CVTree for 16S rRNA:Constructing taxonomy-compatible all-species living tree effectively and efficiently

【摘要】 The composition vector tree(CVTree) method, developed under the leadership of Professor Hao Bailin, is an alignment-free algorithm for constructing phylogenetic trees. Although initially designed for studying prokaryotic evolution based on whole-genome, it has demonstrated broad applicability across diverse biological systems and gene sequences.In this study, we employed two methods, Inter List and Hao, of CVTree to investigate the phylogeny and taxonomy of prokaryote based on the 16S rRNA sequences from All-Species Living Tree Project. We have established a comprehensive phylogenetic tree that incorporates the majority of species documented in human scientific knowledge and compared it with the taxonomy of prokaryotes. And the performance of CVTree was also compared with multiple sequence alignment-based approaches. Our results revealed that CVTree methods achieve computational speeds 1–3 orders of magnitude faster than conventional alignment methods while maintaining high consistency with established taxonomic relationships, even outperforming some multiple sequence alignment methods. These findings confirm CVTree’s effectiveness and efficiency not only for whole-genome evolutionary studies but also for phylogenetic and taxonomic investigations based on genes.更多还原

【Abstract】 The composition vector tree(CVTree) method, developed under the leadership of Professor Hao Bailin, is an alignment-free algorithm for constructing phylogenetic trees. Although initially designed for studying prokaryotic evolution based on whole-genome, it has demonstrated broad applicability across diverse biological systems and gene sequences.In this study, we employed two methods, Inter List and Hao, of CVTree to investigate the phylogeny and taxonomy of prokaryote based on the 16S rRNA sequences from All-Species Living Tree Project. We have established a comprehensive phylogenetic tree that incorporates the majority of species documented in human scientific knowledge and compared it with the taxonomy of prokaryotes. And the performance of CVTree was also compared with multiple sequence alignment-based approaches. Our results revealed that CVTree methods achieve computational speeds 1–3 orders of magnitude faster than conventional alignment methods while maintaining high consistency with established taxonomic relationships, even outperforming some multiple sequence alignment methods. These findings confirm CVTree’s effectiveness and efficiency not only for whole-genome evolutionary studies but also for phylogenetic and taxonomic investigations based on genes.更多还原