Testing Phylogenetic Stability with Variable Taxon Sampling

Area of Science:

• Computational Biology
• Phylogenomics
• Evolutionary Biology

Background:

• Rapid advancements in sequencing technology generate massive genomic datasets.
• Reconstructing large-scale phylogenetic trees is computationally intensive and model-limited.
• Current methods for reducing dataset size, like taxon sampling, are subjective and can influence tree topology.

Purpose of the Study:

• To address the challenges posed by large genomic datasets in phylogenomics.
• To develop and present a systematic approach for investigating the impact of taxon sampling on phylogenetic reconstructions.
• To provide a tool for assessing the accuracy of phylogenetic trees, particularly deep evolutionary nodes.

Main Methods:

• Development of an automated computational pipeline for exploring taxon sampling strategies.
• Application of the pipeline to analyze the effects of species selection on phylogenetic tree topology.
• Utilizing the pipeline in a case study involving Terrabacteria to evaluate deep phylogenetic nodes.

Main Results:

• The automated pipeline offers a practical and implementable method for exploring taxon sampling.
• Demonstrates that taxon sampling significantly influences phylogenetic tree reconstructions.
• The approach proved effective in a study of Terrabacteria, highlighting its utility in assessing deep phylogenetic accuracy.

Conclusions:

• Taxon sampling is a critical factor that requires thorough investigation in phylogenetic studies.
• The presented automated pipeline provides a robust framework for understanding and optimizing taxon sampling strategies.
• This methodology enhances the reliability and accuracy of large-scale phylogenetic analyses and evolutionary inferences.