scholarly journals Differential Expression Analysis of Phytohormone-Related Genes of Korean Wheat (Triticum aestivum) in Response to Preharvest Sprouting and Abscisic Acid (ABA)

2021 ◽  
Vol 11 (8) ◽  
pp. 3562
Author(s):  
Yong Jin Lee ◽  
Sang Yong Park ◽  
Dae Yeon Kim ◽  
Jae Yoon Kim
Keyword(s):  
Abscisic Acid ◽  
Differential Expression Analysis ◽  
Preharvest Sprouting ◽  
Global Changes ◽  
Rna Seq ◽  
Hormone Metabolism ◽  
Global Issue ◽  
End Use ◽  
Transcriptomic Changes

Preharvest sprouting (PHS) is a key global issue in production and end-use quality of cereals, particularly in regions where the rainfall season overlaps the harvest. To investigate transcriptomic changes in genes affected by PHS-induction and ABA-treatment, RNA-seq analysis was performed in two wheat cultivars that differ in PHS tolerance. A total of 123 unigenes related to hormone metabolism and signaling for abscisic acid (ABA), gibberellic acid (GA), indole-3-acetic acid (IAA), and cytokinin were identified and 1862 of differentially expressed genes were identified and divided into 8 groups by transcriptomic analysis. DEG analysis showed the majority of genes were categorized in sugar related processes, which interact with ABA signaling in PHS tolerant cultivar under PHS-induction. Thus, genes related to ABA are key regulators of dormancy and germination. Our results give insight into global changes in expression of plant hormone related genes in response to PHS.

scholarly journals RNA-seq assembler artifacts can bias expression counts and differential expression analysis - case study on the chickpea transcriptome emphasizes importance of freely accessible data for reproducibility

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2394 ◽  
Author(s):  
Sandeep Chakraborty
Keyword(s):  
Differential Expression ◽  
Sequence Data ◽  
Differential Expression Analysis ◽  
Open Reading Frames ◽  
Ribulose Bisphosphate Carboxylase ◽  
Rna Seq ◽  
Bisphosphate Carboxylase ◽  
Intermediate Data

The unprecedented volume of genomic and transcriptomic data analyzed by software pipelines makes verification of inferences based on such data, albeit theoretically possible, a challenging proposition. The availability of intermediate data can immensely aid re-validation efforts. One such example is the transcriptome, assembled from raw RNA-seq reads, which is frequently used for annotation and quantification of genes transcribed. The quality of the assembled transcripts influences the accuracy of inferences based on them. Here the publicly available transcriptome from Cicer arietinum (ICC4958; Desi chickpea, http://www.nipgr.res.in/ctdb.html)1 was analyzed using YeATS2. This revealed that a majority of the highly expressed transcripts (HET) encoded multiple genes, strongly indicating that the counts may have been biased by the merging of different transcripts. TC00004 is ranked in the top five HET for all five tissues analyzed here, and encodes both a retinoblastoma-binding-like protein (E-value=0) and a senescence-associated protein (E-value= 5e-108). Fragmented transcripts are another source of error. The ribulose bisphosphate carboxylase small chain (RBCSC) protein is split into two transcripts with an overlapping amino acid sequence "ASNGGRVHC", TC13991 and TC23009, with length 201 and 332 nucleotides and expression counts 17.90 and 1403.8, respectively. The huge difference in counts indicates an erroneous normalization algorithm in determining counts. It is well known that RBCSC is highly expressed and expectedly TC23009 ranks fifth among HETs in the shoot. Furthermore, some transcripts are split into open reading frames that map to the same protein, although this should not have any significant bearing on the counts. It is proposed that studies analyzing differential expression based on the transcriptome should consider these artifacts, and providing intermediate assembled transcriptomes should be mandatory, possibly with a link to the raw sequence data (Bioproject).

scholarly journals Model based normalization improves differential expression calling in low-depth RNA-seq

2015 ◽  
Author(s):  
Pavel Zakharov ◽  
Alexey Sergushichev ◽  
Alexander Predeus ◽  
Maxim Artyomov
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
High Throughput ◽  
Differential Expression Analysis ◽  
Sequencing Depth ◽  
Rna Seq ◽  
Optimal Balance ◽  
Gene Count ◽  
Count Distribution

