GRIMER

GRIMER detects and analyzes potential contamination in microbiome datasets by integrating annotation, taxonomy, metadata, and external contaminant lists to support contamination assessment in low-biomass studies.

Key Features:

• Automated report generation: Produces contamination-detection reports automatically from contingency tables derived from microbiome count data.
• Integration of annotation, taxonomy, and metadata: Combines taxonomic annotation and sample metadata with observation-level information for cross-referenced analysis.
• Aggregation of multiple evidence sources: Unifies results from multiple contamination-detection methods and external evidence to strengthen contaminant identification.
• Quantification-method independence: Operates directly on contingency tables without reliance on specific quantification techniques.
• Contaminant database: References a compiled list from 22 published articles containing 210 genera and 627 species reported as potential contaminants.

Scientific Applications:

• Contamination detection in low-biomass studies: Identifies taxa and patterns indicative of contamination in samples with low microbial biomass.
• Quality control and study design support: Assists experimental design and early-stage quality assessment by highlighting noisy patterns and potential contaminants.
• Cross-referencing observed taxa: Enables comparison of observed taxa against a curated contaminant database to inform interpretation.
• Evidence integration for contamination assessment: Combines internal data signals with external contaminant lists and multiple detection methods to improve assessment reliability.

Methodology:

Constructs reports from contingency tables, integrates taxonomic annotation and sample metadata, aggregates results from multiple contamination-detection methods, and references a contaminant list compiled from 22 published articles (210 genera, 627 species).