摘要：Researchers studying cystic fbrosis (CF) pathogens have produced numerous RNA-seq datasets which are available in the gene expression omnibus (GEO) . Although these studies are publicly available, substantial computational expertise and manual efort are required to compare similar studies, visualize gene expression patterns within studies, and use published data to generate new experimental hypotheses . Furthermore, it is difcult to flter available studies by domain-relevant attributes such as strain, treatment, or media, or for a researcher to assess how a specifc gene responds to various experimental conditions across studies . To reduce these barriers to data re-analysis, we have developed an R Shiny application called CF-Seq, which works with a compendium of 128 studies and 1,322 individual samples from 13 clinically relevant CF pathogens . The application allows users to flter studies by experimental factors and to view complex diferential gene expression analyses at the click of a button . Here we present a series of use cases that demonstrate the application is a useful and efcient tool for new hypothesis generation .
