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  • 1
    Online Resource
    Online Resource
    Regulatory Networks of lncRNAs, miRNAs, and mRNAs in Response to Heat Stress in Wheat (Triticum Aestivum L.): An Integrated Analysis
    Wiley ; 2023
    In:  International Journal of Genomics Vol. 2023 ( 2023-3-30), p. 1-17
    Details
    In: International Journal of Genomics, Wiley, Vol. 2023 ( 2023-3-30), p. 1-17
    Abstract: Climate change has become a major source of concern, particularly in agriculture, because it has a significant impact on the production of economically important crops such as wheat, rice, and maize. In the present study, an attempt has been made to identify differentially expressed heat stress-responsive long non-coding RNAs (lncRNAs) in the wheat genome using publicly available wheat transcriptome data (24 SRAs) representing two conditions, namely, control and heat-stressed. A total of 10,965 lncRNAs have been identified and, among them, 153, 143, and 211 differentially expressed transcripts have been found under 0 DAT, 1 DAT, and 4 DAT heat-stress conditions, respectively. Target prediction analysis revealed that 4098 lncRNAs were targeted by 119 different miRNA responses to a plethora of environmental stresses, including heat stress. A total of 171 hub genes had 204 SSRs (simple sequence repeats), and a set of target sequences had SNP potential as well. Furthermore, gene ontology analysis revealed that the majority of the discovered lncRNAs are engaged in a variety of cellular and biological processes related to heat stress responses. Furthermore, the modeled three-dimensional (3D) structures of hub genes encoding proteins, which had an appropriate range of similarity with solved structures, provided information on their structural roles. The current study reveals many elements of gene expression regulation in wheat under heat stress, paving the way for the development of improved climate-resilient wheat cultivars.
    Type of Medium: Online Resource
    ISSN: 2314-4378 , 2314-436X
    URL: Article
    DOI: 10.1155/2023/1774764
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2770599-7
    detail.hit.zdb_id: 2711883-6
    SSG: 12
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  • 2
    Online Resource
    Online Resource
    Identification of genetic markers for increasing agricultural productivity: An empirical study
    Indian Council of Agricultural Research, Directorate of Knowledge Management in Agriculture ; 2019
    In:  The Indian Journal of Agricultural Sciences Vol. 89, No. 10 ( 2019-10-22)
    Details
    In: The Indian Journal of Agricultural Sciences, Indian Council of Agricultural Research, Directorate of Knowledge Management in Agriculture, Vol. 89, No. 10 ( 2019-10-22)
    Abstract: Genomic selection (GS) has been used globally for increasing agricultural production and productivity. It has been used for complex quantitative traits by selecting breeding material after predicting Genomic Estimated Breeding Values (GEBVs) of target species. The accuracy of GS for estimation of GEBVs depends on various factors including sampling population, genetic architecture of target species, statistical models, etc. The feature (marker) selection is one of the important steps in development of GS models. There are large numbers of models proposed in the literature for GS. However, applicability of these models is based on many factors including extent of additive and epistatic effects of breeding population. Therefore, there is strong need to evaluate the performance of these models and techniques of feature selection under different situations. In this study, performance of linear/additive effect models, viz. linear least squared regression, BLUP, LASSO, ridge regression, SpAM as well as non-linear/epistatic effect models, viz. mRMR, HSIC LASSO have been evaluated through a simulation study in R platform. In general, performance of SpAM was found to be superior for GS than all other models considered in this study in case of presence of additive effect and absence of epistatic effect. However, in case of low heritability and high epistatic effect the HSIC LASSO outperformed all models. This study will assist researcher in selection of appropriate feature selection technique for a given situation.
    Type of Medium: Online Resource
    ISSN: 2394-3319 , 0019-5022
    URL: Article
    DOI: 10.56093/ijas.v89i10.94633
    Language: Unknown
    Publisher: Indian Council of Agricultural Research, Directorate of Knowledge Management in Agriculture
    Publication Date: 2019
    detail.hit.zdb_id: 413627-5
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  • 3
    Online Resource
    Online Resource
    Integrated model for genomic prediction under additive and non-additive genetic architecture
    Frontiers Media SA ; 2022
    In:  Frontiers in Plant Science Vol. 13 ( 2022-11-30)
    Details
    In: Frontiers in Plant Science, Frontiers Media SA, Vol. 13 ( 2022-11-30)
    Abstract: Using data from genome-wide molecular markers, genomic selection procedures have proved useful for estimating breeding values and phenotypic prediction. The link between an individual genotype and phenotype has been modelled using a number of parametric methods to estimate individual breeding value. It has been observed that parametric methods perform satisfactorily only when the system under study has additive genetic architecture. To capture non-additive (dominance and epistasis) effects, nonparametric approaches have also been developed; however, they typically fall short of capturing additive effects. The idea behind this study is to select the most appropriate model from each parametric and nonparametric category and build an integrated model that can incorporate the best features of both models. It was observed from the results of the current study that GBLUP performed admirably under additive architecture, while SVM’s performance in non-additive architecture was found to be encouraging. A robust model for genomic prediction has been developed in light of these findings, which can handle both additive and epistatic effects simultaneously by minimizing their error variance. The developed integrated model has been assessed using standard evaluation measures like predictive ability and error variance.
