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Modern Molecular Taxonomy01:29

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Classification is the process of organizing organisms into hierarchically inclusive groups based on their phenotypic similarities or evolutionary relationships. A species comprises one or more strains, and closely related species are grouped into genera. Genera are further classified into families, families into orders, orders into classes, and so forth, up to the domain level, which is the broadest taxonomic rank derived from a combination of phenotypic and genotypic data.The nomenclature of...
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Related Experiment Video

Updated: Sep 26, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
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Efficient and Quality-Optimized Metagenomic Pipeline Designed for Taxonomic Classification in Routine Microbiological

Sylvie Buffet-Bataillon1, Guillaume Rizk2, Vincent Cattoir3

  • 1Inserm, Institut NUMECAN (Nutrition Metabolisms and Cancer), CHU Rennes, Univ Rennes, F-35000 Rennes, France.

Microorganisms
|April 23, 2022
PubMed
Summary

This study introduces a robust methodology for interpreting clinical metagenomics data, enhancing the accuracy of microbial identification and abundance assessment for disease diagnosis. The developed quality control pipeline improves the reliability of microbiota analysis in clinical microbiology.

Keywords:
BrackenKaijuKraken2clinical microbiologymetagenomicsmicrobiomemycobiomequality assessmentshotgunvirome

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • Metagenomics analysis is increasingly used for clinical diagnosis.
  • Reliable interpretation of metagenomics data is crucial for clinical microbiology.
  • Current methods require enhanced quality assessment for diagnostic accuracy.

Purpose of the Study:

  • To propose a robust methodology for taxon identification and abundance assessment of shotgun sequencing data.
  • To develop processing steps and quality controls for clinical metagenomics data interpretation.
  • To improve the accuracy and robustness of microbiota studies in a clinical setting.

Main Methods:

  • Developed quality control steps to filter low-quality reads and optimize abundance thresholds.
  • Combined multiple classifiers (e.g., Kraken2/Bracken, Kaiju) and reference databases for species classification.
  • Incorporated an external positive control for enhanced data validation.
  • Assessed prokaryotic and eukaryotic sequences for comprehensive microbial profiling.

Main Results:

  • The proposed methodology effectively identifies microbial taxa and assesses their abundance from shotgun sequencing data.
  • A pipeline combining Kraken2/Bracken and Kaiju demonstrated optimal performance for classification and abundance profiling.
  • Quality control measures successfully filtered low-quality data and optimized abundance thresholds.
  • The approach ensures accurate species identification and abundance profiles for both prokaryotes and eukaryotes.

Conclusions:

  • The developed methodology provides a significant advancement for interpreting metagenomics data in clinical diagnostics.
  • Implementing these quality assessment steps enhances the robustness of biological and clinical conclusions from metagenomic studies.
  • This approach is well-suited for clinical microbiology, improving diagnostic confidence and microbiota analysis.