Scalable method for exploring phylogenetic placement uncertainty with custom visualizations using treeio and ggtree

Area of Science:

• Bioinformatics
• Computational Biology
• Phylogenetics

Background:

• Phylogenetic placement is crucial for taxon identification in metabarcoding.
• Existing methods often lack comprehensive features for downstream analysis and visualization.
• Current visualization tools frequently overlook placement uncertainty, hindering effective data interpretation.

Purpose of the Study:

• To introduce a scalable approach for parsing and visualizing phylogenetic placement data.
• To address limitations in existing phylogenetic placement methods regarding downstream analysis and visualization.
• To improve the exploration and interpretation of phylogenetic placement data, especially concerning uncertainty.

Main Methods:

• Developed a scalable approach using the treeio and ggtree R packages.
• Implemented features for placement filtration and uncertainty exploration.
• Enabled customized visualization of phylogenetic placement data.

Main Results:

• The treeio-ggtree method supports scalable analysis by allowing subtree extraction for focused examination.
• The approach provides clearer representation of phylogenetic placement uncertainty through visualization.
• Facilitates enhanced downstream analysis and interpretation of metabarcoding data.

Conclusions:

• The treeio-ggtree method offers a robust and scalable solution for phylogenetic placement visualization in metabarcoding.
• This approach improves the handling of placement uncertainty, leading to more reliable taxon identification.
• Enhances the utility of phylogenetic placement data for research and interpretation.