Macarron

Macarron prioritizes potentially bioactive metabolites from metabolomics experiments by integrating multiple lines of evidence including covariation with known metabolites, abundance relative to known metabolites, and associations with environmental or phenotypic indicators of bioactivity.

Key Features:

• Stratified clustering: Groups metabolic spectral features that co-vary in abundance under a given condition.
• Functional annotation transfer: Associates unannotated metabolic features with well-characterized metabolites.
• Relative abundance estimation: Quantifies feature abundance in relation to known metabolites.
• Differential abundance analysis: Identifies associations between metabolic features and phenotype or condition.
• Multi-evidence prioritization: Integrates covariation, relative abundance, and phenotypic association scores to rank potentially bioactive compounds.

Scientific Applications:

• Microbial metabolome characterization: Improves characterization of microbial communities by prioritizing bioactive metabolic features from epidemiological and environmental datasets.
• Inflammatory bowel disease research: Identifies potentially bioactive metabolites in IBD, including bile acids, short-chain fatty acids, bilirubin derivatives, polyamines, and vitamin derivatives.
• Therapeutic candidate discovery: Provides prioritized compounds for experimental validation, such as nicotinamide riboside which reduced colitis scores in DSS-treated mice.
• Unannotated feature characterization: Associates prevalent unannotated metabolic features with characterized metabolites to guide further biochemical investigation.

Methodology:

Macarron performs stratified clustering of metabolic spectral features based on co-variation in abundance, transfers functional annotations to uncharacterized features, estimates relative abundance compared to known metabolites, and conducts differential abundance analysis to link features with phenotypes or conditions.