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

Abstract

An 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 the Mycobacterium tuberculosis genome 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 in M. tuberculosis using 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.