Dataset Information

A parallel genetic algorithm for single class pattern classification and its application for gene expression profiling in Streptomyces coelicolor.

ABSTRACT:

Background

Identification of coordinately regulated genes according to the level of their expression during the time course of a process allows for discovering functional relationships among genes involved in the process.

Results

We present a single class classification method for the identification of genes of similar function from a gene expression time series. It is based on a parallel genetic algorithm which is a supervised computer learning method exploiting prior knowledge of gene function to identify unknown genes of similar function from expression data. The algorithm was tested with a set of randomly generated patterns; the results were compared with seven other classification algorithms including support vector machines. The algorithm avoids several problems associated with unsupervised clustering methods, and it shows better performance then the other algorithms. The algorithm was applied to the identification of secondary metabolite gene clusters of the antibiotic-producing eubacterium Streptomyces coelicolor. The algorithm also identified pathways associated with transport of the secondary metabolites out of the cell. We used the method for the prediction of the functional role of particular ORFs based on the expression data.

Conclusion

Through analysis of a time series of gene expression, the algorithm identifies pathways which are directly or indirectly associated with genes of interest, and which are active during the time course of the experiment.

SUBMITTER: To CC

PROVIDER: S-EPMC1804277 | biostudies-literature | 2007 Feb

REPOSITORIES: biostudies-literature

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Publications

A parallel genetic algorithm for single class pattern classification and its application for gene expression profiling in Streptomyces coelicolor.

To Cuong C CC Vohradsky Jiri J

BMC genomics 20070213

<h4>Background</h4>Identification of coordinately regulated genes according to the level of their expression during the time course of a process allows for discovering functional relationships among genes involved in the process.<h4>Results</h4>We present a single class classification method for the identification of genes of similar function from a gene expression time series. It is based on a parallel genetic algorithm which is a supervised computer learning method exploiting prior knowledge o ...[more]

PMID: 17298664

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Project description:Nucleoid-associated proteins (NAPs) are small, highly abundant transcriptional regulators with low sequence specificity which are involved in multiple DNA-related processes including gene expression, DNA protection, recombination/repair and nucleoid structuring. Through these functions they are able to regulate important phenotypic properties including virulence, secondary metabolism and stress resistance. However the set of NAPs known within the Actinobacteria is small and incomplete. The missing proteins are likely to be key regulators of virulence in pathogens such as Mycobacterium tuberculosis and also of development and secondary metabolism in industrially-important species such as Streptomyces. Here, we use label-free LC-MS/MS to systematically search for novel NAPs in isolated nucleoids of the model actinomycete Streptomyces coelicolor. Based on the criteria of high abundance (emPAI score) and predicted DNA-binding ability (DNAbinder score) we identified a set of 24 proteins with a high predicted likelihood of being NAPs. The approach was deemed successful as the set included known major NAPs HupA, HupS, sIHF and Lsr2 as well as the global transcriptional regulators BldD and CRP and the pleiotropic response regulator AfsQ1. It also included a number of proteins whose functions are not yet known from recognisable classes of transcription factor (SCO2140, SCO4493, SCO1839, SCO1210, SCO5405, SCO4229, SCO3198) or from uncharacterised protein families (SCO5783, SCO5592, SCO3793, SCO6482) which comprise a valuable set of candidates for further study.In this paper we establish a robust protocol for preparing S. coelicolor nucleoids for mass spectrometric analysis and develop a workflow for identifying novel nucleoid-associated proteins (NAPs) by combining LC-MS/MS with a bioinformatical analysis. The nucleoid-associated proteins of many species are known to be key regulators of virulence, stress tolerance and global patterns of gene expression. Identifying the "missing" nucleoid proteins of S. coelicolor is likely to have important implications for manipulating the production of secondary metabolites such as antibiotics. Candidate NAPs were identified. Several of these are highly conserved in clinically important species such as Mycobacterium and in many commercially important species such as Salinispora and Micromonospora which represent a vital source of novel drugs such as antibiotics, antifungals and anticancer agents.

Dataset Information

A parallel genetic algorithm for single class pattern classification and its application for gene expression profiling in Streptomyces coelicolor.

Background

Results

Conclusion

Publications

A parallel genetic algorithm for single class pattern classification and its application for gene expression profiling in Streptomyces coelicolor.

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