An operon is a set of neighboring genes in a genome that is transcribed as a 
single polycistronic message. Genes that are part of the same operon often have 
related functional roles or participate in the same metabolic pathways. The 
majority of all bacterial genes are co-transcribed with one or more other genes 
as part of a multi-gene operon. Thus, accurate identification of operons is 
important in understanding co-regulation of genes and their functional 
relationships. Here, we present a computational system that uses RNA-seq data to 
determine operons throughout a genome. The system takes the name of a genome and 
one or more files of RNA-seq data as input. Our method combines primary genomic 
sequence information with expression data from the RNA-seq files in a unified 
probabilistic model in order to identify operons. We assess our method's ability 
to accurately identify operons in a range of species through comparison to 
external databases of operons, both experimentally confirmed and computationally 
predicted, and through focused experiments that confirm new operons identified 
by our method. Our system is freely available at 
https://cs.wellesley.edu/~btjaden/Rockhopper/.