Unveiling errors in soil microbial community sequencing: a case for reference soils and improved diagnostics for...

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Microbial community analyses rely on amplicon sequencing, but workflow steps can introduce errors.
• Reproducibility and data validation are critical for reliable microbial ecology studies.

Purpose of the Study:

• To evaluate sources of variability and error in a standardized amplicon sequencing workflow.
• To assess the effectiveness of common diagnostics for detecting workflow abnormalities.
• To propose new techniques for ensuring accuracy and reproducibility in soil microbial community analyses.

Main Methods:

• Cross-laboratory evaluation of DNA extraction, PCR, and sequencing steps.
• Utilized Oxford Nanopore MinION sequencing for agricultural soil and mock communities.
• Assessed variability and error impact on diversity estimates.

Main Results:

• Variability and errors were observed at each workflow stage, particularly PCR and library size.
• Standard bioinformatic diagnostics and mock communities failed to detect PCR abnormalities.
• Proposed diagnostic checks and reference soils improve data accuracy.

Conclusions:

• Workflow steps, especially PCR, significantly influence soil microbial community data.
• New diagnostic strategies are essential for reliable amplicon sequencing results.
• Implementing these checks enhances data validity and inter-laboratory comparisons.