Vibrio Parahaemolyticus Pangenome Data for Pathogenic Potential Prediction

Public genomic data for the foodborne pathogen Vibrio parahaemolyticus supports machine learning models for source differentiation. The dataset underpins models achieving an AUC greater than 0.95 in distinguishing clinical from environmental isolates based on pangenome assemblies. It facilitates the discovery of virulence-associated genes, such as vspR, sctC5, and tdh1, for seafood safety risk assessment.

Use Cases

• Training machine learning classifiers to predict the isolation source (clinical vs. environmental) of Vibrio parahaemolyticus strains.
• Identifying accessory and cloud genes associated with pathogenic potential through feature weight analysis.
• Developing a cost-effective genomic surveillance framework for seafood safety risk assessment.
• Validating deep learning algorithms for pathogenicity prediction using pangenomic signals.

Strengths

• Models built on this data achieved a high predictive performance, with an AUC greater than 0.95.
• Dataset is publicly shared under a permissive CC-BY-4.0 license, facilitating reuse.
• Cross-platform presence on figshare indicates established sharing and potential for community validation.

Limitations

• Column names and a precise row count are not provided, limiting understanding of the data structure.
• The primary data file is a DOCX document ('Data Sheet 1'), which is an atypical and potentially less accessible format for structured genomic data analysis.
• Conflicting 'last updated' timestamps (2026-05-21 and 2026-05-20) create minor uncertainty about versioning.

Provenance

Source

Zhuosheng Liu

Collection Method

Aggregated from public Vibrio parahaemolyticus genomic data and metadata, likely from sequencing databases.

Freshness

Last updated 2026-05-21 12:40:03