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Related Experiment Video

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Richness estimation in microbiome data obtained from denoising pipelines.

Sven Kleine Bardenhorst1, Marius Vital2, André Karch1

  • 1Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.

Computational and Structural Biotechnology Journal
|January 24, 2022
PubMed
Summary

Rarefaction and sub-sampling are inappropriate for microbiome richness estimation with default bioinformatics pipelines. Pooled processing in DADA2 resolves spurious correlations between sequencing depth and richness.

Keywords:
DenoisingMicrobiomeRarefactionSequencing depthSub-sampling

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Microbiome studies often quantify species richness, a metric highly dependent on sequencing depth.
  • Rarefaction curves are used to assess this dependency, but may not fully resolve depth-related biases.
  • Current bioinformatics pipelines can introduce variations confounding richness estimates.

Purpose of the Study:

  • To investigate the impact of DADA2 and deblur pipeline settings on microbiome richness estimates.
  • To determine the suitability of rarefaction and sub-sampling for richness estimation.
  • To identify and resolve spurious correlations between sequencing depth and richness.

Main Methods:

  • Analysis of richness estimates using DADA2 and deblur pipelines.
  • Comparison of sample-wise versus pooled processing approaches.
  • Evaluation of rarefaction and sub-sampling techniques.

Main Results:

  • Rarefaction and sub-sampling are inappropriate with default DADA2/deblur settings.
  • Independent sample-wise processing in DADA2 creates spurious correlations between sequencing depth and richness.
  • Pooled processing in DADA2 effectively resolves these spurious correlations.

Conclusions:

  • Default bioinformatics pipeline settings complicate accurate microbiome richness estimation.
  • Rarefaction is not a universally appropriate method for correcting sequencing depth biases.
  • Pooled processing offers a robust solution for accurate richness estimation in DADA2 microbiome data.