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PhyloBARCODER: A Web Tool for Phylogenetic Classification of Eukaryote Metabarcodes Using Custom Reference Databases.


ABSTRACT: We developed phyloBARCODER (https://github.com/jun-inoue/phyloBARCODER), a new web tool that can identify short DNA sequences to the species level using metabarcoding. phyloBARCODER estimates phylogenetic trees based on the uploaded anonymous DNA sequences and reference sequences from databases. Without such phylogenetic contexts, alternative, similarity-based methods independently identify species names and anonymous sequences of the same group by pairwise comparisons between queries and database sequences, with the caveat that they must match exactly or very closely. By putting metabarcoding sequences into a phylogenetic context, phyloBARCODER accurately identifies (i) species or classification of query sequences and (ii) anonymous sequences associated with the same species or even with populations of query sequences, with clear and accurate explanations. Version 1 of phyloBARCODER stores a database comprising all eukaryotic mitochondrial gene sequences. Moreover, by uploading their own databases, phyloBARCODER users can conduct species identification specialized for sequences obtained from a local geographic region or those of nonmitochondrial genes, e.g. ITS or rbcL.

SUBMITTER: Inoue J 

PROVIDER: S-EPMC11297486 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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phyloBARCODER: A Web Tool for Phylogenetic Classification of Eukaryote Metabarcodes Using Custom Reference Databases.

Inoue Jun J   Shinzato Chuya C   Hirai Junya J   Itoh Sachihiko S   Minegishi Yuki Y   Ito Shin-Ichi SI   Hyodo Susumu S  

Molecular biology and evolution 20240801 8


We developed phyloBARCODER (https://github.com/jun-inoue/phyloBARCODER), a new web tool that can identify short DNA sequences to the species level using metabarcoding. phyloBARCODER estimates phylogenetic trees based on the uploaded anonymous DNA sequences and reference sequences from databases. Without such phylogenetic contexts, alternative, similarity-based methods independently identify species names and anonymous sequences of the same group by pairwise comparisons between queries and databa  ...[more]

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