CF-Seq, An Accessible Web Application for Rapid Re-Analysis of Cystic Fibrosis Pathogen RNA Sequencing Studies

Abstract

Researchers studying cystic fibrosis (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 effort are required to compare similar studies, visualize gene expression patterns within studies, and use published data to generate new experimental hypotheses. Furthermore, it is difficult to filter available studies by domain-relevant attributes such as strain, treatment, or media, or for a researcher to assess how a specific 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 147 studies and 1,446 individual samples from 13 clinically relevant CF pathogens. The application allows users to filter studies by experimental factors and to view complex differential 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 efficient tool for new hypothesis generation. (CFSeq: http://scangeo.dartmouth.edu/CFSeq/)

ESKAPE Act Plus: Pathway Activation Analysis for Bacterial Pathogens

“…Downloadable output is comprised of box plots for all significant KEGG pathways and GO terms, tables with statistical results for all KEGG pathways and GO terms that were tested, and optional KEGG Mapper ( 14 , 15 ) generated images for significantly activated or repressed KEGG pathways, if applicable. ESKAPE Act PLUS also seamlessly interfaces with output from CF-Seq ( 16 ), a web application that facilitates re-analysis of publicly available RNA-seq data relevant to cystic fibrosis research, including transcriptomic data from bacteria that are common causes of lung infection.…”