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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
Published on: August 14, 2018
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Fast and accurate branch lengths estimation for phylogenomic trees
Manuel Binet1,2,3, Olivier Gascuel4,5, Celine Scornavacca6,7
1Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), CNRS, Université de Montpellier, Montpellier, France. binet@lirmm.fr.
BMC Bioinformatics
|January 9, 2016
Summary
A new method, ERaBLE (Evolutionary Rates and Branch Length Estimation), efficiently estimates branch lengths in large phylogenomic datasets. This approach complements supertree methods and offers an alternative to computationally intensive analyses.
Area of Science:
- Evolutionary Biology
- Phylogenomics
- Bioinformatics
Background:
- Branch lengths in phylogenetic trees are crucial for evolutionary studies.
- Current supertree methods often prioritize tree topology over branch lengths.
- Existing branch length estimation methods struggle with large genomic datasets due to computational demands.
Purpose of the Study:
- To introduce ERaBLE (Evolutionary Rates and Branch Length Estimation), a novel phylogenomic method.
- To estimate branch lengths from a reference topology and relative evolutionary rates of genes.
- To address the limitations of existing methods for large-scale phylogenomic data.
Main Methods:
- Developed a distance-based phylogenomic method named ERaBLE.
- Utilizes a collection of distance matrices derived from genomic regions or gene trees.
- Designed for efficient processing of large datasets.
Main Results:
- ERaBLE demonstrates high speed and accuracy compared to existing approaches.
- Successfully handles very large datasets, exemplified by the OrthoMaM v8 database.
- Provides accurate estimation of branch lengths and relative evolutionary rates.
Conclusions:
- ERaBLE serves as a valuable complement to supertree methods.
- Offers an efficient alternative to traditional maximum likelihood analysis for large phylogenomic datasets.
- Facilitates more practical branch length estimation in evolutionary biology.
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