Condition-Specific Mapping of Operons (COSMO) using dynamic and static genome data

Abstract

AbstractAn operon is a set of adjacent genes which are transcribed into a single messenger RNA. Operons allow prokaryotes to efficiently circumvent environmental stresses. It is estimated that about 60% of theMycobacterium tuberculosisgenome is arranged into operons, which makes them interesting drug targets in the face of emerging drug resistance. We therefore developed COSMO - a tool for operon prediction inM. tuberculosisusing RNA-seq data. We analyzed four algorithmic parameters and benchmarked COSMO against two top performing operon predictors. COSMO outperformed both predictors in its accuracy and in its ability to distinguish operons activated under distinct conditions.Author SummaryOperons may be important drug targets for the development of effective anti-microbials to combat the emerging, global drug resistance challenge. However, there is a shortage of knownMycobacterium tuberculosis (Mtb)operons. This is exacerbated by the fact that current operon predictors are not optimized for the unique genome of Mtb. COSMO removes the limitations imposed by using the constraints of a specific organism’s genome and exploits RNA-seq data instead. This allows COSMO to more accurately predict full-length operons in Mtb, and it also avails COSMO to other microorganisms for the same purpose.