PathFams: statistical detection of pathogen-associated protein domains
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Updated: Oct 20, 2025
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
Briallen Lobb1, Benjamin Jean-Marie Tremblay1, Gabriel Moreno-Hagelsieb2
1Department of Biology, University of Waterloo, Waterloo, Ontario, Canada.
Background:
A substantial fraction of genes identified within bacterial genomes encode proteins of unknown function. Identifying which of these proteins represent potential virulence factors, and mapping their key virulence determinants, is a challenging but important goal.
Results:
To facilitate virulence factor discovery, we performed a comprehensive analysis of 17,929 protein domain families within the Pfam database, and scored them based on their overrepresentation in pathogenic versus non-pathogenic species, taxonomic distribution, relative abundance in metagenomic datasets, and other factors.
Conclusions:
We identify pathogen-associated domain families, candidate virulence factors in the human gut, and eukaryotic-like mimicry domains with likely roles in virulence. Furthermore, we provide an interactive database called PathFams to allow users to explore pathogen-associated domains as well as identify pathogen-associated domains and domain architectures in user-uploaded sequences of interest. PathFams is freely available at https://pathfams.uwaterloo.ca .
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