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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
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microclass: an R-package for 16S taxonomy classification.

Kristian Hovde Liland1,2, Hilde Vinje1, Lars Snipen3

  • 1Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, P.O. Box 5003, N-1432, Norway.

BMC Bioinformatics
|March 18, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces an R package for accurate microbial community profiling using 16S rRNA gene sequences. The tool quantifies classification uncertainties and provides high-quality taxonomic assignments within the R environment.

Keywords:
16SRTaxonomy

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate taxonomic classification of microbial communities relies on 16S rRNA gene sequencing.
  • Quantifying uncertainties in these classifications is crucial for reliable microbial profiling.

Purpose of the Study:

  • To present an R package for microbial taxonomic classification using 16S rRNA gene sequences.
  • To provide tools for quantifying uncertainties in taxonomic assignments.
  • To enable high-quality taxonomic assignments from variable sequence data.

Main Methods:

  • Development of an R package with C++ for computational efficiency.
  • Inclusion of a ready-to-use classifier trained on a comprehensive dataset.
  • Implementation of novel methods for uncertainty quantification in classifications.

Main Results:

  • The R package facilitates accurate taxonomic classification of microbial communities.
  • It offers robust methods for quantifying uncertainties associated with classifications.
  • Users can train custom classifiers or utilize a pre-trained, comprehensive classifier.

Conclusions:

  • The package enables high-quality taxonomic assignments from 16S sequences of varying length and quality within R.
  • It provides valuable tools for microbial community analysis and uncertainty assessment.
  • The R package is publicly available via the Comprehensive R Archive Network (CRAN).