Chen Meijun, Luo Xiao, Xu Shuangbin, Li Lin, Li Junrui, Xie Zijing, Wang Qianwen, Liao Yufan, Liu Bingdong, Liang Wenquan, Mo Ke, Song Qiong, Chen Xia, Lam Tommy Tsan-Yuk, Yu Guangchuang
Department of Bioinformatics, School of Basic Medical Sciences Southern Medical University Guangzhou China.
Department of Cell Biology, School of Basic Medical Sciences Southern Medical University Guangzhou China.
Imeta. 2025 Jan 12;4(1):e269. doi: 10.1002/imt2.269. eCollection 2025 Feb.
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 and for parsing and visualizing phylogenetic placement data. The - 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.
在元条形码研究中，如分类群鉴定，系统发育定位起着关键作用。然而，许多现有的系统发育定位方法缺乏用于下游分析和可视化的全面功能。可视化工具常常忽略定位的不确定性，使得难以有效地探索和解释定位数据。为了克服这些限制，我们引入了一种可扩展的方法，使用[具体工具1]和[具体工具2]来解析和可视化系统发育定位数据。[具体方法名称]方法支持定位过滤、不确定性探索和定制可视化。它通过允许用户从完整的参考树中提取子树，专注于一个分支内的特定样本，增强了大型分析的可扩展性。此外，这种方法通过在最终定位树上可视化相关的定位信息，更清晰地呈现了系统发育定位的不确定性。
