Longitudinal Metagenomic Profiles of Diabetic Foot Ulcers During Infection Onset

Michael Radzieta's dataset contains shotgun metagenomic sequencing results from a longitudinal study of 6 patients with diabetes-related foot ulcers who developed clinical infections. The data profiles taxonomic shifts in the wound microbiome during the transition from non-infected to infected states. It was last updated on 2026-06-02 and is shared under a CC-BY-4.0 license.

Use Cases

• Modeling microbial community shifts based on longitudinal sampling described in the study.
• Identifying pathogen and anaerobe abundance patterns associated with clinical infection onset.
• Training classifiers to predict infection state from microbiome composition data.
• Analyzing strain-level dynamics of pathogens like Staphylococcus aureus and Pseudomonas aeruginosa.

Strengths

• Longitudinal design tracking within-patient microbial dynamics over time.
• Shotgun metagenomic sequencing provides taxonomic and potentially functional profiling.
• Focus on a clinically significant cohort of 6 patients with diabetes-related foot infections.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is small in size (67.6 KB), indicating limited scope.

Provenance

Source

Michael Radzieta via figshare.

Collection Method

Shotgun metagenomic sequencing of wound samples from patients, followed by taxonomic profiling, metagenome assembly, binning, and strain-level analysis.

Freshness

Last updated 2026-06-02 04:21:13; freshness should be verified.