Browsing by Author "Narayanan, N.K."

Now showing 1 - 8 of 8
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    Computational approach for the prediction of ERF and DREB proteins in indica rice using support vector machine
    (2012) Hemalatha, N.; Rajesh, M.K.; Narayanan, N.K.
    Drought and salt stress are considered to be major impediments in rice production systems. To understand the genetics of tolerance to these abiotic stresses and develop drought/salt tolerant cultivars, genomic regions influencing yield and its response to water deficit have to be identified. A method for predicting two drought tolerant proteins viz. dehydration-responsive element binding proteins (DREB) and ethylene responsive factor (ERF) in the genome of indica rice has been described. The proposed method, ERFDREBSVMPRED, was developed using support vector machine and a prediction accuracy of 89% for DREB and 81% for ERF was achieved. The developed tool could predict DREB protein with 100% specificity at a 71% sensitivity rate and ERF protein with 100% specificity at a 60% sensitivity rate.
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    Genome-Wide Analysis of Putative ERF and DREB GENE Families in Indica Rice (O. sativa L. subsp. Indica)
    (2012-10) Hemalatha, N.; Rajesh, M.K.; Narayanan, N.K.
    Drought is a major constraint to rice production and its stability in rain-fed and poorly irrigated environments. Identifying genomic regions influencing the yield and its response to water deficits will aid in our understanding of the genetics of drought tolerance and development of more drought tolerant cultivars. Besides drought, the other major impediment to increased crop production is salt stress. In this context, identification of drought and salt-responsive genes assumes significance. In this paper we carried out genome-wide analyses to explore putative genes encoding ethylene responsive factor (ERF) and dehydration-responsive element binding proteins (DREB) in the genome of indica rice. Reference nucleotides of well established molecular function, representing each of the protein families investigated, were chosen as query sequences for searches in the indica rice genome database. Clones having genomic sequences similar to the related genes were taken and converted to amino acid sequences. Putative sequences were subjected to PROSITE and Pfam databases and 31 signature sequences related to ERF family and 30 sequences related to DREB were obtained. Proteins showing more than 30% identity were taken and phylogenetic trees were generated for each family. The results of this sudy provide basic genomic information about new ERF and DREB gene families in indica rice.
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    Genome-wide Analysis of Putative Erfand Dreb Gene Families in Indica Rice (o. Sativa l. Subsp. Indica)
    (2011) Hemalatha, N.; Rajesh, M.K.; Narayanan, N.K.
    Drought is a major constraint to rice production and its stability in rain-fed and poorly irrigated environments. Identifying genomic regions influencing the yield and its response to water deficits will aid in our understanding of the genetics of drought tolerance and development of more drought tolerant cultivars. Besides drought, the other major impediment to increased crop production is salt stress. In this context, identification of drought and salt-responsive genes assumes significance. In this paper we carried out genome-wide analyses to explore putative genes encoding ethylene responsive factor (ERF) and dehydration-responsive element binding proteins (DREB) in the genome of indica rice. Reference nucleotides of well established molecular function, representing each of the protein families investigated, were chosen as query sequences for searches in the indica rice genome database. Clones having genomic sequences similar to the related genes were taken and converted to amino acid sequences. Putative sequences were subjected to PROSITE and Pfam databases and 31 signature sequences related to ERF family and 30 sequences related to DREB were obtained. Proteins showing more than 30% identity were taken and phylogenetic trees were generated for each family. The results of this sudy provide basic genomic information about new ERF and DREB gene families in indica rice.
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    An Integrative system for prediction of NAC proteins in rice using different feature extraction methods
    (2013-02) Hemalatha, N.; Rajesh, M.K.; Narayanan, N.K.
    The NAC gene family encodes a large family of plant-specific transcription factors with diverse roles in various developmental processes and stress responses in plants. Creation of genome wide prediction tools for NAC proteins will have a significant impact on gene annotation in rice. In the present study, NACSVM,a tool for computational genome-scale prediction of NAC proteins in rice was developed integrating compositional and evolutionary information of NAC proteins. Initially, support vector machine (SVM)- based modules were developed using combinatorial presence of diverse protein features such as traditional amino acid, dipeptide (i+1), tripeptide (i+2), four-parts composition and PSSM and an overall accuracy of 79%, 93%, 93%, 79% and 100% respectively was achieved. Later, two hybrid modules were developed based on amino acid, dipeptide and tripeptide composition, through which an overall accuracy of 83% and 79% was achieved. NACSVM was also evaluated using position-specific iterated – basic local alignment search tool which resulted in a lower accuracy of 50%. In order to enchmark NACSVM , the tool was evaluated using independent data test and cross validation methods. The different statistical analyses carried out revealed that the proposed algorithm is an useful tool for annotating NAC proteins in genome of rice.
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    A machine learning approach for detecting MAP kinase in the genome of Oryza sativa L. ssp. indica
    (2014) Hemalatha, N.; Rajesh, M.K.; Narayanan, N.K.
