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mTAGs: taxonomic profiling using degenerate consensus reference sequences of ribosomal RNA genes.
Salazar, Guillem; Ruscheweyh, Hans-Joachim; Hildebrand, Falk; Acinas, Silvia G; Sunagawa, Shinichi.
Afiliação
  • Salazar G; Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, 8093 Zürich, Switzerland.
  • Ruscheweyh HJ; Department of Biology, Institute of Microbiology and Swiss Institute of Bioinformatics, ETH Zürich, 8093 Zürich, Switzerland.
  • Hildebrand F; Department of Gut Microbes and Health, Quadram Institute Bioscience, NR4 7UQ Norwich, UK.
  • Acinas SG; Department of Digital Biology, Earlham Institute, NR4 7UZ Norwich, UK.
  • Sunagawa S; Department of Marine Biology and Oceanography, Institute of Marine Sciences (ICM)-CSIC, 08003 Barcelona, Spain.
Bioinformatics ; 38(1): 270-272, 2021 12 22.
Article em En | MEDLINE | ID: mdl-34260698
ABSTRACT
Profiling the taxonomic composition of microbial communities commonly involves the classification of ribosomal RNA gene fragments. As a trade-off to maintain high classification accuracy, existing tools are typically limited to the genus level. Here, we present mTAGs, a taxonomic profiling tool that implements the alignment of metagenomic sequencing reads to degenerate consensus reference sequences of small subunit ribosomal RNA genes. It uses DNA fragments, that is, paired-end sequencing reads, as count units and provides relative abundance profiles at multiple taxonomic ranks, including operational taxonomic units based on a 97% sequence identity cutoff. At the genus rank, mTAGs outperformed other tools across several metrics, such as the F1 score by >11% across data from different environments, and achieved competitive (F1 score) or better results (Bray-Curtis dissimilarity) at the sub-genus level. AVAILABILITY AND IMPLEMENTATION The software tool mTAGs is implemented in Python. The source code and binaries are freely available (https//github.com/SushiLab/mTAGs). The data underlying this article are available in Zenodo, at https//doi.org/10.5281/zenodo.4352762. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Microbiota Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Suíça

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Microbiota Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Suíça