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Updated: Jun 16, 2026
08:57
Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
Published on: August 14, 2018
Efficient genome-scale phylogenetic analysis under the duplication-loss and deep coalescence cost models
Mukul S Bansal1, J Gordon Burleigh, Oliver Eulenstein
1School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel. bansal@tau.ac.il
BMC Bioinformatics
|February 4, 2010
Summary
New algorithms for gene tree parsimony now efficiently analyze large genomic datasets. This advance allows for more comprehensive phylogenetic analyses, incorporating complex evolutionary models for thousands of genes across hundreds of species.
Area of Science:
- Computational phylogenetics
- Genomic data analysis
- Evolutionary biology
Background:
- Genomic data offers vast potential for phylogenetic analysis.
- Existing phylogenetic methods struggle with large datasets and complex gene evolution processes like duplication, loss, and deep coalescence.
- Gene tree parsimony aims to find a species tree minimizing evolutionary events but current algorithms are too slow for large-scale data.
Purpose of the Study:
- To develop novel, efficient algorithms for gene tree parsimony.
- To enable phylogenetic analysis of large genomic datasets with duplication-loss and deep coalescence costs.
- To improve computational scalability for phylogenetic inference.
Main Methods:
- Developed novel algorithms for SPR (subtree pruning and regrafting) and TBR (tree bisection and reconnection) based local search heuristics.
- Adapted algorithms for both duplication-loss and deep coalescence reconciliation costs.
- Implemented and evaluated an SPR-based local search algorithm for the duplication-loss cost.
Main Results:
- New algorithms offer an n-fold improvement over existing methods, where n is the number of species.
- Demonstrated significant improvements in runtime and scalability with the implemented SPR algorithm.
- Successfully evaluated the algorithm's performance on three large-scale genomic datasets.
Conclusions:
- Novel algorithms enable gene tree parsimony analyses on unprecedented scales (thousands of genes, hundreds of taxa).
- This work expands the scope of phylogenetic analyses by accommodating larger datasets and complex evolutionary models.
- Facilitates more robust and comprehensive genome-scale phylogenetic reconstructions.
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