metabolic

metabolic profiles metabolic and biogeochemical functional traits from metagenome-assembled genomes (MAGs), single-cell amplified genomes (SAGs), and isolate genomes to assess microbial metabolic capabilities and community contributions to biogeochemical cycles.

Key Features:

• Genome-scale annotation: Performs comprehensive annotation of MAGs, SAGs, and isolate genomes to identify metabolic functions.
• Motif validation: Uses biochemically validated conserved protein residues for motif-based validation of functional annotations.
• Metabolic marker detection: Identifies key marker genes indicative of specific metabolic pathways.
• Pathway analysis: Analyzes metabolic pathways and their contributions to biogeochemical transformations.
• Community-scale analyses: Estimates genome abundances, detects potential metabolic handoffs and metabolite exchange, and calculates community-level contributions to biogeochemical cycles.
• Metrics and network scoring: Computes community-scale metabolic networks and the MN-score (metabolic network score) for network quantification.
• Outputs and visualizations: Produces tabular metabolism summaries, biogeochemical cycling potential diagrams, sequential metabolic transformation representations, and community-scale metabolic network visualizations.
• HMM-based searches: Employs hmmsearch as a compute-intensive HMMER step for profile HMM searches.
• Implementation: Implemented using Perl and R.
• Computational performance: Processes ~100 genomes in ~3 hours using 40 CPU threads with hmmsearch taking ~45 minutes and scales to larger datasets (e.g., ~3600 genomes with hmmsearch ~5 hours).

Scientific Applications:

• Environmental metagenomics: Applied to marine subsurface, terrestrial subsurface, meadow soil, deep sea, freshwater lake, and wastewater metagenomic datasets to profile metabolic potential.
• Human microbiome analysis: Applied to human gut metagenomes to assess microbial metabolic capabilities.
• Community ecology and biogeochemistry: Enables consistent analyses of microbial community ecology and quantification of contributions to biogeochemical cycles, including incorporation of uncultivated organisms into metabolic models.

Methodology:

Annotation of genomes, HMM-based searches using hmmsearch, motif validation with conserved protein residues, detection of metabolic marker genes, pathway analysis, genome abundance estimation, calculation of community-level metrics including the MN-score, and generation of tabular and diagrammatic visualizations; implemented in Perl and R.