Comparative Transcriptomics and Co-Expression Database for Bacterial Pathogens

J Mol Biol. 2022 Jun 15;434(11):167380. doi: 10.1016/j.jmb.2021.167380. Epub 2021 Nov 25.


While bacteria can be beneficial to our health, their deadly pathogenic potential has been an ever-present concern exacerbated by the emergence of drug-resistant strains. As such, there is a pressing urgency for an enhanced understanding of their gene function and regulation, which could mediate the development of novel antimicrobials. Transcriptomic analyses have been established as insightful and indispensable to the functional characterization of genes and identification of new biological pathways, but in the context of bacterial studies, they remain limited to species-specific datasets. To address this, we integrated the genomic and transcriptomic data of the 17 most notorious and researched bacterial pathogens, creating, an interactive database that can identify, visualize, and compare gene expression profiles, coexpression networks, functionally enriched clusters, and gene families across species. Through illustrating antibiotic resistance mechanisms in P. aeruginosa, we demonstrate that could potentially aid in discovering multi-faceted antibiotic targets and, overall, facilitate future bacterial research. AVAILABILITY: The database and coexpression networks are freely available from Sample annotations can be found in the supplemental data.

Keywords: co-expression; expression; function; networks; pathogens.

MeSH terms

  • Anti-Bacterial Agents / pharmacology
  • Bacteria* / drug effects
  • Bacteria* / genetics
  • Databases, Genetic*
  • Drug Resistance, Bacterial* / genetics
  • Gene Expression Profiling*
  • Internet Use*
  • Pseudomonas aeruginosa / drug effects
  • Pseudomonas aeruginosa / genetics
  • Transcriptome / genetics


  • Anti-Bacterial Agents