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Identifying a species tree subject to random lateral gene transfer
Mike Steel1, Simone Linz, Daniel H Huson
1Allan Wilson Centre for Molecular Ecology and Evolution, University of Canterbury, Christchurch, New Zealand. mathmomike@gmail.com
Journal of Theoretical Biology
|January 24, 2013
Summary
Reconstructing species trees can be challenging due to gene tree conflicts. A triplet-based method consistently infers species trees under lateral gene transfer (LGT) if LGT is not excessive.
Area of Science:
- Evolutionary biology
- Phylogenetics
- Computational biology
Background:
- Inferring species trees from gene trees is complex due to evolutionary processes causing conflicts.
- Incomplete lineage sorting (ILS) is a known challenge, with existing solutions like three-taxon analyses.
- The efficacy of these methods under lateral gene transfer (LGT) remains less understood.
Purpose of the Study:
- To investigate the effectiveness of combining three-taxon analyses for species tree reconstruction under lateral gene transfer (LGT).
- To determine if triplet-based approaches are robust to LGT and ILS.
- To develop and implement a novel procedure for species tree reconstruction accounting for LGT and ILS.
Main Methods:
- Exact analysis of species tree inference models under LGT.
- Utilizing three-taxon analyses (gene tree topologies) to infer species trees.
- Connecting LGT models to random walks on cyclic graphs.
- Implementing a procedure for patchy taxon coverage scenarios.
Main Results:
- Majority-rule three-taxon gene trees can lead to inconsistent species tree reconstruction under LGT.
- A triplet-based approach consistently reconstructs species trees under LGT, given limited LGT.
- A novel connection between LGT and random walks on cyclic graphs was established.
- A practical procedure for reconstructing species trees under LGT or ILS was implemented.
Conclusions:
- Triplet-based methods offer a robust strategy for species tree reconstruction in the presence of LGT.
- The proposed method provides a consistent way to infer species trees even with patchy data.
- This work advances our understanding of phylogenetic inference under complex evolutionary scenarios like LGT and ILS.
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