RNA-seq is a powerful tool for gene expression profiling and differential expression analysis. Its power depends on sequencing depth which limits its high-throughput potential, with 10-15 million reads considered as optimal balance between quality of differential expression calling and cost per sample. We observed, however, that some statistical features of the data, e.g. gene count distribution, are preserved well below 10-15M reads, and found that they improve differential expression analysis at low sequencing depths when distribution statistics is estimated by pooling individual samples to a combined higher-depth library. Using a novel gene-by-gene scaling technique, based on the fact that gene counts obey Pareto-like distribution, we re-normalize samples towards bigger sequencing depth and show that this leads to significant improvement in differential expression calling, with only a marginal increase in false positive calls. This makes differential expression calling from 3-4M reads comparable to 10-15M reads, improving high-throughput of RNA-sequencing 3-4 fold.

scholarly journals RNA-seq assembler artifacts can bias expression counts and differential expression analysis - application of YeATS on the chickpea transcriptome

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2394
Author(s):  
Sandeep Chakraborty
Keyword(s):  
Differential Expression ◽  
Sequence Data ◽  
Differential Expression Analysis ◽  
Open Reading Frames ◽  
Ribulose Bisphosphate Carboxylase ◽  
Rna Seq ◽  
Bisphosphate Carboxylase ◽  
Intermediate Data ◽  
Analysis Application

Background: The unprecedented volume of genomic and transcriptomic data analyzed by software pipelines makes verification of inferences based on such data, albeit theoretically possible, a challenging proposition. The availability of intermediate data can immensely aid re-validation efforts. One such example is the transcriptome, assembled from raw RNA-seq reads, which is frequently used for annotation and quantification of genes transcribed. The quality of the assembled transcripts influences the accuracy of inferences based on them. Method: Here the publicly available transcriptome from Cicer arietinum (ICC4958; Desi chickpea, http://www.nipgr.res.in/ctdb.html) was analyzed using YeATS. Results and Conclusion: The analysis revealed that a majority of the highly expressed transcripts (HET) encoded multiple genes, strongly indicating that the counts may have been biased by the merging of different transcripts. TC00004 is ranked in the top five HET for all five tissues analyzed here, and encodes both a retinoblastoma-binding-like protein (E-value=0) and a senescence-associated protein (E-value= 5e-108). Fragmented transcripts are another source of error. The ribulose bisphosphate carboxylase small chain (RBCSC) protein is split into two transcripts with an overlapping amino acid sequence ”ASNGGRVHC”, TC13991 and TC23009, with length 201 and 332 nucleotides and expression counts 17.90 and 1403.8, respectively. The huge difference in counts indicates an erroneous normalization algorithm in determining counts. It is well known that RBCSC is highly expressed and expectedly TC23009 ranks fifth among HETs in the shoot. Furthermore, some transcripts are split into open reading frames that map to the same protein, although this should not have any significant bearing on the counts. It is proposed that studies analyzing differential expression based on the transcriptome should consider these artifacts, and providing intermediate assembled transcriptomes should be mandatory, possibly with a link to the raw sequence data (Bioproject).

scholarly journals Fast, scalable and accurate differential expression analysis for single cells

2016 ◽  
Author(s):  
Debarka Sengupta ◽  
Nirmala Arul Rayan ◽  
Michelle Lim ◽  
Bing Lim ◽  
Shyam Prabhakar
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Differential Expression Analysis ◽  
Nonparametric Method ◽  
Rna Seq ◽  
Noise Levels ◽  
High Noise ◽  
Technical Variability ◽  
Massive Sample

ABSTRACTAnalysis of single-cell RNA-seq data is challenging due to technical variability, high noise levels and massive sample sizes. Here, we describe a normalization technique that substantially reduces technical variability and improves the quality of downstream analyses. We also introduce a nonparametric method for detecting differentially expressed genes that scales to > 1,000 cells and is both more accurate and ~10 times faster than existing parametric approaches.

scholarly journals Transcriptome Profile Alterations with Carbon Nanotubes, Quantum Dots, and Silver Nanoparticles: A Review

