scholarly journals Differential Expression Analysis of a Subset of Drought-Responsive GmNAC Genes in Two Soybean Cultivars Differing in Drought Tolerance

2013 ◽  
Vol 14 (12) ◽  
pp. 23828-23841 ◽  
Author(s):  
Nguyen Thao ◽  
Nguyen Thu ◽  
Xuan Hoang ◽  
Chien Ha ◽  
Lam-Son Tran
Keyword(s):  
Drought Tolerance ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Soybean Cultivars

scholarly journals Transcriptome analysis of drought-tolerant sorghum genotype SC56 in response to water stress reveals an oxidative stress defense strategy

2019 ◽  
Author(s):  
Farida Olden ◽  
Arthur G. Hunt ◽  
Randy Dinkins
Keyword(s):  
Oxidative Stress ◽  
Water Stress ◽  
Drought Tolerance ◽  
Stress Tolerance ◽  
Differential Expression ◽  
Expression Analysis ◽  
Transcriptome Analysis ◽  
Differential Expression Analysis ◽  
Drought Tolerant ◽  
Drought Conditions

Abstract Background Drought tolerance is a crucial trait for crops to curtail the yield loss inflicted by water stress to crops, yet genetic improvement efforts are challenged by the complexity of this character. The adaptation of sorghum to abiotic stress, its genotypic variability, and relatively small genome make this species well-suited to dissect the molecular basis of drought tolerance. One efficient approach to this question is the use of differential transcriptome analysis, which provides a snapshot of the processes underlying drought response as well as genes that might be determinants of the drought tolerance trait. Results RNA sequencing was used to compare the transcriptome profiles of two sorghum lines, the drought-tolerant SC56 and the drought-sensitive Tx7000. The differential expression analysis revealed unambiguous genotypic disparities, including a massive increase of upregulated transcripts in SC56. Concomitantly, gene ontology enrichment showed that SC56 biologically outperformed Tx7000 in wet conditions, since it upregulated processes driving growth and guaranteeing homeostasis. The drought tolerance of SC56 seems to be an intrinsic trait that occurs through the overexpression of stress tolerance genes in wet conditions, notably those acting in defense against oxidative stress (SOD1, SOD2, VTC1, MDAR1, MSRB2, and ABC1K1). Under drought conditions, SC56 enhanced its transmembrane transport and maintained growth-promoting mechanisms similar to those implemented under wet conditions. SC56 also appears to preserve its biological function, in a limiting environment, by relying on reported validated stress tolerance genes that heighten the antioxidant capacity (SOD1, RCI3, VTE1, UCP1, FD1, and FD2), regulatory factors (CIPK1 and CRK7), and repressors of premature senescence (SAUL1). Of the stress tolerance genes overexpressed under both wet and drought conditions, DHAR2 might be a key determinant of drought tolerance since its role in recycling ascorbic acid was described to be directly linked to protection against reactive oxygen species-mediated damage, positive effects on photosynthetic activity, higher rate of plant growth, and delayed leaf aging. Conclusion The differential expression analysis uncovered biological processes which upregulation enables SC56 to be a better accumulator of biomass and connects the drought tolerance trait to key stress tolerance genes, making this genotype a judicious choice for isolation of tolerance genes.

scholarly journals Best practices on the differential expression analysis of multi-species RNA-seq

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Matthew Chung ◽  
Vincent M. Bruno ◽  
David A. Rasko ◽  
Christina A. Cuomo ◽  
José F. Muñoz ◽  
...  
Keyword(s):  
Best Practices ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Single Species ◽  
Rna Seq ◽  
Species Analysis ◽  
Differential Gene ◽  
Multiple Species ◽  
Downstream Analysis

AbstractAdvances in transcriptome sequencing allow for simultaneous interrogation of differentially expressed genes from multiple species originating from a single RNA sample, termed dual or multi-species transcriptomics. Compared to single-species differential expression analysis, the design of multi-species differential expression experiments must account for the relative abundances of each organism of interest within the sample, often requiring enrichment methods and yielding differences in total read counts across samples. The analysis of multi-species transcriptomics datasets requires modifications to the alignment, quantification, and downstream analysis steps compared to the single-species analysis pipelines. We describe best practices for multi-species transcriptomics and differential gene expression.

scholarly journals The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Xueyi Dong ◽  
Luyi Tian ◽  
Quentin Gouil ◽  
Hasaru Kariyawasam ◽  
Shian Su ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Transcriptomic Analysis ◽  
Statistical Testing ◽  
Rna Seq ◽  
Sequencing Data ◽  
Short Read ◽  
Sequencing Platform ◽  
Long Read

Abstract Application of Oxford Nanopore Technologies’ long-read sequencing platform to transcriptomic analysis is increasing in popularity. However, such analysis can be challenging due to the high sequence error and small library sizes, which decreases quantification accuracy and reduces power for statistical testing. Here, we report the analysis of two nanopore RNA-seq datasets with the goal of obtaining gene- and isoform-level differential expression information. A dataset of synthetic, spliced, spike-in RNAs (‘sequins’) as well as a mouse neural stem cell dataset from samples with a null mutation of the epigenetic regulator Smchd1 was analysed using a mix of long-read specific tools for preprocessing together with established short-read RNA-seq methods for downstream analysis. We used limma-voom to perform differential gene expression analysis, and the novel FLAMES pipeline to perform isoform identification and quantification, followed by DRIMSeq and limma-diffSplice (with stageR) to perform differential transcript usage analysis. We compared results from the sequins dataset to the ground truth, and results of the mouse dataset to a previous short-read study on equivalent samples. Overall, our work shows that transcriptomic analysis of long-read nanopore data using long-read specific preprocessing methods together with short-read differential expression methods and software that are already in wide use can yield meaningful results.

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