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Related Experiment Video

Updated: Dec 22, 2025

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
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Sequence count data are poorly fit by the negative binomial distribution.

Stijn Hawinkel1, J C W Rayner2,3, Luc Bijnens4,5

  • 1Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium.

Plos One
|May 1, 2020
PubMed
Summary
This summary is machine-generated.

The negative binomial (NB) distribution assumption is frequently violated in RNA-Seq and microbiome data analysis. Nonparametric tests are recommended over NB-based methods for reliable false discovery rate control.

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Area of Science:

  • Bioinformatics
  • Statistical modeling
  • Genomic data analysis

Background:

  • Sequence count data, common in RNA-Seq and microbiome studies, are often modeled using the negative binomial (NB) distribution.
  • Empirical evidence suggests NB-based methods may fail to adequately control the false discovery rate (FDR).

Purpose of the Study:

  • To develop and apply a statistical goodness-of-fit test for the NB distribution in regression models.
  • To evaluate the validity of the NB assumption in publicly available RNA-Seq and 16S rRNA microbiome datasets.

Main Methods:

  • Proposed a dedicated goodness-of-fit test for NB regression models.
  • Assessed NB distribution fit on RNA-Seq and 16S rRNA microbiome datasets.
  • Compared performance of NB-based tests on data with and without NB assumption violations.

Main Results:

  • The NB assumption was found to be violated in numerous public RNA-Seq and 16S rRNA microbiome datasets.
  • Zero-inflated NB models did not offer a substantially improved fit.
  • NB-based tests performed poorly on datasets violating the NB assumption.

Conclusions:

  • The NB assumption is often inappropriate for RNA-Seq and microbiome sequence count data.
  • Violations of the NB assumption explain the poor performance of NB-based statistical tests in evaluations.
  • Nonparametric statistical tests are recommended over parametric NB-based methods for analyzing such data.