Transcriptome dynamics-based operon prediction and verification in Streptomyces coelicolor.
Streptomyces spp. produce a variety of valuable secondary metabolites, which are regulated in a spatio-temporal manner by a complex network of inter-connected gene products. Using a compilation of genome-scale temporal transcriptome data for the model organism, Streptomyces coelicolor, under different environmental and genetic perturbations, we have developed a supervised machine-learning method for operon prediction in this microorganism. We demonstrate that, using features dependent on transcriptome dynamics and genome sequence, a support vector machines (SVM)-based classification algorithm can accurately classify >90% of gene pairs in a set of known operons. Based on model predictions for the entire genome, we verified the co-transcription of more than 250 gene pairs by RT-PCR. These results vastly increase the database of known operons in S. coelicolor and provide valuable information for exploring gene function and regulation to harness the potential of this differentiating microorganism for synthesis of natural products.
Charaniya
Salim
S
Department of Chemical Engineering and Materials Science, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN 55455-0132, USA.
Mehra
Sarika
S
Lian
Wei
W
Jayapal
Karthik P
KP
Karypis
George
G
Hu
Wei-Shou
WS
D001185:Artificial Intelligence; D021901:DNA, Intergenic / Q000032:analysis; D020869:Gene Expression Profiling; D015964:Gene Expression Regulation, Bacterial; D016680:Genome, Bacterial; D009876:Operon; D015203:Reproducibility of Results; D020133:Reverse Transcriptase Polymerase Chain Reaction; D048372:Streptomyces coelicolor / Q000235:genetics*; D013728:Terminator Regions, Genetic; D014158:Transcription, Genetic
D021901:DNA, Intergenic