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No full textThis article was featured in the Editor’s Choice section and is considered the best research article by the editorial team in this published issue.RNA-binding proteins (RBPs) are essential for post-transcriptional gene regulation in eukaryotes, including splicing control, mRNA transport and decay. Thus, accurate identification of RBPs is important to understand gene expression and regulation of cell state. In order to detect RBPs, a number of computational models have been developed. These methods made use of datasets from several eukaryotic species, specifically from mice and humans. Although some models have been tested on Arabidopsis, these techniques fall short of correctly identifying RBPs for other plant species. Therefore, the development of a powerful computational model for identifying plant-specific RBPs is needed. In this study, we presented a novel computational model for locating RBPs in plants. Five deep learning models and ten shallow learning algorithms were utilized for prediction with 20 sequence-derived and 20 evolutionary feature sets. The highest repeated five-fold cross-validation accuracy, 91.24% AU-ROC and 91.91% AU-PRC, was achieved by light gradient boosting machine. While evaluated using an independent dataset, the developed approach achieved 94.00% AU-ROC and 94.50% AU-PRC. The proposed model achieved significantly higher accuracy for predicting plant-specific RBPs as compared to the currently available state-of-art RBP prediction models. Despite the fact that certain models have already been trained and assessed on the model organism Arabidopsis, this is the first comprehensive computer model for the discovery of plant-specific RBPs. The web server RBPLight was also developed, which is publicly accessible at https://iasri-sg.icar.gov.in/rbplight/, for the convenience of researchers to identify RBPs in plants.Not Availabl
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No full textNot AvailableIn the last two decades six oilseeds crops viz., castor, linseed, Niger, safflower, sesame and sunflower (IIOR mandate crops) on an average contributed only 15 per cent area and rest of the oilseeds viz., groundnut, soybean and rapeseed and Mustard together contributed 85 per cent area of total oilseeds. However, on an average for the last two decades the contribution of these six crops towards exports is 19 per cent and per cent in terms of export quantity and 36 per cent in terms exports value. In the last QE 2021-22 these six crops contributed 30 per cent and 44 per cent respectively. Thus, despite their relatively little contribution to area and output as compared to other oilseeds, IIOR mandate crops have strong export potential. It also underlines the necessity of stronger marketing chain development for these six oilseeds, as well as the need for area expansion, in order to reinforce and grow their contribution to exports.Not Availabl
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No full textChapter 9 of Training manual “Advances in seafood processing and waste utilization ”Not AvailableNot Availabl
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No full textChapter 5 of Training manual “In-plant training under student ready program”Not AvailableNot Availabl
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No full textChapter 19 of Training manual “In-plant training under student ready program”Not AvailableNot Availabl
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No full textNot AvailableIndia is one of the secondary centers of origin for both wild and domesticated bananas, with the north-eastern region holding the greatest repository. Therefore, the present study used three different molecular marker systems viz., inter simple sequence repeats (ISSR), inter-retrotransposon amplified polymorphism (IRAP), and start codon targeted (SCoT) polymorphism to analyze the genetic diversity among 17 cultivated varieties of the north-eastern region belonging to different genomic groups. The percent polymorphism was found to be 91.79, 86.78, and 82.35 in ISSR, IRAP, and SCoT markers respectively. ISSR had the highest values for all the marker parameters. However, IRAP outperformed SCoT and ISSRs by recording the highest values for effective number of alleles (Ne), Shannon index (I), and Nei’s (1973) gene diversity (H). The dendrogram obtained using ISSR, SCoT, and combined data had two major clusters and the clustering pattern was almost similar, but it differed slightly in IRAP markers. To learn more about the population structure and allelic diversity, model-based structural analysis was carried out in addition to phylogenetic analysis and principal component analysis (PCA). The structure analysis identified three subpopulations in ISSR, four in IRAP, and five in SCoT and combined marker data. The Q-value indicates that almost all the subpopulations are composed of varieties with and without admixture, thereby suggesting that more alleles are being shared among the varieties used in this study.Not Availabl
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No full textChapter 42 of Training manual “In-plant training under student ready program”Not AvailableNot Availabl
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No full textChapter 17 of Training manual “Technological Interventions in Processing, Value addition and Packaging of Aquatic Resources”Not AvailableNot Availabl