WasteFams

WasteFams catalogs non-redundant protein families from wastewater and WWTP ecosystems, integrating metagenomic, metatranscriptomic, and reference genome datasets with structural, functional, taxonomic, and ecological annotations to enable discovery of novel microbial functions and monitoring of resistance mechanisms.

Key Features:

• Protein Family Database: WasteFams catalogs 3887 non-redundant protein families containing >=100 members, derived from over 105 million predicted proteins.
• Multi-omics Data Integration: The database integrates data from 580 metagenomes, 132 metatranscriptomes, and 1709 reference genomes (504 metagenomes, 129 metatranscriptomes, and 1151 reference genomes after filtering).
• Structural Predictions: Each protein family is enriched with AlphaFold3 structural predictions.
• Taxonomic and Ecological Annotations: Protein families include taxonomic classifications and biome-specific metadata.
• Genomic Context Analysis: Deep genomic context analysis links protein families to mobile genetic elements, biosynthetic gene clusters, antibiotic resistance genes, and CRISPR elements.
• Sequence and Structural Similarity Tools: The platform provides sequence and structural similarity search capabilities.

Scientific Applications:

• Novel Microbial Function Discovery: WasteFams enables discovery of novel microbial functions in wastewater-engineered ecosystems.
• Antibiotic Resistance Monitoring: The database supports monitoring of antibiotic resistance mechanisms in wastewater microbiomes.
• Biotechnological Exploration: WasteFams facilitates exploration of the biotechnological potential of secondary metabolites within wastewater systems.
• Global Epidemiology Support: The resource supports global wastewater surveillance initiatives for public health and environmental insights.

Methodology:

Protein families were derived from predicted proteins across integrated metagenomic, metatranscriptomic, and reference genome datasets, filtered for non-redundancy and minimum membership thresholds, and annotated with AlphaFold3 structural predictions, taxonomic classifications, biome-specific metadata, and deep genomic context analysis.