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

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.

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Publication Details

Journal
Pathogens
Published
2024-08-09
DOI
https://doi.org/10.3390/pathogens13080672
Citations
1
Primary Topic
Genomics and Phylogenetic Studies
Type
article
Field-Weighted Citation Impact
0.23
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article

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

S. Ribeiro, Julien Nourikyan, Laurent Buffat, Jean‐Pierre Lavergne et al.
1 citations
Pathogens
Genomics and Phylogenetic Studies
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article

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

S. Ribeiro, Julien Nourikyan, Laurent Buffat, Jean‐Pierre Lavergne, Lei Shi, Karine Alves, Ivan Mijakovic̀, Simon de Bernard, Guillaume Chaumet
article en
1 citations

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.

PathogensVol. 13(8)
Université Claude Bernard Lyon 1 (FR), Centre National de la Recherche Scientifique (FR), Novo Nordisk Foundation (DK), Structural and Molecular Basis of Infectious Systems (FR), Chalmers University of Technology (SE), Technical University of Denmark (DK)
Openalex Percentile: Top 43%
Genomics and Phylogenetic Studies
0.23
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