10
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Identification of Differentially Expressed Drought-Responsive Genes in Guar [ Cyamopsis tetragonoloba (L.) Taub]

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Drought remains one of the most serious environmental stresses because of the continuous reduction in soil moisture, which requires the improvement of crops with features such as drought tolerance. Guar [ Cyamopsis tetragonoloba (L.) Taub], a forage and industrial crop, is a nonthirsty plant. However, the information on the transcriptome changes that occur under drought stress in guar is very limited; therefore, a gene expression analysis is necessary in this context. Here, we studied the differentially expressed genes (DEGs) in response to drought stress and their metabolic pathways. RNA-Seq via an expectation-maximization algorithm was used to estimate gene abundance. Subsequently, an Empirical Analysis of Digital Gene Expression Data in the R Bioconductor package was used to identify DEGs. Blast2GO, InterProScan, and the Kyoto Encyclopedia of Genes and Genomes were used to explore functional annotation, protein analysis, enzymes, and metabolic pathways. Transcription factors were identified using the PlantTFDB database. Our study identified 499 upregulated and 191 downregulated genes in response to drought stress. Of those, 32 upregulated and six downregulated genes were deemed as novel genes exclusive to guar. An aggregate of 137 protein families, 306 domains, 12 repeats, and two sites were upregulated. The proton-dependent oligopeptide transporter family and transferase, aquaporin transporter, calcium/calmodulin-dependent/calcium-dependent protein kinase, aspartic peptidase A1 family, UDP-glucuronosyl/UDP-glucosyltransferase, and major intrinsic protein were the most upregulated protein families. The upregulated unigenes were associated with 88 enzymes and 77 KEGG pathways. Finally, the MYB-related, MYB, and ERF transcription factor families were upregulated. These data may be useful for understanding the plant molecular response to drought stress.

          Related collections

          Most cited references103

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            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
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

              Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
                Bookmark

                Author and article information

                Contributors
                Journal
                Int J Genomics
                Int J Genomics
                IJG
                International Journal of Genomics
                Hindawi
                2314-436X
                2314-4378
                2020
                3 December 2020
                : 2020
                : 4147615
                Affiliations
                Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
                Author notes

                Academic Editor: Atsushi Kurabayashi

                Author information
                https://orcid.org/0000-0003-4618-8347
                https://orcid.org/0000-0001-5262-5799
                https://orcid.org/0000-0001-6218-9572
                https://orcid.org/0000-0003-1582-0266
                https://orcid.org/0000-0002-0284-2100
                https://orcid.org/0000-0001-7634-2400
                https://orcid.org/0000-0003-3142-7501
                https://orcid.org/0000-0002-7362-2578
                Article
                10.1155/2020/4147615
                7732403
                3ce4b7b3-90c1-4de5-b519-9b8e844976f9
                Copyright © 2020 Aref Alshameri et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 25 July 2020
                : 20 November 2020
                Categories
                Research Article

                Comments

                Comment on this article