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A Practical Guide to Phylogenetics for Nonexperts
Published on: February 5, 2014
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Species trees from consensus single nucleotide polymorphism (SNP) data: Testing phylogenetic approaches with
Alexander N Schmidt-Lebuhn1, Nicola C Aitken2, Aaron Chuah3
1CSIRO, Australian National Herbarium, Clunies Ross Street, Canberra, ACT 2601, Australia.
Molecular Phylogenetics and Evolution
|July 27, 2017
Summary
Comparing methods for inferring species trees from SNP data, this study finds that Bayesian and parsimony analyses of summarized data best approximate results from computationally intensive methods, especially with missing data. These approaches offer efficient alternatives for phylogenetic analysis.
Area of Science:
- Population genetics
- Phylogenetics
- Bioinformatics
Background:
- Single Nucleotide Polymorphisms (SNPs) are crucial for studying population structure and phylogenetics.
- Current methods like SNAPP are computationally demanding and sensitive to missing data.
- Efficient alternatives are needed for analyzing large SNP datasets.
Purpose of the Study:
- To compare species tree inference methods using SNP data.
- To evaluate the impact of data summarization and missing data on phylogenetic accuracy.
- To identify robust and efficient alternatives to SNAPP.
Main Methods:
- Comparison of SNAPP, SVD quartets, Bayesian, and parsimony analyses.
- Utilized simulated and empirical SNP data from plant species.
- Tested various data summarization strategies for Bayesian and parsimony analyses.
Main Results:
- SVD quartets and SNAPP accurately retrieved topologies from simulated data, but failed with high missing data.
- Bayesian and parsimony analyses using allele-wise summarization yielded trees closest to true topologies.
- For empirical data, Bayesian and Dollo parsimony analyses were most congruent with SNAPP results.
Conclusions:
- Data summarization approaches offer efficient alternatives for species tree inference from SNP data.
- These methods are particularly valuable for computationally demanding or incomplete datasets.
- Careful selection of data summarization is key for accurate phylogenetic reconstruction.
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