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Nucleic Acids Research Advance Access published online on October 24, 2007
Nucleic Acids Research, doi:10.1093/nar/gkm501
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Genomics

Transcriptome dynamics-based operon prediction and verification in Streptomyces coelicolor

Salim Charaniya1, Sarika Mehra3, Wei Lian1, Karthik P. Jayapal1, George Karypis2 and Wei-Shou Hu1,*

1Department of Chemical Engineering and Materials Science and 2Department of Computer Science and Engineering, University of Minnesota, 421 Washington Avenue SE, Minneapolis, MN 55455-0132, USA and 3Department of Chemical Engineering, Indian Institute of Technology, Bombay, Powai, Mumbai 400 076, India

*To whom correspondence should be addressed. Tel: +1 612 625 0546; Fax: +1 612 626 7246; Email: acre{at}cems.umn.edu

Received May 18, 2007. Accepted June 7, 2007.

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.

Present Address: Wei Lian, Abbott Bioresearch Center, 100 Research Drive, Worcester, MA 01605 USA


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