ArticleBMC bioinformatics2017
microclass: an R-package for 16S taxonomy classification.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Effects of different additives on fermentation characteristics, nutrient composition and microbial communities of Leymus chinensis silage.BMC microbiology · 2025Article
- Article
- Hitac: a hierarchical taxonomic classifier for fungal ITS sequences compatible with QIIME2.BMC bioinformatics · 2024Article
- Cervicovaginal Microbial-Immune State and Group B Streptococcus Colonization in Pregnancy.American journal of perinatology · 2024Article
- Article
- Construction of habitat-specific training sets to achieve species-level assignment in 16S rRNA gene datasets.Microbiome · 2020Article
- Microbiome data science.Journal of biosciences · 2019Review
- DAIRYdb: a manually curated reference database for improved taxonomy annotation of 16S rRNA gene sequences from dairy products.BMC genomics · 2019Article
- IDTAXA: a novel approach for accurate taxonomic classification of microbiome sequences.Microbiome · 2018Article
- Article
- Evaluating Established Methods for Rumen 16S rRNA Amplicon Sequencing With Mock Microbial Populations.Frontiers in microbiology · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTaxonomic classification based on the 16S rRNA gene sequence is important for the profiling of microbial communities. In addition to giving the best possible accuracy, it is also important to quantify uncertainties in the classifications.
resultsWe present an R package with tools for making such classifications, where the heavy computations are implemented in C++ but operated through the standard R interface. The user may train classifiers based on specialized data sets, but we also supply a ready-to-use function trained on a comprehensive training data set designed specifically for this purpose. This tool also includes some novel ways to quantify uncertainties in the classifications.
conclusionsBased on input sequences of varying length and quality, we demonstrate how the output from the classifications can be used to obtain high quality taxonomic assignments from 16S sequences within the R computing environment. The package is publicly available at the Comprehensive R Archive Network.
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Registered trials
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