Identification of a suitable clustering method and allocation strategy for core set development in salt stress tolerant rice (Oryza sativa) germplasm

Preserving genetic diversity in repository of germplasm is essential for crop breeding programs. However, maintenance and protection of all the germplasms in gene bank is difficult due to its voluminous size. Hence the development of core set with minimum number of germplasm representing maximum genetic diversity of the population has become an alternative. From the available clustering methods and allocation strategies, identifying a suitable combination is essential for the development of species-specific core set. In the present study, data on 219 salt stress tolerant rice (Oryza sativa L.) germplasm accessions with 14 phenotypic traits and 2915 genome wide Single Nucleotide Polymorphisms (SNPs) is considered to identify a suitable combination of clustering method and allocation strategy for core set development. Eight different combinations consisting of two clustering methods, viz. Ward’s and UPGMA along with four different allocation strategies, viz. L, D, LD and NY allocation with three level of sampling intensities (20%, 25% and 30%) have been tried. Based on the study carried out during 2013-14 at Indian Agricultural Statistics Research Institute, New Delhi, it is concluded that the Ward’s clustering method with NY allocation, irrespective of sampling intensity, is suitable for developing core set with maximum diversity

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Not AvailableIdentification of splice sites is an important aspect with regard to the prediction of gene structure. In most of the existing splice site prediction studies, machine learning algorithms coupled with sequence-derived features have been successfully employed for splice site recognition. However, the splice site identification by incorporating the secondary structure information is lacking, particularly in plant species. Thus, we made an attempt in this study to evaluate the performance of structural features on the splice site prediction accuracy in Arabidopsis thaliana. Prediction accuracies were evaluated with the sequence-derived features alone as well as by incorporating the structural features into the sequence-derived features, where support vector machine (SVM) was employed as prediction algorithm. Both short (40 base pairs) and long (105 base pairs) sequence datasets were considered for evaluation. After incorporating the secondary structure features, improvements in accuracies were observed only for the longer sequence dataset and the improvement was found to be higher with the sequence-derived features that accounted nucleotide dependencies. On the other hand, either a little or no improvement in accuracies was found for the short sequence dataset. The performance of SVM was further compared with that of LogitBoost, Random Forest (RF), AdaBoost and XGBoost machine learning methods. The prediction accuracies of SVM, AdaBoost and XGBoost were observed to be at par and higher than that of RF and LogitBoost algorithms. While prediction was performed by taking all the sequence-derived features along with the structural features, a little improvement in accuracies was found as compared to the combination of individual sequence-based features and structural features. To the best of our knowledge, this is the first attempt concerning the computational prediction of splice sites using machine learning methods by incorporating the secondary structure information into the sequence-derived features. All the source codes are available at https://github.com/meher861982/SSFeature.Not Availabl

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Not AvailableWe evaluated the performances of three BLUP and five Bayesian methods for genomic prediction by using nine actual and 54 simulated datasets. The genomic prediction accuracy was measured using Pearson’s correlation coefficient between the genomic estimated breeding value (GEBV) and the observed phenotypic data using a fivefold cross-validation approach with 100 replications. The Bayesian alphabets performed better for the traits governed by a few genes/QTLs with relatively larger effects. On the contrary, the BLUP alphabets (GBLUP and CBLUP) exhibited higher genomic prediction accuracy for the traits controlled by several small-effect QTLs. Additionally, Bayesian methods performed better for the highly heritable traits and, for other traits, performed at par with the BLUP methods. Further, genomic BLUP (GBLUP) was identified as the least biased method for the GEBV estimation. Among the …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 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 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 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