    Type of Medium: Online Resource
    ISSN: 1664-462X
    URL: Article
    DOI: 10.3389/fpls.2022.1027558
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2613694-6
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  • 4
    Online Resource
    Online Resource
    Integrated Framework for Selection of Additive and Nonadditive Genetic Markers for Genomic Selection
    Mary Ann Liebert Inc ; 2020
    In:  Journal of Computational Biology Vol. 27, No. 6 ( 2020-06-01), p. 845-855
    Details
    In: Journal of Computational Biology, Mary Ann Liebert Inc, Vol. 27, No. 6 ( 2020-06-01), p. 845-855
    Type of Medium: Online Resource
    ISSN: 1557-8666
    URL: Article
    DOI: 10.1089/cmb.2019.0223
    Language: English
    Publisher: Mary Ann Liebert Inc
    Publication Date: 2020
    detail.hit.zdb_id: 2030900-4
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  • 5
    Online Resource
    Online Resource
    Table_14.csv
    Frontiers Media SA ; 2023
    In:  Frontiers in Plant Science Vol. 14 ( 2023-8-15)
    Details
    In: Frontiers in Plant Science, Frontiers Media SA, Vol. 14 ( 2023-8-15)
    Abstract: Wheat stripe rust (yellow rust) caused by Puccinia striiformis f. sp. tritici ( Pst ) is a serious biotic stress factor limiting wheat production worldwide. Emerging evidence demonstrates that long non-coding RNAs (lncRNAs) participate in various developmental processes in plants via post-transcription regulation. In this study, RNA sequencing (RNA-seq) was performed on a pair of near-isogenic lines—rust resistance line FLW29 and rust susceptible line PBW343—which differed only in the rust susceptibility trait. A total of 6,807 lncRNA transcripts were identified using bioinformatics analyses, among which 10 lncRNAs were found to be differentially expressed between resistance and susceptible lines. In order to find the target genes of the identified lncRNAs, their interactions with wheat microRNA (miRNAs) were predicted. A total of 199 lncRNAs showed interactions with 65 miRNAs, which further target 757 distinct mRNA transcripts. Moreover, detailed functional annotations of the target genes were used to identify the candidate genes, pathways, domains, families, and transcription factors that may be related to stripe rust resistance response in wheat plants. The NAC domain protein, disease resistance proteins RPP13 and RPM1, At1g58400, monodehydroascorbate reductase, NBS-LRR-like protein, rust resistance kinase Lr10-like, LRR receptor, serine/threonine-protein kinase, and cysteine proteinase are among the identified targets that are crucial for wheat stripe rust resistance. Semiquantitative PCR analysis of some of the differentially expressed lncRNAs revealed variations in expression profiles of two lncRNAs between the Pst -resistant and Pst -susceptible genotypes at least under one condition. Additionally, simple sequence repeats (SSRs) were also identified from wheat lncRNA sequences, which may be very useful for conducting targeted gene mapping studies of stripe rust resistance in wheat. These findings improved our understanding of the molecular mechanism responsible for the stripe rust disease that can be further utilized to develop wheat varieties with durable resistance to this disease.
    Type of Medium: Online Resource
    ISSN: 1664-462X
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fpls.2023.1120898
    DOI: 10.3389/fpls.2023.1120898.s001
    DOI: 10.3389/fpls.2023.1120898.s002
    DOI: 10.3389/fpls.2023.1120898.s003
    DOI: 10.3389/fpls.2023.1120898.s004
    DOI: 10.3389/fpls.2023.1120898.s005
    DOI: 10.3389/fpls.2023.1120898.s006
    DOI: 10.3389/fpls.2023.1120898.s007
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2613694-6
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  • 6
    Online Resource
    Online Resource
    Estimation of Error Variance in Genomic Selection for Ultrahigh Dimensional Data
    MDPI AG ; 2023
    In:  Agriculture Vol. 13, No. 4 ( 2023-04-04), p. 826-
    Details
    In: Agriculture, MDPI AG, Vol. 13, No. 4 ( 2023-04-04), p. 826-
    Abstract: Estimation of error variance in the case of genomic selection is a necessary step to measure the accuracy of the genomic selection model. For genomic selection, whole-genome high-density marker data is used where the number of markers is always larger than the sample size. This makes it difficult to estimate the error variance because the ordinary least square estimation technique cannot be used in the case of datasets where the number of parameters is greater than the number of individuals (i.e., p 〉 n). In this article, two existing methods, viz. Refitted Cross Validation (RCV) and kfold-RCV, were suggested for such cases. Moreover, by considering the limitations of the above methods, two new methods, viz. Bootstrap-RCV and Ensemble method, have been proposed. Furthermore, an R package “varEst” has been developed, which contains four different functions to implement these error variance estimation methods in the case of Least Absolute Shrinkage and Selection Operator (LASSO), Least Squares Regression (LSR) and Sparse Additive Models (SpAM). The performances of the algorithms have been evaluated using simulated and real datasets.
    Type of Medium: Online Resource
    ISSN: 2077-0472
    URL: Article
    DOI: 10.3390/agriculture13040826
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2651678-0
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  • 7
    Online Resource
    Online Resource
    Prediction of protein–protein interactions between anti-CRISPR and CRISPR-Cas using machine learning technique
    Springer Science and Business Media LLC ; 2023
    In:  Journal of Plant Biochemistry and Biotechnology Vol. 32, No. 4 ( 2023-12), p. 818-830
    Details
    In: Journal of Plant Biochemistry and Biotechnology, Springer Science and Business Media LLC, Vol. 32, No. 4 ( 2023-12), p. 818-830
    Type of Medium: Online Resource
    ISSN: 0971-7811 , 0974-1275
    URL: Article
    DOI: 10.1007/s13562-022-00813-1
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 1178808-2
    detail.hit.zdb_id: 2206337-7
    SSG: 12
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