Synthetic microbe communities provide internal reference standards for metagenome sequencing and analysis

Area of Science:

• Microbiology
• Genomics
• Bioinformatics

Background:

• Metagenomic analysis faces challenges due to microbial community complexity and technical biases in sequencing.
• Accurate quantification and normalization are crucial for reliable interpretation of metagenomic data.

Purpose of the Study:

• To develop and validate internal DNA standards, termed "sequins" (sequencing spike-ins), for improved metagenomic analysis.
• To provide a standardized tool for quantitative normalization and benchmarking of metagenomic methods.

Main Methods:

• Development of a synthetic community of artificial microbial genomes (sequins).
• Addition of sequins to environmental DNA samples before library preparation and sequencing.
• Validation using mock microbial communities and analysis of real metagenome samples.
• Demonstration of sequins for fold change measurement and method optimization.

Main Results:

• Sequins enable accurate measurement of fold change differences in microbial community size and structure.
• Quantitative normalization between samples is effectively performed using sequins.
• Sequins facilitate benchmarking and optimization of sequencing technologies, including nanopore long-read sequencing.

Conclusions:

• Sequins serve as valuable reference standards for enhancing the accuracy and reliability of metagenomic studies.
• The developed sequins, protocols, and software toolkit support broader adoption in the field.
• This approach addresses key limitations in current metagenomic analysis pipelines.