Scalable method for exploring phylogenetic placement uncertainty with custom visualizations using treeio and ggtree
Abstract In 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...
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| Format: | Article |
| Language: | English |
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Wiley
2025-02-01
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| Series: | iMeta |
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| Online Access: | https://doi.org/10.1002/imt2.269 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Meijun Chen |
| collection | DOAJ |
| description | Abstract In 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. |
| format | Article |
| id | doaj-art-38e10aea8b1a41d193c1ca0d253d5b38 |
| institution | DOAJ |
| issn | 2770-596X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | iMeta |
| spelling | doaj-art-38e10aea8b1a41d193c1ca0d253d5b382025-08-20T02:45:30ZengWileyiMeta2770-596X2025-02-0141n/an/a10.1002/imt2.269Scalable method for exploring phylogenetic placement uncertainty with custom visualizations using treeio and ggtreeMeijun Chen0Xiao Luo1Shuangbin Xu2Lin Li3Junrui Li4Zijing Xie5Qianwen Wang6Yufan Liao7Bingdong Liu8Wenquan Liang9Ke Mo10Qiong Song11Xia Chen12Tommy Tsan‐Yuk Lam13Guangchuang Yu14Department of Bioinformatics, School of Basic Medical Sciences Southern Medical University Guangzhou ChinaDepartment of Bioinformatics, School of Basic Medical Sciences Southern Medical University Guangzhou ChinaDepartment of Bioinformatics, School of Basic Medical Sciences Southern Medical University Guangzhou ChinaDepartment of Bioinformatics, School of Basic Medical Sciences Southern Medical University Guangzhou ChinaDepartment of Bioinformatics, School of Basic Medical Sciences Southern Medical University Guangzhou ChinaDepartment of Bioinformatics, School of Basic Medical Sciences Southern Medical University Guangzhou ChinaDepartment of Bioinformatics, School of Basic Medical Sciences Southern Medical University Guangzhou ChinaDepartment of Bioinformatics, School of Basic Medical Sciences Southern Medical University Guangzhou ChinaDepartment of Bioinformatics, School of Basic Medical Sciences Southern Medical University Guangzhou ChinaDepartment of Cell Biology, School of Basic Medical Sciences Southern Medical University Guangzhou ChinaDepartment of General Surgery, Zhujiang Hospital Southern Medical University Guangzhou ChinaBioinformation Center of BioInforCloud, YuanDong International Academy of Life Sciences Hong Kong SAR ChinaCentral Laboratory of the Medical Research Center The First Affiliated Hospital of Ningbo University Ningbo ChinaState Key Laboratory of Emerging Infectious Diseases, School of Public Health The University of Hong Kong Hong Kong SAR ChinaDepartment of Bioinformatics, School of Basic Medical Sciences Southern Medical University Guangzhou ChinaAbstract In 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.https://doi.org/10.1002/imt2.269ggtreephylogenetic placementplacement uncertaintytreeiovisualization |
| spellingShingle | 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 Scalable method for exploring phylogenetic placement uncertainty with custom visualizations using treeio and ggtree iMeta ggtree phylogenetic placement placement uncertainty treeio visualization |
| title | Scalable method for exploring phylogenetic placement uncertainty with custom visualizations using treeio and ggtree |
| title_full | Scalable method for exploring phylogenetic placement uncertainty with custom visualizations using treeio and ggtree |
| title_fullStr | Scalable method for exploring phylogenetic placement uncertainty with custom visualizations using treeio and ggtree |
| title_full_unstemmed | Scalable method for exploring phylogenetic placement uncertainty with custom visualizations using treeio and ggtree |
| title_short | Scalable method for exploring phylogenetic placement uncertainty with custom visualizations using treeio and ggtree |
| title_sort | scalable method for exploring phylogenetic placement uncertainty with custom visualizations using treeio and ggtree |
| topic | ggtree phylogenetic placement placement uncertainty treeio visualization |
| url | https://doi.org/10.1002/imt2.269 |
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