Development of a tool for computational prediction of σ70 promoters in Pseudomonas spp using SVM and HMM approaches

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dc.contributor.authorMerin K. Eldo
dc.contributor.authorRajesh, M.K.
dc.contributor.authorJamshinath, T.P.
dc.contributor.authorHemalatha, N.
dc.contributor.authorMurali Gopal
dc.contributor.authorGeorge V. Thomas
dc.date.accessioned2014-06-07T10:27:57Z
dc.date.available2014-06-07T10:27:57Z
dc.date.issued2014
dc.description.abstractPromoters are regions in DNA that play important role in the regulation of gene expression. The ability to locate promoters within a section of DNA is known to be a very difficult and important task in DNA analysis. Since experimental techniques to identify promoters are costly and time consuming, in silico methods offer an alternative. In this study, we have developed a tool for identification of σ70 promoters in the –10 and –35 regions of sequences from Pseudomonas spp. Promoters were predicted using both Support Vector Machine (SVM) and Hidden Markov Model (HMM) based approaches. SVM performed better when trained using RBF kernel with a cross-validation of 5 and a value of 0.03 for the gamma parameter. The module developed using SVM showed a sensitivity of 78% and a specificity of 80%. The programmes required to process the user input were written using Perl and HTML codes were used to create a user interface. The user interface accepts a query sequence and the processed result will be displayed in a new window. The tool named PROMIT (PROMoter Identification Tool), was developed in the Windows platform, has a user friendly interface and works well for sequences from Pseudomonas spp.en_US
dc.identifier.citationIndian Journal of Agricultural Sciences 84 (1): 119–23, January 2014en_US
dc.identifier.urihttp://hdl.handle.net/123456789/2556
dc.language.isoenen_US
dc.subjectHMMen_US
dc.subjectPromoteren_US
dc.subjectPseudomonasen_US
dc.subjectSVMen_US
dc.subjectσ70en_US
dc.titleDevelopment of a tool for computational prediction of σ70 promoters in Pseudomonas spp using SVM and HMM approachesen_US
dc.typeArticleen_US

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