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

• DOI: 10.1002/imt2.269
• Publication Date: 2025-01-13T00:40:13Z
• Authors: Meijun Chen; Xiao Luo; Shuangbin Xu; Lin Li; Junrui Li; Zijing Xie; Qianwen Wang; Yufan Liao; Bingdong Liu; Wenquan Liang; Ke Mo; Qiong Song; Xia Chen; Tommy Tsan‐Yuk Lam; Guangchuang Yu
• Keywords: phylogenetic placement; treeio; Computer applications to medicine. Medical informatics; R858-859.7; ggtree; visualization; placement uncertainty

AbstractIn metabarcoding research, such as taxon identification, phylogenetic placement plays a critical role. However, many existing phylogenetic placement methods lack comprehensive features for downstream analysis and visualization. Visualization tools often ignore placement uncertainty, making it difficult to explore and interpret placement data effectively. To overcome these limitations, we introduce a scalable approach using treeio and ggtree for parsing and visualizing phylogenetic placement data. The treeio‐ggtree method supports placement filtration, uncertainty exploration, and customized visualization. It enhances scalability for large analyses by enabling users to extract subtrees from the full reference tree, focusing on specific samples within a clade. Additionally, this approach provides a clearer representation of phylogenetic placement uncertainty by visualizing associated placement information on the final placement tree.