In the framework of the PEST-BIN project, we are thrilled to announce that Sara Ribeiro‘s work on the generation of BacSPaD has been published in the journal Pathogens. 🧬
This achievement highlights Sara’s exceptional effort in combining metadata from trusted sources with automated keyword matching, extensive manual curation, and detailed literature reviews. The result is an invaluable database that includes both pathogenic and non-pathogenic strains. This resource advances our understanding of bacterial pathogenicity mechanisms and aids in the development of predictive models.
This breakthrough has the potential to significantly impact public health by contributing to efforts to mitigate the challenges posed by infectious diseases.
Congratulations, Sara, on this remarkable publication! 🎉
#PESTBIN #MSCA #ITN #BacterialPathogenicity #Genomics #Research #Pathogens #InfectiousDiseases
Article Summary:
The vast array of omics data in microbiology presents significant opportunities for studying bacterial pathogenesis. 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). This thoroughly curated database focuses 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 5,502 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. This highlights 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.