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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Terrace Aware Data Structure for Phylogenomic Inference from Supermatrices.

Olga Chernomor1, Arndt von Haeseler1,2, Bui Quang Minh3

  • 1Center for Integrative Bioinformatics Vienna, Max F. Perutz Laboratories, University of Vienna, Medical University of Vienna, A-1030 Vienna, Austria and.

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Summary
This summary is machine-generated.

Phylogenomic analyses using partition models can be slowed by missing data. The new phylogenetic terrace aware (PTA) data structure efficiently handles missing data, speeding up phylogenetic tree searches.

Keywords:
Maximum likelihoodpartial terracespartition modelsphylogenetic terracesphylogenomic inference

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Area of Science:

  • Phylogenomics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Phylogenomic analyses commonly use partition models, where each gene or partition evolves under its own model.
  • Missing data in supermatrices can create phylogenetic (partial) terraces, hindering tree search algorithms.
  • Existing methods may struggle with efficiency when dealing with missing data in complex partition models.

Purpose of the Study:

  • To introduce a novel data structure, phylogenetic terrace aware (PTA), for efficient phylogenetic analysis under partition models.
  • To address the computational challenges posed by missing data in phylogenomic supermatrix analyses.
  • To improve the speed and applicability of phylogenetic inference software.

Main Methods:

  • Development of the phylogenetic terrace aware (PTA) data structure.
  • Implementation of PTA within the IQ-TREE software.
  • Evaluation of PTA's performance against standard IQ-TREE and RAxML implementations, particularly in the presence of missing data.
  • Exploitation of (partial) terraces and induced partition trees for computational savings.

Main Results:

  • The PTA data structure significantly speeds up phylogenetic tree searches in the presence of missing data.
  • Implementations of PTA in IQ-TREE achieved speedups of up to 4.5 times compared to standard IQ-TREE and 8 times compared to RAxML.
  • PTA effectively utilizes phylogenetic (partial) terraces to reduce computation time.
  • The method demonstrates efficiency gains across various partition models and topological rearrangements.

Conclusions:

  • The phylogenetic terrace aware (PTA) data structure offers a substantial computational advantage for phylogenomic analyses employing partition models, especially with missing data.
  • PTA's ability to exploit phylogenetic terraces makes it a valuable tool for accelerating tree search algorithms.
  • The general applicability of PTA to diverse partition models and rearrangements suggests its broad utility across phylogenomic inference software.