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Not AvailableIdentification of splice sites is important due to their key role in predicting the exon-intron structure of protein coding genes. Though several approaches have been developed for the prediction of splice sites, further improvement in the prediction accuracy will help predict gene structure more accurately. This paper presents a computational approach for prediction of donor splice sites with higher accuracy. In this approach, true and false splice sites were first encoded into numeric vectors and then used as input in artificial neural network (ANN), support vector machine (SVM) and random forest (RF) for prediction. ANN and SVM were found to perform equally and better than RF, while tested on HS3D and NN269 datasets. Further, the performance of ANN, SVM and RF were analyzed by using an independent test set of 50 genes and found that the prediction accuracy of ANN was higher than that of SVM and RF. All the predictors achieved higher accuracy while compared with the existing methods like NNsplice, MEM, MDD, WMM, MM1, FSPLICE, GeneID and ASSP, using the independent test set. We have also developed an online prediction server (PreDOSS) available at http://cabgrid.res.in:8080/predoss, for prediction of donor splice sites using the proposed approach.Not Availabl

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Not AvailableCoding regions are the fragments of DNA sequence that codes for protein through the process of transcription and translation respectively. On the other hand, the non coding regions do not give rise to any protein. Discrimination of coding regions from the non coding regions is essential for genome annotation. In this study, an attempt has been made to develop a random forest based computational approach for discriminating coding regions (CDS) from non-coding regions (introns). The features based on codon structure and methylation mediated substitutions were used in this approach. The developed approach achieved high classification accuracy, while tested on two agriculturally important species i.e., rice and cattle. The proposed approach is believed to complement the other prediction methods. Based on the proposed approach, an online prediction server ‘DCDNC’ has also been developed for easy prediction by the users. The prediction server is freely available at http://cabgrid.res.in:8080/DCDNC.Not Availabl

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Not AvailableDetection of splice sites plays a key role for predicting the gene structure and thus development of efficient analytical methods for splice site prediction is vital. This paper presents a novel sequence encoding approach based on the adjacent di-nucleotide dependencies in which the donor splice site motifs are encoded into numeric vectors. The encoded vectors are then used as input in Random Forest (RF), Support Vector Machines (SVM) and Artificial Neural Network (ANN), Bagging, Boosting, Logistic regression, kNN and Naïve Bayes classifiers for prediction of donor splice sites.Not Availabl

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Not AvailableDNA barcoding is a molecular diagnostic method that allows automated and accurate identification of species based on a short and standardized fragment of DNA. To this end, an attempt has been made in this study to develop a computational approach for identifying the species by comparing its barcode with the barcode sequence of known species present in the reference library. Each barcode sequence was first mapped onto a numeric feature vector based on k-mer frequencies and then Random forest methodology was employed on the transformed dataset for species identification. The proposed approach outperformed similarity-based, tree-based, diagnostic-based approaches and found comparable with existing supervised learning based approaches in terms of species identification success rate, while compared using real and simulated datasets. Based on the proposed approach, an online web interface SPIDBAR has also been developed and made freely available at http://cabgrid.res.in:8080/spidbar/ for species identification by the taxonomists.Not Availabl

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Not AvailablePrediction of splice sites plays an important role in predicting the gene structure. Rice being one of the major cereal crops, continuous improvement is possible with the prediction of unknown genes associated with complex traits. Machine learning techniques i.e., Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been successfully used for the prediction of splice sites but comparison of their performance has not been made yet to our limited knowledge. Further, Random Forest (RF), another machine learning method, has been successfully used and reported to outperform ANN and SVM in areas other than splice site prediction. In this study we have developed an approach to encode the splice site sequence data of rice into numeric form that are subsequently used as input in ANN, SVM and RF for prediction of donor splice sites. The performances were then evaluated and compared using receiving operating characteristics (ROC) curve and estimate of area under ROC curve (AUC), averaged over 5-fold cross validation. The result reveals that AUC of RF is higher than ANN and SVM which implies that it can be preferred over SVM and ANN in the prediction splice sites.Not Availabl

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Not AvailableOne of the thrust areas of research in plant breeding is to develop crop cultivars with enhanced tolerance to abiotic stresses. Thus, identifying abiotic stress-responsive genes (SRGs) and proteins is important for plant breeding research. However, identifying such genes via established genetic approaches is laborious and resource intensive. Although transcriptome profiling has remained a reliable method of SRG identification, it is species specific. Additionally, identifying multistress responsive genes using gene expression studies is cumbersome. Thus, endorsing the need to develop a computational method for identifying the genes associated with different abiotic stresses. In this work, we aimed to develop a computational model for identifying genes responsive to six abiotic stresses: cold, drought, heat, light, oxidative, and salt. The predictions were performed using support vector machine (SVM), random forest, adaptive boosting (ADB), and extreme gradient boosting (XGB), where the autocross covariance (ACC) and K-mer compositional features were used as input. With ACC, K-mer, and ACC + K-mer compositional features, the overall accuracy of ∼60–77, ∼75–86, and ∼61–78% were respectively obtained using the SVM algorithm with fivefold cross-validation. The SVM also achieved higher accuracy than the other three algorithms. The proposed model was also assessed with an independent dataset and obtained an accuracy consistent with cross-validation. The proposed model is the first of its kind and is expected to serve the requirement of experimental biologists; however, the prediction accuracy was modest. Given its importance for the research community, the online prediction application, ASRpro, is made freely available (https://iasri-sg.icar.gov.in/asrpro/) for predicting abiotic SRGs and proteins.Not Availabl

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Not AvailableAccurate prediction of the gene structure depends upon the accurate prediction of splice sites. The conserved feature in splicing junction has been successfully used for the prediction of eukaryotic splice sites. In eukaryotes, though the di-nucleotide GT is conserved at 5′ splice sites, the pattern surrounding the conserved di-nucleotide varies from species to species. Most of the work related to splice site analysis has been extensively done in Homo sapiens and Arabidopsis thaliana. However, such works are yet to be fully explored in Oryza sativa and other species of grass family. In this study, statistical techniques have been applied to discriminate the real splice sites from pseudo splice sites in rice, maize and barley genomes and based on this a suitable window size is determined for the prediction of donor splice sites. Depending upon the determined window size, appropriate methods for predicting donor splice sites in rice have been considered and compared in terms of prediction accuracy. The results revealed that a window size of 9 base pair (3 bp at the exon end and 6 bp at the intron start including the conserved di-nucleotide GT at the beginning of intron) is an effective window size in all the three species of grass family for the prediction of donor splice sites. Further, the Maximum Entropy Model based method is found as best among the short sequence based prediction methods for donor splice sites with the 9 base pair window size.Not Availabl

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Not AvailableMost of the approaches for splice site prediction are based on machine learning techniques. Though, these approaches provide high prediction accuracy, the window lengths used are longer in size. Hence, these approaches may not be suitable to predict the novel splice variants using the short sequence reads generated from next generation sequencing technologies. Further, machine learning techniques require numerically encoded data and produce different accuracy with different encoding procedures. Therefore, splice site prediction with short sequence motifs and without encoding sequence data became a motivation for the present study.Not Availabl