Journal article
The cross-species prediction of bacterial promoters using a support vector machine
Computational Biology and Chemistry, Vol.32(5), pp.359-366
2008
Abstract
Due to degeneracy of the observed binding sites, the in silico prediction of bacterial σ70-like promoters remains a challenging problem. A large number of σ70-like promoters has been biologically identified in only two species, Escherichia coli and Bacillus subtilis. In this paper we investigate the issues that arise when searching for promoters in other species using an ensemble of SVM classifiers trained on E. coli promoters. DNA sequences are represented using a tagged mismatch string kernel. The major benefit of our approach is that it does not require a prior definition of the typical -35 and -10 hexamers. This gives the SVM classifiers the freedom to discover other features relevant to the prediction of promoters. We use our approach to predict σA promoters in B. subtilis and σ66 promoters in Chlamydia trachomatis. We extended the analysis to identify specific regulatory features of gene sets in C. trachomatis having different expression profiles. We found a strong -35 hexamer and TGN/-10 associated with a set of early expressed genes. Our analysis highlights the advantage of using TSS-PREDICT as a starting point for predicting promoters in species where few are known.
Details
- Title
- The cross-species prediction of bacterial promoters using a support vector machine
- Authors
- M Towsey (Author) - University of QueenslandPeter Timms (Author) - University of QueenslandJ Hogan (Author) - Queensland University of TechnologyS A Mathews (Author) - University of Queensland
- Publication details
- Computational Biology and Chemistry, Vol.32(5), pp.359-366
- Publisher
- Elsevier Ltd.
- Date published
- 2008
- DOI
- 10.1016/j.compbiolchem.2008.07.009
- ISSN
- 1476-9271
- Organisation Unit
- University of the Sunshine Coast, Queensland; Centre for Bioinnovation
- Language
- English
- Record Identifier
- 99449048102621
- Output Type
- Journal article
Metrics
2 File views/ downloads
484 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- Web Of Science research areas
- Biology
- Computer Science, Interdisciplinary Applications
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites