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      Transcriptome profiling reveals the genes and pathways involved in thermo-tolerance in wheat (Triticum aestivum L.) genotype Raj 3765

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          Abstract

          Wheat, one of the most widely consumed staple food crops globally, is relatively vulnerable to high temperature-induced heat stress. It is therefore essential to gain more insight into the comprehensive mechanism of thermotolerance of wheat in order to safeguard its production. In view of this, we analysed heat stress responsive transcriptome data of wheat to determine its gene expression level under heat stress. A total of 7990 DEGs, including 4483 up-regulated and 3507 down regulated genes were identified. Gene Ontology (GO) analysis categorized 3910 DEGs into different ontology families. 146 pathways involving 814 DEGs were enriched during KEGG analysis. Metabolic pathways and biosynthesis of secondary metabolites were the major pathways enriched. MYB (myeloblastosis) transcription factors (TFs) and many other TFs as bHLH, WRKY, NAC, ERF, were determined to be quite abundant in the DEGs. Since various reports indicate that these TFs play important role in plants abiotic stress, it is an indication that our DEGs are functional in heat stress tolerance. Verification of few selected DEGs using RT-qPCR produced expression levels similar to the transcriptome data. This indicates that the transcriptome data is reliable. These results could be helpful in enhancing our understanding of the mechanism underlying thermotolerance in wheat.

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              CD-HIT: accelerated for clustering the next-generation sequencing data

              Summary: CD-HIT is a widely used program for clustering biological sequences to reduce sequence redundancy and improve the performance of other sequence analyses. In response to the rapid increase in the amount of sequencing data produced by the next-generation sequencing technologies, we have developed a new CD-HIT program accelerated with a novel parallelization strategy and some other techniques to allow efficient clustering of such datasets. Our tests demonstrated very good speedup derived from the parallelization for up to ∼24 cores and a quasi-linear speedup for up to ∼8 cores. The enhanced CD-HIT is capable of handling very large datasets in much shorter time than previous versions. Availability: http://cd-hit.org. Contact: liwz@sdsc.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                jasdeep_kaur64@yahoo.co.in
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                1 September 2022
                1 September 2022
                2022
                : 12
                : 14831
                Affiliations
                [1 ]GRID grid.418196.3, ISNI 0000 0001 2172 0814, PG School, , ICAR-Indian Agricultural Research Institute, ; New Delhi, 110012 India
                [2 ]GRID grid.418105.9, ISNI 0000 0001 0643 7375, ICAR-National Institute for Plant Biotechnology, ; New Delhi, 110012 India
                [3 ]GRID grid.418196.3, ISNI 0000 0001 2172 0814, Division of Genetics, , Indian Agricultural Research Institute, ; Pusa, New Delhi, 110012 India
                [4 ]GRID grid.418196.3, ISNI 0000 0001 2172 0814, Division of Plant Physiology, , Indian Agricultural Research Institute, ; Pusa, New Delhi, 110012 India
                [5 ]GRID grid.423756.1, ISNI 0000 0004 1764 1672, Present Address: CSIR-Food Research Institute, ; Accra, Ghana
                Article
                18625
                10.1038/s41598-022-18625-7
                9437100
                36050336
                b893717c-d795-4730-936b-8b6a0202c4a6
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 December 2021
                : 16 August 2022
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                © The Author(s) 2022

                Uncategorized
                biotechnology,molecular biology,plant sciences
                Uncategorized
                biotechnology, molecular biology, plant sciences

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