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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
Published on: August 14, 2018
Assessing statistical reliability of phylogenetic trees via a speedy double bootstrap method
Aizhen Ren1, Takashi Ishida, Yutaka Akiyama
1Graduate School of Information Science and Engineering, Tokyo Institute of Technology, 2-12-1 Ookayama, Tokyo 152-8552, Japan. ren@bi.cs.titech.ac.jp
Molecular Phylogenetics and Evolution
|March 15, 2013
Summary
The speedy double bootstrap method accurately assesses phylogenetic tree reliability faster than traditional methods. This computational advance makes complex phylogenetic analyses more accessible for molecular evolution studies.
Area of Science:
- Computational Biology
- Molecular Phylogenetics
- Statistical Modeling
Background:
- Assessing phylogenetic tree reliability is crucial for accurate molecular phylogenetics.
- The standard bootstrap method can be biased, impacting reliability.
- Advanced methods like the double bootstrap are accurate but computationally intensive.
Purpose of the Study:
- To develop a computationally efficient double bootstrap method for phylogenetic tree assessment.
- To enable practical application of the double bootstrap in molecular phylogenetics.
- To offer a generalizable method for model selection using maximum likelihood.
Main Methods:
- Proposed the 'speedy double bootstrap' method, omitting the second-tier resampling step.
- Implemented the regular double bootstrap for comparative analysis.
- Tested methods on mammalian mitochondrial amino acid sequences and rRNA genes.
Main Results:
- The speedy double bootstrap shows no significant loss in accuracy compared to the regular double bootstrap.
- The speedy method is significantly faster, at least 371 times quicker in tested cases.
- Demonstrated the practical feasibility of double bootstrap for phylogenetic tree selection.
Conclusions:
- The speedy double bootstrap provides an accurate and computationally efficient alternative for assessing phylogenetic reliability.
- This method overcomes previous computational limitations, making double bootstrap viable for large-scale phylogenetics.
- The approach has broader applicability in model selection problems utilizing the maximum likelihood criterion.
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