The metagenomic data life-cycle: standards and best practices

Area of Science:

• Microbiology
• Bioinformatics
• Data Science

Background:

• Metagenomics research generates vast datasets, but comparing results across studies is challenging due to inconsistent analysis workflows.
• Standardization is needed for material sampling, sequencing, data analysis, and archiving in metagenomics.

Purpose of the Study:

• To map existing data standards for metagenomics workflows.
• To identify gaps in standardization, particularly in data analysis reporting and archiving.
• To propose best practices for community-wide adoption.

Main Methods:

• Literature review and landscape analysis of data standards in metagenomics.
• Case study examples from marine research to illustrate essential variables.
• Identification of critical areas lacking standardization.

Main Results:

• Standards exist for sampling and sequencing but require wider adoption.
• Significant lack of standardized reporting for metagenomics data analysis.
• Absence of clear guidelines for archiving and publishing analysis outputs.

Conclusions:

• Establishing community standards for metagenomics data analysis reporting and archiving is essential.
• Proposed best practices can form the foundation for future standardization efforts.
• Improved standardization will enhance reproducibility, data sharing, and repurposing of metagenomics datasets.