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Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
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HIPSTR: highest independent posterior subtree reconstruction in TreeAnnotator X.

Guy Baele1, Luiz M Carvalho2, Marius Brusselmans1

  • 1Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, Leuven, Belgium.

Biorxiv : the Preprint Server for Biology
|December 23, 2024
PubMed
Summary

A new method, highest independent posterior subtree reconstruction (HIPSTR), consistently identifies more highly supported clades in phylogenetic and phylodynamic studies than the maximum clade credibility (MCC) tree. HIPSTR also offers improved computational efficiency in TreeAnnotator X.

Keywords:
BEASTBEAST 2Bayesian inferenceMCCMCMCMrBayesTreeAnnotatorconsensus treephylogeneticsrevBayes

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Area of Science:

  • Bayesian phylogenetics
  • Phylodynamics
  • Computational biology

Background:

  • Summarizing posterior distributions of phylogenetic trees is crucial in Bayesian phylogenetic and phylodynamic studies.
  • The maximum clade credibility (MCC) tree is a commonly used method for this summarization.
  • However, the MCC tree may not always capture the full support of clades within the posterior distribution.

Purpose of the Study:

  • To introduce and evaluate a novel consensus tree method, the highest independent posterior subtree reconstruction (HIPSTR).
  • To compare the performance of HIPSTR against the MCC tree in identifying highly supported clades.
  • To provide updated, faster computational routines for consensus tree estimation in TreeAnnotator X.

Main Methods:

  • Developed and implemented the HIPSTR algorithm for consensus tree reconstruction.
  • Utilized an updated version of the open-source software TreeAnnotator X for computational routines.
  • Applied both HIPSTR and MCC methods to reconstruct consensus trees from Ebola virus and SARS-CoV-2 datasets.

Main Results:

  • HIPSTR consistently yielded consensus trees with higher supported clades compared to MCC trees across all tested datasets.
  • MCC trees frequently missed clades with very high (≥ 0.95) and moderate-to-high (≥ 0.50) posterior probabilities.
  • HIPSTR demonstrated near-perfect performance in retaining these highly supported clades, outperforming MCC.
  • HIPSTR also showed favorable computational performance within TreeAnnotator X compared to MCC.

Conclusions:

  • HIPSTR is a superior method to MCC for reconstructing time-calibrated consensus phylogenies, particularly for identifying well-supported clades.
  • The updated TreeAnnotator X with enhanced computational routines facilitates efficient consensus tree generation.
  • Further research is warranted to explore the performance of HIPSTR in comparison to other emerging algorithms like CCD0-MAP.