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 794
Author(s):  
Cullen Horstmann ◽  
Victoria Davenport ◽  
Min Zhang ◽  
Alyse Peters ◽  
Kyoungtae Kim
Keyword(s):  
Carbon Nanotubes ◽  
Quantum Dots ◽  
High Throughput Sequencing ◽  
The Body ◽  
Rna Seq ◽  
Mechanisms Of Toxicity ◽  
Promising Tool ◽  
Engineered Nanomaterial ◽  
Next Generation Sequencing Ngs ◽  
Transcriptomic Changes

Next-generation sequencing (NGS) technology has revolutionized sequence-based research. In recent years, high-throughput sequencing has become the method of choice in studying the toxicity of chemical agents through observing and measuring changes in transcript levels. Engineered nanomaterial (ENM)-toxicity has become a major field of research and has adopted microarray and newer RNA-Seq methods. Recently, nanotechnology has become a promising tool in the diagnosis and treatment of several diseases in humans. However, due to their high stability, they are likely capable of remaining in the body and environment for long periods of time. Their mechanisms of toxicity and long-lasting effects on our health is still poorly understood. This review explores the effects of three ENMs including carbon nanotubes (CNTs), quantum dots (QDs), and Ag nanoparticles (AgNPs) by cross examining publications on transcriptomic changes induced by these nanomaterials.

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 Gene Expression Profile and Co-Expression Network of Pearl Gentian Grouper under Cold Stress by Integrating Illumina and PacBio Sequences

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1745
Author(s):  
Ben-Ben Miao ◽  
Su-Fang Niu ◽  
Ren-Xie Wu ◽  
Zhen-Bang Liang ◽  
Bao-Gui Tang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cold Stress ◽  
Differential Expression Analysis ◽  
Endocrine System ◽  
Control Group ◽  
Regulation Mechanism ◽  
Rna Seq ◽  
Cold Tolerant ◽  
Stress Signals ◽  
Membrane Changes

Pearl gentian grouper (Epinephelus fuscoguttatus ♀ × Epinephelus lanceolatus ♂) is a fish of high commercial value in the aquaculture industry in Asia. However, this hybrid fish is not cold-tolerant, and its molecular regulation mechanism underlying cold stress remains largely elusive. This study thus investigated the liver transcriptomic responses of pearl gentian grouper by comparing the gene expression of cold stress groups (20, 15, 12, and 12 °C for 6 h) with that of control group (25 °C) using PacBio SMRT-Seq and Illumina RNA-Seq technologies. In SMRT-Seq analysis, a total of 11,033 full-length transcripts were generated and used as reference sequences for further RNA-Seq analysis. In RNA-Seq analysis, 3271 differentially expressed genes (DEGs), two low-temperature specific modules (tan and blue modules), and two significantly expressed gene sets (profiles 0 and 19) were screened by differential expression analysis, weighted gene co-expression networks analysis (WGCNA), and short time-series expression miner (STEM), respectively. The intersection of the above analyses further revealed some key genes, such as PCK, ALDOB, FBP, G6pC, CPT1A, PPARα, SOCS3, PPP1CC, CYP2J, HMGCR, CDKN1B, and GADD45Bc. These genes were significantly enriched in carbohydrate metabolism, lipid metabolism, signal transduction, and endocrine system pathways. All these pathways were linked to biological functions relevant to cold adaptation, such as energy metabolism, stress-induced cell membrane changes, and transduction of stress signals. Taken together, our study explores an overall and complex regulation network of the functional genes in the liver of pearl gentian grouper, which could benefit the species in preventing damage caused by cold stress.

scholarly journals Genome-wide identification of sucrose nonfermenting-1-related protein kinase (SnRK) genes in barley and RNA-seq analyses of their expression in response to abscisic acid treatment

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zhiwei Chen ◽  
Longhua Zhou ◽  
Panpan Jiang ◽  
Ruiju Lu ◽  
Nigel G. Halford ◽  
...  
Keyword(s):  
Abscisic Acid ◽  
Gene Family ◽  
Stress Responses ◽  
Phylogenetic Analyses ◽  
Regulatory Elements ◽  
Gene Promoters ◽  
Related Protein ◽  
Rna Seq ◽  
Response Elements ◽  
Genome Data

