A sensitivity analysis of methodological variables associated with microbiome measurements

Area of Science:

• Microbiome research
• Metagenomic sequencing
• Bioinformatics

Background:

• Metagenomic sequencing analyses of microbiome samples are sensitive to experimental methods, leading to significant inter-laboratory variability.
• Quantifying method-specific bias is crucial but challenging for accurate microbiome characterization.

Purpose of the Study:

• To systematically evaluate the impact of various methodological choices on metagenomic sequencing results.
• To develop a framework for quantitatively assessing and correcting method biases in microbiome data.

Main Methods:

• A full factorial experimental design was employed, varying factors such as sample, operator, lot, DNA extraction kit, variable region, and reference database.
• Main effects were calculated to compare methodological biases against real biological differences.
• Computational correction methods were applied to harmonize data from different protocols.

Main Results:

• Methodological bias was found to be of similar magnitude to real biological differences in microbiome samples.
• Biases varied significantly across different taxa, even closely related genera.
• Computational correction using reference material successfully harmonized results from diverse protocols.

Conclusions:

• Methodological choices introduce substantial bias in metagenomic sequencing, comparable to biological variation.
• A quantitative framework for assessing method bias can improve understanding and comparability of microbiome datasets.
• Computational correction offers a viable strategy to mitigate method-specific biases and enhance data integration.