GENIA: Synthetic Microbial Community Data for Multipollutant Bioremediation

A dataset describing 45 bacterial strains isolated from a xenobiotic-enriched cotton detritusphere and a resulting nine-member synthetic community designed for pollutant degradation. The data includes genomic profiles and functional annotations used to predict community assemblies via a machine learning framework called GENIA. The dataset was created by Esaú De la Vega-Camarillo and last updated on 2026-04-27.

Use Cases

• Training graph neural network models for microbial community prediction based on genomic profile encodings.
• Analyzing metabolic pathway complementarity for synthetic community design based on functional annotation data.
• Benchmarking pollutant degradation kinetics (lignin, atrazine, PFOS) for engineered microbial consortia.
• Studying community stability and hub strain dynamics using 16S rRNA metabarcoding data mentioned in the description.

Strengths

• Includes whole-genome sequencing and functional annotation for 45 prioritized bacterial strains.
• Documents a nine-member synthetic community that demonstrated high degradation rates: lignin (91.6%), atrazine (91.4%), and PFOS (93.1%).
• Framework validation includes soil microcosm results showing >70% degradation at 3 weeks.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small (38.9 KB), indicating limited scope.

Provenance

Source

figshare

Collection Method

Strains were isolated using a microfluidic high-throughput cultivation platform from a xenobiotic-enriched cotton detritusphere and screened for pollutant-specific growth.

Freshness

Last updated 2026-04-27 04:47:32; freshness should be verified.