Abstract Background Sucrose nonfermenting-1 (SNF1)-related protein kinases (SnRKs) play important roles in regulating metabolism and stress responses in plants, providing a conduit for crosstalk between metabolic and stress signalling, in some cases involving the stress hormone, abscisic acid (ABA). The burgeoning and divergence of the plant gene family has led to the evolution of three subfamilies, SnRK1, SnRK2 and SnRK3, of which SnRK2 and SnRK3 are unique to plants. Therefore, the study of SnRKs in crops may lead to the development of strategies for breeding crop varieties that are more resilient under stress conditions. In the present study, we describe the SnRK gene family of barley (Hordeum vulgare), the widespread cultivation of which can be attributed to its good adaptation to different environments. Results The barley HvSnRK gene family was elucidated in its entirety from publicly-available genome data and found to comprise 50 genes. Phylogenetic analyses assigned six of the genes to the HvSnRK1 subfamily, 10 to HvSnRK2 and 34 to HvSnRK3. The search was validated by applying it to Arabidopsis (Arabidopsis thaliana) and rice (Oryza sativa) genome data, identifying 50 SnRK genes in rice (four OsSnRK1, 11 OsSnRK2 and 35 OsSnRK3) and 39 in Arabidopsis (three AtSnRK1, 10 AtSnRK2 and 26 AtSnRK3). Specific motifs were identified in the encoded barley proteins, and multiple putative regulatory elements were found in the gene promoters, with light-regulated elements (LRE), ABA response elements (ABRE) and methyl jasmonate response elements (MeJa) the most common. RNA-seq analysis showed that many of the HvSnRK genes responded to ABA, some positively, some negatively and some with complex time-dependent responses. Conclusions The barley HvSnRK gene family is large, comprising 50 members, subdivided into HvSnRK1 (6 members), HvSnRK2 (10 members) and HvSnRK3 (34 members), showing differential positive and negative responses to ABA.

scholarly journals Complex fibroblast response to glucocorticoids may underlie variability of clinical efficacy in the vocal folds

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ryosuke Nakamura ◽  
Shigeyuki Mukudai ◽  
Renjie Bing ◽  
Michael J. Garabedian ◽  
Ryan C. Branski
Keyword(s):  
Gene Expression ◽  
Transforming Growth Factor ◽  
Regulation Of Gene Expression ◽  
Vocal Folds ◽  
Global Changes ◽  
Rna Seq ◽  
Fibrotic Response ◽  
Group A ◽  
Tgf Β1 ◽  
Nuclear Receptor Subfamily

AbstractSimilar to the hypertrophic scar and keloids, the efficacy of glucorticoids (GC) for vocal fold injury is highly variable. We previously reported dexamethasone enhanced the pro-fibrotic effects of transforming growth factor (TGF)-β as a potential mechanism for inconsistent clinical outcomes. In the current study, we sought to determine the mechanism(s) whereby GCs influence the fibrotic response and mechanisms underlying these effects with an emphasis on TGF-β and nuclear receptor subfamily 4 group A member 1 (NR4A1) signaling. Human VF fibroblasts (HVOX) were treated with three commonly-employed GCs+ /-TGF-β1. Phosphorylation of the glucocorticoid receptor (GR:NR3C1) and activation of NR4A1 was analyzed by western blotting. Genes involved in the fibrotic response, including ACTA2, TGFBR1, and TGFBR2 were analyzed by qPCR. RNA-seq was performed to identify global changes in gene expression induced by dexamethasone. GCs enhanced phosphorylation of GR at Ser211 and TGF-β-induced ACTA2 expression. Dexamethasone upregulated TGFBR1, and TGFBR2 in the presence of TGF-β1 and increased active NR4A1. RNA-seq results confirmed numerous pathways, including TGF-β signaling, affected by dexamethasone. Synergistic pro-fibrotic effects of TGF-β were observed across GCs and appeared to be mediated, at least partially, via upregulation of TGF-β receptors. Dexamethasone exhibited diverse regulation of gene expression including NR4A1 upregulation consistent with the anti-fibrotic potential of GCs.

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