that addresses limitations of existing approaches.
  • To introduce a visualization technique that reflects the uncertainty in ancestral state inferences.
  • To implement this method in a user-friendly program and web server (PastML).

Main Methods:

  • Utilized decision-theory concepts, specifically the Brier score, to associate nodes with sets of likely ancestral states.
  • Developed a visualization approach by clustering neighboring nodes with similar states using graph visualization tools.
  • Implemented the method in PastML, a program and web server for phylogenetic analysis.

Main Results:

  • PastML demonstrates accuracy and robustness on simulated data.
  • Applied to Dengue serotype 2 (DENV2) phylogeography, it identified transmission routes and highlighted uncertainty in geographic origin.
  • Analysis of HIV drug resistance revealed independent emergence of mutations under treatment and identified transmission clusters among untreated patients.

Conclusions:

  • PastML offers a superior approach to ancestral state reconstruction by effectively managing and visualizing inference uncertainty.
  • The method is computationally efficient, enabling rapid analysis of large datasets.
  • PastML provides valuable insights into the evolutionary history of pathogens and the emergence of drug resistance.