Transcriptomic analysis of Camellia oleifera in response to drought stress using high throughput RNA-seq

2017 ◽  
Vol 64 (5) ◽  
pp. 728-737 ◽  
Author(s):  
H. Yang ◽  
H. Y. Zhou ◽  
X. N. Yang ◽  
J. J. Zhan ◽  
H. Zhou ◽  
...  
Keyword(s):  
Drought Stress ◽  
High Throughput ◽  
Transcriptomic Analysis ◽  
Camellia Oleifera ◽  
Rna Seq

scholarly journals Identification of novel mRNAs and lncRNAs associated with mouse experimental colitis and human inflammatory bowel disease

2018 ◽  
Vol 315 (5) ◽  
pp. G722-G733 ◽  
Author(s):  
Carl Robert Rankin ◽  
Evangelos Theodorou ◽  
Ivy Ka Man Law ◽  
Lorraine Rowe ◽  
Efi Kokkotou ◽  
...  
Keyword(s):  
Inflammatory Bowel Disease ◽  
Mouse Models ◽  
High Throughput ◽  
Bowel Disease ◽  
Noncoding Rnas ◽  
Transcriptomic Analysis ◽  
Differentially Expressed ◽  
Clinical Samples ◽  
Rna Seq ◽  
Inflammatory Bowel

Inflammatory bowel disease (IBD) is a complex disorder that is associated with significant morbidity. While many recent advances have been made with new diagnostic and therapeutic tools, a deeper understanding of its basic pathophysiology is needed to continue this trend toward improving treatments. By utilizing an unbiased, high-throughput transcriptomic analysis of two well-established mouse models of colitis, we set out to uncover novel coding and noncoding RNAs that are differentially expressed in the setting of colonic inflammation. RNA-seq analysis was performed using colonic tissue from two mouse models of colitis, a dextran sodium sulfate-induced model and a genetic-induced model in mice lacking IL-10. We identified 81 coding RNAs that were commonly altered in both experimental models. Of these coding RNAs, 12 of the human orthologs were differentially expressed in a transcriptomic analysis of IBD patients. Interestingly, 5 of the 12 of human differentially expressed genes have not been previously identified as IBD-associated genes, including ubiquitin D. Our analysis also identified 15 noncoding RNAs that were differentially expressed in either mouse model. Surprisingly, only three noncoding RNAs were commonly dysregulated in both of these models. The discovery of these new coding and noncoding RNAs expands our transcriptional knowledge of mouse models of IBD and offers additional targets to deepen our understanding of the pathophysiology of IBD. NEW & NOTEWORTHY Much of the genome is transcribed as non-protein-coding RNAs; however, their role in inflammatory bowel disease is largely unknown. This study represents the first of its kind to analyze the expression of long noncoding RNAs in two mouse models of inflammatory bowel disease and correlate them to human clinical samples. Using high-throughput RNA-seq analysis, we identified new coding and noncoding RNAs that were differentially expressed such as ubiquitin D and 5730437C11Rik.

scholarly journals Transcriptomic Analysis of Drought Stress Responses in Ammopiptanthus mongolicus Leaves Using the RNA-Seq Technique

PLoS ONE ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. e0124382 ◽  
Author(s):  
Fei Gao ◽  
Jianyue Wang ◽  
Shanjun Wei ◽  
Zhanglei Li ◽  
Ning Wang ◽  
...  
Keyword(s):  
Drought Stress ◽  
Stress Responses ◽  
Transcriptomic Analysis ◽  
Rna Seq ◽  
Ammopiptanthus Mongolicus

scholarly journals Transcriptomic analysis of drought stress responses of sea buckthorn (Hippophae rhamnoidessubsp. sinensis) by RNA-Seq

PLoS ONE ◽  
2018 ◽  
Vol 13 (8) ◽  
pp. e0202213 ◽  
Author(s):  
Guisheng Ye ◽  
Yuhua Ma ◽  
Zhipeng Feng ◽  
Xiaofen Zhang
Keyword(s):  
Drought Stress ◽  
Stress Responses ◽  
Sea Buckthorn ◽  
Transcriptomic Analysis ◽  
Rna Seq

scholarly journals Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Zeeshan Ahmed ◽  
Eduard Gibert Renart ◽  
Saman Zeeshan ◽  
XinQi Dong
Keyword(s):  
Data Analysis ◽  
Patient Care ◽  
Expression Analysis ◽  
High Throughput ◽  
Gene Annotation ◽  
Next Generation Sequencing Data ◽  
Rna Seq ◽  
Sequencing Data ◽  
Complex Disorders ◽  
Transcriptomics Data

Abstract Background Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists. Results In this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients’ transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer’s disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases. Conclusions We emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.

