BacSPaD: A Robust Bacterial Strains’ Pathogenicity Resource Based on Integrated and Curated Genomic Metadata

Author:

Ribeiro Sara12ORCID,Chaumet Guillaume1,Alves Karine1ORCID,Nourikyan Julien1ORCID,Shi Lei3ORCID,Lavergne Jean-Pierre2,Mijakovic Ivan34ORCID,de Bernard Simon1ORCID,Buffat Laurent1

Affiliation:

1. AltraBio SAS, 69007 Lyon, France

2. Bases Moléculaires et Structurales des Systèmes Infectieux, IBCP, Université Lyon 1, CNRS, UMR 5086, 69007 Lyon, France

3. Division of Systems and Synthetic Biology, Department of Life Sciences, Chalmers University of Technology, 412 96 Göteborg, Sweden

4. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark

Abstract

The vast array of omics data in microbiology presents significant opportunities for studying bacterial pathogenesis and creating computational tools for predicting pathogenic potential. However, the field lacks a comprehensive, curated resource that catalogs bacterial strains and their ability to cause human infections. Current methods for identifying pathogenicity determinants often introduce biases and miss critical aspects of bacterial pathogenesis. In response to this gap, we introduce BacSPaD (Bacterial Strains’ Pathogenicity Database), a thoroughly curated database focusing on pathogenicity annotations for a wide range of high-quality, complete bacterial genomes. Our rule-based annotation workflow combines metadata from trusted sources with automated keyword matching, extensive manual curation, and detailed literature review. Our analysis classified 5502 genomes as pathogenic to humans (HP) and 490 as non-pathogenic to humans (NHP), encompassing 532 species, 193 genera, and 96 families. Statistical analysis demonstrated a significant but moderate correlation between virulence factors and HP classification, highlighting the complexity of bacterial pathogenicity and the need for ongoing research. This resource is poised to enhance our understanding of bacterial pathogenicity mechanisms and aid in the development of predictive models. To improve accessibility and provide key visualization statistics, we developed a user-friendly web interface.

Funder

European Union’s Horizon 2020 research and innovation programme

Publisher

MDPI AG

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