    Plant development and crop yield are highly influenced by temperature. High temperature negatively affects different stages of plant development in rice, mainly booting and flowering. Identifying candidate genes associated with high temperature stress response may provide knowledge for the improvement of heat tolerance in rice. As the rice genome sequencing has already been undertaken, a major work challenge is annotating proteins and decoding their functionalities. MAP kinase (MAPK) proteins are involved in signaling various abiotic and biotic stresses, like temperature stress or drought, wounding and pathogen infection. Moreover, MAPKs have also been implicated in cell cycle and developmental processes. In this study, an attempt has been made in developing a MAP kinase prediction tool for rice, MapPred. The computational approach has been developed using Sequential Minimum Optimization (SMO) algorithm in Weka workbench, and a sensitivity of 100% was obtained using dipeptide method. MapPred was also tested with three plants, namely Arabidopsis, maize and tomato to prove that developed tool has higher accuracy with rice than other plants which further proves the higher prediction accuracy of species-specific tools. Prediction performance of MapPred was evaluated using cross validation, independent data test and leave one out validation. Our experimental results demonstrated that proposed algorithm based on dipeptide method could be very effective in the computational approach for predicting MAPK proteins in Oryza sativasubsp.indica.
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    Machine Learning Approaches for Prediction of Expansin Gene Family in Indica Rice
    (2013-12) Hemalatha, N.; Rajesh, M.K.; Narayanan, N.K.
    Expansin refers to a family of proteins present in the plant cell wall which has important roles in plant cell growth, emergence of root hairs, meristem function and other developmental processes. A major constraint to rice production is submergence of rice by flash flooding. In our earlier study, we had identified 21 novel sequences related to expansin gene families in the genome of indica rice using genome-wide analysis. Development of a tool for the prediction of these expansin genes using computational approaches might significantly enhance rice gene annotation. ExpansinPred, a novel computational method based on radial basis function (RBF) and support vector machines (SVMs) for prediction of aexpansins (EXPA) and b-expansins (EXPB), is presented in this work. Two large families of expansin genes have been discovered in plants, namely EXPA and EXPB. The experimental data are curated from NCBI and include 24 EXPA and 20 EXPB, of indica rice, after redundancy elimination. The proper window length for a potential expansin was optimized as 4 for EXPA and EXPB with prediction accuracies 100 % each for both classifiers for RBF classifier. For SVM, the window length was optimized as 3 for EXPA and 4 for EXPB with prediction accuracies 90 and 100 %, respectively. To evaluate the prediction performance of ExpansinPred, cross-validation, independent dataset validation and jackknife validation were carried out. ExpansinPred was also compared with four more algorithms namely Naive Bayes, sequential minimal optimization, J48 and random forest. To further prove that species-specific predictor is much better than general tool, ExpansinPred was compared with an All-plant tool and also with plants other than rice as test set. The different statistical analyses carried out demonstrated that the proposed algorithm is a useful computational tool for rice genome annotation, specifically for predicting expansin gene family, and can benefit rice research community.
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    Nacpred: Computational Prediction of Nac Proteins in Rice Implemented Using Smo Algorithm
    (2013) Hemalatha, N.; Rajesh, M.K.; Narayanan, N.K.
    The impact of abiotic stresses, such as drought, on plant growth and development severely hampers crop production worldwide. The development of stress-tolerant crops will greatly benefit agricultural systems in areas prone to abiotic stresses. Recent advances in molecular and genomic technologies have resulted in a greater understanding of the mechanisms underlying the genetic control of the abiotic stress response in plants. NAC (NAM, ATAF1/2 and CUC2) domain proteins are plant-specific transcriptional factors which has diversified roles in various plant developmental processes and stress responses. More than 100 NAC genes have been identified in rice. In the proposed method, NACPred, an attempt has been made in the direction of computational prediction of NAC proteins. The well-known sequential minimum optimization (SMO) algorithm, which is most commonly used algorithm for numerical solutions of the support vector learning problems, has been used for the development of various modules in this tool. Modules were first developed using amino acid, traditional dipeptide (i+1), tripeptide (i+2) and an overall accuracy of 76%, 90%, and 97% respectively was achieved. To gain further insight, a hybrid module (hybrid1 and hybrid2) was also developed based on amino acid composition and dipeptide composition, which achieved an overall accuracy of 90% and 97%. To evaluate the prediction performance of NACPred, cross validation, leave one out validation and independent data test validation were carried out. It was also compared with algorithms namely RBF and Random Forest. The different statistical analyses worked out revealed that the proposed algorithm is useful for rice genome annotation, specifically predicting NAC proteins.
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    NACSVMPred: A Machine Learning Approach for Prediction of NAC Proteins in Rice Using Support Vector Machines
    (2012) Hemalatha, N.; Rajesh, M.K.; Narayanan, N.K.
    NAC proteins are plant-specific transcriptional factors with diversified roles in various developmental processes and stress responses. Development of genome wide prediction tools for NAC proteins will substantially have an impact on rice gene annotation. NACSVMPred is an effort in this direction for computational genome-scale prediction of NAC proteins in rice by integrating compositional and evolutionary information of proteins. Support vector machine (SVM)-based modules were first developed using traditional amino acid, dipeptide (i+1), tripeptide (i+2), four-parts composition and PSSM and an overall accuracy of 79%, 93%, 93%, 79% and 100% respectively was achieved. Further, two hybrid modules were developed based on amino acid, dipeptide and tripeptide composition, which achieved an overall accuracy of 83% and 79%. NACSVMPred was also evaluated with PSI-BLAST, which resulted in a lower accuracy of 50%. The different statistical analyses carried out revealed that the proposed algorithm is useful for rice genome annotation, specifically predicting NAC proteins.