scholarly journals Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses

2021 ◽  
Vol 13 (1) ◽  
pp. 147
Author(s):  
Tom De Swaef ◽  
Wouter H. Maes ◽  
Jonas Aper ◽  
Joost Baert ◽  
Mathias Cougnon ◽  
...  
Keyword(s):  
Drought Stress ◽  
Drought Tolerance ◽  
Lolium Perenne ◽  
High Throughput ◽  
Festuca Arundinacea ◽  
Stress Index ◽  
Forage Grasses ◽  
Crop Water Stress Index ◽  
Ploidy Levels ◽  
Field Phenotyping

The persistence and productivity of forage grasses, important sources for feed production, are threatened by climate change-induced drought. Breeding programs are in search of new drought tolerant forage grass varieties, but those programs still rely on time-consuming and less consistent visual scoring by breeders. In this study, we evaluate whether Unmanned Aerial Vehicle (UAV) based remote sensing can complement or replace this visual breeder score. A field experiment was set up to test the drought tolerance of genotypes from three common forage types of two different species: Festuca arundinacea, diploid Lolium perenne and tetraploid Lolium perenne. Drought stress was imposed by using mobile rainout shelters. UAV flights with RGB and thermal sensors were conducted at five time points during the experiment. Visual-based indices from different colour spaces were selected that were closely correlated to the breeder score. Furthermore, several indices, in particular H and NDLab, from the HSV (Hue Saturation Value) and CIELab (Commission Internationale de l’éclairage) colour space, respectively, displayed a broad-sense heritability that was as high or higher than the visual breeder score, making these indices highly suited for high-throughput field phenotyping applications that can complement or even replace the breeder score. The thermal-based Crop Water Stress Index CWSI provided complementary information to visual-based indices, enabling the analysis of differences in ecophysiological mechanisms for coping with reduced water availability between species and ploidy levels. All species/types displayed variation in drought stress tolerance, which confirms that there is sufficient variation for selection within these groups of grasses. Our results confirmed the better drought tolerance potential of Festuca arundinacea, but also showed which Lolium perenne genotypes are more tolerant.

scholarly journals High-Throughput Phenotyping Methods for Breeding Drought-Tolerant Crops

2021 ◽  
Vol 22 (15) ◽  
pp. 8266
Author(s):  
Minsu Kim ◽  
Chaewon Lee ◽  
Subin Hong ◽  
Song Lim Kim ◽  
Jeong-Ho Baek ◽  
...  
Keyword(s):  
Drought Stress ◽  
High Throughput ◽  
Large Scale ◽  
Crop Yields ◽  
Plant Traits ◽  
Rapid Development ◽  
Plant Responses ◽  
Phenotypic Data ◽  
Agricultural Technologies ◽  
High Throughput Phenotyping

Drought is a main factor limiting crop yields. Modern agricultural technologies such as irrigation systems, ground mulching, and rainwater storage can prevent drought, but these are only temporary solutions. Understanding the physiological, biochemical, and molecular reactions of plants to drought stress is therefore urgent. The recent rapid development of genomics tools has led to an increasing interest in phenomics, i.e., the study of phenotypic plant traits. Among phenomic strategies, high-throughput phenotyping (HTP) is attracting increasing attention as a way to address the bottlenecks of genomic and phenomic studies. HTP provides researchers a non-destructive and non-invasive method yet accurate in analyzing large-scale phenotypic data. This review describes plant responses to drought stress and introduces HTP methods that can detect changes in plant phenotypes in response to drought.

scholarly journals Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tracy M. Yamawaki ◽  
Daniel R. Lu ◽  
Daniel C. Ellwanger ◽  
Dev Bhatt ◽  
Paolo Manzanillo ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Immune Cell ◽  
Cell Types ◽  
Data Interpretation ◽  
Detection Sensitivity ◽  
Rna Seq ◽  
Cell Recovery

Abstract Background Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluated methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5′ v1 and 3′ v3 methods. We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures. Conclusion Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

High-Throughput Single-Cell RNA-Seq of Large Cells and Nuclei

2017 ◽  
Vol 37 (17) ◽  
pp. 12-13
Author(s):  
Jennifer Chew ◽  
Adam Bemis ◽  
Ronald Lebofsky ◽  
Anna Quinlan ◽  
Kelly Kaihara
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Rna Seq

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.

scholarly journals Strand-specific libraries for high throughput RNA sequencing (RNA-Seq) prepared without poly(A) selection

Silence ◽  
2012 ◽  
Vol 3 (1) ◽  
pp. 9 ◽  
Author(s):  
Zhao Zhang ◽  
William E Theurkauf ◽  
Zhiping Weng ◽  
Phillip D Zamore
Keyword(s):  
Rna Sequencing ◽  
High Throughput ◽  
Rna Seq
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