SalmonAct

Foodborne Salmonella enterica infection remains a major public health threat due to its prevalence in food, ease of transmission, and increasing antibiotic resistance. Consequently, understanding the molecular mechanisms underlying Salmonella pathogenesis is crucial for guiding novel diagnostic, preventative, and therapeutic approaches. High-throughput transcriptomic technologies are now often employed in Salmonella research to quantify how gene expression changes in response to various conditions or mutations. But due to the high dimensionality of this data, resulting from the comparison of the expression of thousands of genes from multiple strains or culture conditions with complex interactions, interpretation remains a demanding task. To address this issue, we present SalmonAct, a comprehensive signed and directed prior knowledge resource for inferring transcription factor activities in Salmonella. This new resource expands the toolkit available for Salmonella functional analysis. Built as an extension of the SalmoNet2 database, SalmonAct can be used to infer the activity of 191 transcription factors in 5991 regulatory interactions based on publicly available interaction and regulatory knockout data. SalmonAct enhances the interpretation of highly-dimensional transcriptomic data by identifying both highly influential and minimally active transcription factors that drive the observed expression state. SalmonAct aids in bridging the gap between model and non-model organisms? functional analysis, and together with the SalmoNet2 resource, can be used for further downstream data analyses.