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

Meijun Chen1,2, Xiao Luo1, Shuangbin Xu1

  • 1Department of Bioinformatics, School of Basic Medical Sciences Southern Medical University Guangzhou China.

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Summary

This study introduces a new method for visualizing phylogenetic placements in metabarcoding, improving taxon identification. The treeio-ggtree approach enhances scalability and clarifies placement uncertainty for better data interpretation.

Keywords:
ggtreephylogenetic placementplacement uncertaintytreeiovisualization

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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.