CAMAMED

CAMAMED performs composition-aware mapping-based analysis of metagenomic sequencing data to generate taxonomic and functional profiles and gene frequency estimates for compositional analyses.

Key Features:

• Mapping to non-redundant gene catalogs: Maps metagenome sequences onto non-redundant gene catalogs to obtain gene frequency data.
• Composition-aware normalization (Cumulative Sum Scaling): Applies cumulative sum-scaling (CSS) at both taxa and gene levels to address compositionality in metagenomic data.
• KEGG integration: Extracts KEGG annotations including KEGG ortholog groups, enzyme commission (EC) numbers, and reactions across functional levels.
• Biomarker identification: Identifies functional differences between case-control metagenomic samples for biomarker discovery.
• Mapping-based approach: Employs mapping-based methods as an alternative to assembly-based approaches when appropriate gene catalogs are available, particularly for functional-level analyses.

Scientific Applications:

• Taxonomic profiling: Generates taxa-level profiles from metagenomic samples using gene-mapping-derived abundances.
• Functional profiling and pathway analysis: Derives functional profiles and metabolic pathway representation via KEGG orthologs, EC numbers, and reactions.
• Biomarker discovery in case-control studies: Detects functional biomarkers by comparing case and control metagenomic samples.

Methodology:

Maps metagenome sequences onto non-redundant gene catalogs to compute gene frequencies, applies cumulative sum-scaling (CSS) at taxa and gene levels, extracts KEGG annotations (KEGG orthologs, EC numbers, reactions), and performs comparative analysis of functional differences between case-control samples using mapping-based methods rather than assembly-based approaches when appropriate gene catalogs are available.