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Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT.


ABSTRACT: Current-day metagenomics analyses increasingly involve de novo taxonomic classification of long DNA sequences and metagenome-assembled genomes. Here, we show that the conventional best-hit approach often leads to classifications that are too specific, especially when the sequences represent novel deep lineages. We present a classification method that integrates multiple signals to classify sequences (Contig Annotation Tool, CAT) and metagenome-assembled genomes (Bin Annotation Tool, BAT). Classifications are automatically made at low taxonomic ranks if closely related organisms are present in the reference database and at higher ranks otherwise. The result is a high classification precision even for sequences from considerably unknown organisms.

SUBMITTER: von Meijenfeldt FAB 

PROVIDER: S-EPMC6805573 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT.

von Meijenfeldt F A Bastiaan FAB   Arkhipova Ksenia K   Cambuy Diego D DD   Coutinho Felipe H FH   Dutilh Bas E BE  

Genome biology 20191022 1


Current-day metagenomics analyses increasingly involve de novo taxonomic classification of long DNA sequences and metagenome-assembled genomes. Here, we show that the conventional best-hit approach often leads to classifications that are too specific, especially when the sequences represent novel deep lineages. We present a classification method that integrates multiple signals to classify sequences (Contig Annotation Tool, CAT) and metagenome-assembled genomes (Bin Annotation Tool, BAT). Classi  ...[more]

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