Benchmarking bacterial taxonomic classification using nanopore metagenomics data of several mock communities

Taxonomic classification is crucial in identifying organisms within diverse microbial communities when using metagenomics shotgun sequencing. While second-generation Illumina sequencing still dominates, third-generation nanopore sequencing promises improved classification through longer reads. Howev...

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Bibliographic Details
Published in:Scientific data 2024-08, Vol.11 (1), p.864-17, Article 864
Main Authors: Van Uffelen, Alexander, Posadas, Andrés, Roosens, Nancy H. C., Marchal, Kathleen, De Keersmaecker, Sigrid C. J., Vanneste, Kevin
Format: Article
Language:English
Subjects:
631/114/1386
631/61/212/2142
Analysis
Bacteria - classification
Bacteria - genetics
Classification
Datasets as Topic
Humanities and Social Sciences
Metagenomics
Metagenomics - methods
Microbiota
multidisciplinary
Nanopore Sequencing
Nanopores
Science
Science (multidisciplinary)
Taxonomy
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Description
Summary:Taxonomic classification is crucial in identifying organisms within diverse microbial communities when using metagenomics shotgun sequencing. While second-generation Illumina sequencing still dominates, third-generation nanopore sequencing promises improved classification through longer reads. However, extensive benchmarking studies on nanopore data are lacking. We systematically evaluated performance of bacterial taxonomic classification for metagenomics nanopore sequencing data for several commonly used classifiers, using standardized reference sequence databases, on the largest collection of publicly available data for defined mock communities thus far (nine samples), representing different research domains and application scopes. Our results categorize classifiers into three categories: low precision/high recall; medium precision/medium recall, and high precision/medium recall. Most fall into the first group, although precision can be improved without excessively penalizing recall with suitable abundance filtering. No definitive ‘best’ classifier emerges, and classifier selection depends on application scope and practical requirements. Although few classifiers designed for long reads exist, they generally exhibit better performance. Our comprehensive benchmarking provides concrete recommendations, supported by publicly available code for reassessment and fine-tuning by other scientists.
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-024-03672-8