Differential richness inference for 16S rRNA marker gene surveys

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• Microbial community diversity is linked to health outcomes.
• High-throughput 16S rRNA gene sequencing is widely used for microbiome analysis.
• 16S rRNA surveys often generate spurious microbial taxa, particularly at sub-genus levels.

Purpose of the Study:

• To address the confounding effect of spurious taxa on microbiome diversity analysis.
• To develop a robust method for differential richness inference in microbiome studies.
• To enable the derivation of reliable health indicators from microbiome data.

Main Methods:

• Modeling systematic patterns of sub-genus taxa generation as a function of genus abundance.
• Developing a control for false taxa accumulation in 16S rRNA data.
• Utilizing classical regression approaches for differential richness inference.

Main Results:

• Most sub-genus taxa identified in 16S rRNA surveys are spurious artifacts.
• Ignoring spurious taxa inflates observed richness and confounds differential richness inference.
• The proposed method effectively controls for false taxa accumulation, enabling accurate differential richness inference.

Conclusions:

• False discoveries in microbiome data bias richness estimation and confound analysis.
• A novel method, implemented in the R package Prokounter, corrects for spurious taxa in 16S rRNA data.
• This approach facilitates more reliable microbiome-based health indicator development.