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A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
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Family-Joining: A Fast Distance-Based Method for Constructing Generally Labeled Trees.

Prabhav Kalaghatgi1, Nico Pfeifer2, Thomas Lengauer2

  • 1Department of Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken, Germany Graduate School of Computer Science, Saarland University, Saarbrücken, Germany prabhavk@mpi-inf.mpg.de.

Molecular Biology and Evolution
|July 21, 2016
PubMed
Summary
This summary is machine-generated.

A new family-joining (FJ) method constructs generally labeled trees, accommodating direct ancestral relationships and polytomies. FJ-BIC demonstrated superior accuracy in reconstructing evolutionary trees and accurately reflected HIV transmission chains.

Keywords:
densely sampled taxadistance-based phylogeniesgenerally labeled treeslatent tree graphical models.

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Area of Science:

  • Phylogenetics and Evolutionary Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Traditional phylogenetic models use bifurcating trees, which may not accurately represent evolutionary relationships with dense sampling or incomplete resolution.
  • Direct ancestral relationships and polytomies (unresolved branching points) pose challenges for standard tree-building methods.
  • Existing methods often struggle to fully resolve complex evolutionary histories.

Purpose of the Study:

  • To introduce a novel, fast distance-based agglomeration method called family-joining (FJ) for constructing generally labeled trees.
  • To evaluate different threshold selection strategies (FJ-AIC, FJ-BIC, FJ-CV) for the FJ method.
  • To compare the performance of FJ-BIC against existing tree-reconstruction methods using simulated and real-world data.

Main Methods:

  • Developed the family-joining (FJ) algorithm, a distance-based agglomeration technique for building generally labeled trees.
  • Implemented and tested three threshold selection criteria: Akaike information criterion (FJ-AIC), Bayesian information criterion (FJ-BIC), and cross-validation error (FJ-CV).
  • Compared FJ-BIC with other methods on simulated datasets and applied it to HIV sequence data from a known transmission chain.

Main Results:

  • FJ-BIC exhibited high accuracy in reconstructing correct evolutionary trees across various simulation scenarios, outperforming related methods.
  • Application of FJ-BIC to HIV transmission data showed strong compatibility with known transmission events.
  • FJ-BIC trees demonstrated higher bootstrap support for internal branches compared to trees built with RAxML, indicating greater confidence in the inferred relationships.

Conclusions:

  • The family-joining (FJ) method, particularly FJ-BIC, offers a robust approach for constructing generally labeled trees, accommodating complex evolutionary scenarios.
  • FJ-BIC provides a reliable tool for phylogenetic inference, especially when dealing with densely sampled taxa or uncertain branching patterns.
  • This work represents a significant advancement as the first method for modeling evolutionary relationships using generally labeled trees.