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Predicted transcription factor binding sites as predictors of operons in Escherichia coli and Streptomyces coelicolor

Laing, E, Sidhu, K and Hubbard, SJ (2008) Predicted transcription factor binding sites as predictors of operons in Escherichia coli and Streptomyces coelicolor BMC GENOMICS, 9, 79.

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Background: As a polycistronic transcriptional unit of one or more adjacent genes, operons play a key role in regulation and function in prokaryotic biology, and a better understanding of how they are constituted and controlled is needed. Recent efforts have attempted to predict operonic status in sequenced genomes using a variety of techniques and data sources. To date, non-homology based operon prediction strategies have mainly used predicted promoters and terminators present at the extremities of transcriptional unit as predictors, with reasonable success. However, transcription factor binding sites (TFBSs), typically found upstream of the first gene in an operon, have not yet been evaluated. Results: Here we apply a method originally developed for the prediction of TFBSs in Escherichia coli that minimises the need for prior knowledge and tests its ability to predict operons in E. coli and the 'more complex', pharmaceutically important, Streptomyces coelicolor. We demonstrate that through building genome specific TFBS position-specific-weight-matrices (PSWMs) it is possible to predict operons in E. coli and S. coelicolor with 83% and 93% accuracy respectively, using only TFBS as delimiters of operons. Additionally, the 'palindromicity' of TFBS footprint data of E. coli is characterised. Conclusion: TFBS are proposed as novel independent features for use in prokaryotic operon prediction (whether alone or as part of a set of features) given their efficacy as operon predictors in E. coli and S. coelicolor. We also show that TFBS footprint data in E. coli generally contains inverted repeats with significantly (p < 0.05) greater palindromicity than random sequences. Consequently, the palindromicity of putative TFBSs predicted can also enhance operon predictions.

Item Type: Article
Divisions : Faculty of Health and Medical Sciences > School of Biosciences and Medicine > Department of Microbial and Cellular Sciences
Authors : Laing, E, Sidhu, K and Hubbard, SJ
Date : 12 February 2008
DOI : 10.1186/1471-2164-9-79
Depositing User : Melanie Hughes
Date Deposited : 03 Nov 2010 10:16
Last Modified : 31 Oct 2017 14:05

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