Browsing by Author "Anil Paul"

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    LTTRPred: A tool for prediction of LysR-type transcriptional regulator of pyoluteorin pathway in plant growth promoting Pseudomonas spp
    (2014-12) Anil Paul; Hemalatha, N.; Rajesh, M.K.
    Plant growth promoting Pseudomonas spp. produce an antifungal compound called pyoluteorin (Plt) that suppress diseases caused by phytopathogenic fungi. The pathway specific regulator PltR, a typical LysR-type transcriptional regulator (LTTR), is responsible for the transcriptional activation of the Plt biosynthetic operon. The LTTR family represents one of the largest classes of bacterial transcriptional regulatory proteins. A large number of LTTRs possess function as global transcriptional activators or repressors of unlinked genes or operons involved in metabolism, quinoline signal, virulence etc. The proposed method, LTTRPred, is an useful tool developed for identifying and predicting the LTTR, which is responsible for the activation of Plt transcription regulators, from whole genomes of various Pseudomonas spp. LTTRPred was developed using support vector machine (SVM) and Waikato Environment for Knowledge Analysis (WEKA) based on the composition of amino acid and amino acid pairs. Modules in SVM were developed using traditional amino acid, dipeptide (n+1) and hybrid amino acid composition modules and an overall accuracy of 100, 100 and 98 per cent respectively, was achieved. Modules in WEKA were also developed using the same modules and an overall accuracy of 100 per cent achieved for all. The performance of the tool was tested using various datasets of LTTR genes from different Pseudomonas spp. The best performing SVM and WEKA modules from the present investigation was implemented as a dynamic web server ‘LTTRPred’, which is freely available and can be accessed online (http://210.212.229.56/lttrpred/). This tool can be used for the functional annotation of the Pseudomonas spp. possessing LTTR ge
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    LTTRPred: A tool for prediction of transcriptional regulator of pyoluteorin pathway in Pseudomonas species using SVM-based approach
    (2012-11) Anil Paul; Rajesh, M.K.; Hemalatha, N.; Jamshinath, T.P.; Murali Gopal; George V. Thomas
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    PhzPred – A Tool for Prediction of Phenazine Synthesizing Genes in Plant Growth Promoting Pseudomonas spp
    (2014-10) Shilpa, S.; Anil Paul; Naganeeswaran, S.; Hemalatha, N.; Rajesh, M.K.
    Phenazines are natural products produced by the bacterial strain of Pseudomonas spp. which possess anti-microbial activities and include more than 50 pigmented heterocyclic nitrogen containing secondary metabolites. Seven core phenazine biosynthetic genes have been identified in nearly all identified bacterial strains that produce phenazine compounds. In this study, a model has been developed to predict the phenazine biosynthetic genes from a set of protein sequences usingmachine learning algorithms from whole genomes of Pseudomonas spp. Initially, protein sequences from the Pseudomonas spp. were retrieved from public databases and used to train the WEKA models. To train the different classifiers in WEKA, three amino acid compositions were used: monomer amino acids, dipeptide amino acids, and a hybridmethod. The trained models were then used for the prediction of phenazine synthesizing gene in anuser submitted sequence. The best WEKA modules were selected based on the performance of different classifiers in training and testing. The performances of the classifier’s were then evaluated based on 10-fold cross validation and independent data set validation techniques. In the proposed methodology, better performance was observed for the hybrid feature extraction method. The development of a genome wide prediction tool for phenazinesynthesizing genes will substantially have an impact on bacterial genome annotation and devising crop protection strategies using plant growth promoting rhizobacteria.
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    Updated Version of PHYTODB, the First Data Warehousing and Mining Web Server for Phytoplasma Research
    (2011) Manimekalai, R.; Anoop Raj, P.; Roshna, O.M.; Anil Paul; George V. Thomas
    PHYTODB contains a repository of phytoplasma genes and proteins. It provides a unified gateway to store, search, retrieve, update information about phytoplasma and computational resources for the analysis of nucleotide and aminoacid sequence data of phytoplasma. Server facilitates to differentiate and classify new phytoplasma for taxonomic purposes. PHYTODB database was updated by dividing the whole resources into two domains: DataBanks and Tools. DataBanks serve as the storage device of all information. Functional characterization of genes and protein are done. Updated Groupidentifier tool by rearrangement of RFLP classification scheme of phytoplasma and possibilities 6 new groups based on the new tool. PhytoDB can be obtained through http://220.227.88.253/phytodb/ .