Extracting pertinent biological information from the large and complex datasets that are generated by high-throughput genetic approaches remains a challenge. We performed a negative selection screen using Tn-seq to dissect out genes required by a strain of ExPEC (Extraintestinal Pathogenic E. coli) in vivo. To zero in on genes that act specifically to enhance fitness under pathogenic conditions, we developed a novel algorithm referred to as ‘TEA’ (Trait Enrichment Analysis). TEA allowed us to identify several previously uncharacterized genes that are more often confined to the genomes of pathogens—suggesting that they may have evolved to specifically promote pathogenic behaviors. The images above depict the overflow of candidate genes that can result from high-throughput experimental procedures (text, background) and the penultimate goal of assembling this information into a working, functional model (bacterium with inner workings shown, foreground).
our findings are reported in the open access journal PLOS Genetics