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Amalgamating source trees with different taxonomic levels.

Vincent Berry1, Olaf R P Bininda-Emonds, Charles Semple

  • 1Méthodes et Algorithmes pour la Bioinformatique MAB team, Université Montpellier 2, L.I.R.M.M. - C.N.R.S., 161 rue Ada, 34095 Montpellier Cedex 5, France.

Systematic Biology
|November 27, 2012
PubMed
Summary
This summary is machine-generated.

The MultiLevelSupertree (MLS) method constructs a single supertree from multiple rooted source trees, even with nested taxa and conflicting data. This approach accommodates incompatibilities, preserving common subtrees and ancestral relationships.

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Area of Science:

  • Phylogenetics
  • Computational Biology
  • Systematics

Background:

  • Supertree methods typically require non-nested taxa across source trees.
  • Existing methods like AncestralBuild handle nested taxa but are limited to compatible datasets.
  • Incompatibility in source trees poses a challenge for supertree construction.

Purpose of the Study:

  • To introduce a novel supertree method, MultiLevelSupertree (MLS), that accommodates incompatibilities among rooted source trees with nested taxa.
  • To overcome the limitations of existing methods by allowing for conflicting data in the input trees.
  • To develop a supertree method that preserves vertical and horizontal signals even with data conflict.

Main Methods:

  • The MultiLevelSupertree (MLS) method is introduced, accepting rooted source trees with potentially nested taxa.
  • MLS employs a MinCut-like procedure to handle incompatibilities within the source trees.
  • The method is designed to preserve common subtrees and compatible ancestral relationships.

Main Results:

  • MLS successfully constructs a supertree from incompatible rooted source trees containing nested taxa.
  • The method preserves common subtrees and compatible ancestral relationships.
  • Polynomial time complexity is achieved for the MLS method.
  • Application to Phocidae data demonstrated correct placement of nested taxa despite conflicting clades.

Conclusions:

  • MLS provides a robust solution for supertree construction with nested taxa and data conflict.
  • The method effectively integrates vertical (nested taxa) and horizontal (clades) signals.
  • MLS offers a valuable tool for phylogenetic inference when source trees exhibit incompatibilities.