scholarly journals Effects of sodium houttuyfonate on transcriptome of Pseudomonas aeruginosa

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Yeye Zhao ◽  
Yuanqing Si ◽  
Longfei Mei ◽  
Jiadi Wu ◽  
Jing Shao ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Quality Control ◽  
Differentially Expressed Genes ◽  
Reference Genome ◽  
Negative Control ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Sequencing Data ◽  
Control Groups ◽  
Data Description

Abstract Objectives The purpose of this experiment is to analyze the changes of transcriptome in Pseudomonas aeruginosa under the action of sodium houttuyfonate (SH) to reveal the possible mechanism of SH inhibiting P. aeruginosa. We analyzed these data in order to compare the transcriptomic differences of P. aeruginosa in SH treatment and blank control groups. Data description In this project, RNA-seq of BGISEQ-500 platform was used to sequence the transcriptome of P. aeruginosa, and sequencing data of 8 samples of P. aeruginosa are generated as follows: SH treatment (SH1, SH2, SH3, SH4), negative control (Control 1, Control 2, Control 3, Control 4). Quality control is carried out on raw reads to determine whether the sequencing data is suitable for subsequent analysis. Totally 170.53 MB of transcriptome sequencing data is obtained. Then the filtered clean reads are aligned and compared to the reference genome to proceed second quality control. After completion, 5938 genes are assembled from sequencing data. Further quantitative analysis of genes and screening of differentially expressed genes based on gene expression level reveals that there are 2047 significantly differentially expressed genes under SH treatment, including 368 up-regulated genes and 1679 down-regulated genes.

scholarly journals Transcriptomic analysis reveals distinct resistant response by physcion and chrysophanol against cucumber powdery mildew

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1991 ◽  
Author(s):  
Yanping Li ◽  
Shilin Tian ◽  
Xiaojun Yang ◽  
Xin Wang ◽  
Yuhai Guo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Disease Resistance ◽  
Powdery Mildew ◽  
Differentially Expressed Genes ◽  
Expression Patterns ◽  
Defense Responses ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Sequencing Data ◽  
Transcriptional Change

Physcion and chrysophanol induce defense responses against powdery mildew in cucumbers. The combination of these two compounds has synergistic interaction against the disease. We performed RNA-seq on cucumber leaf samples treated with physcion and chrysophanol alone and with their combination. We generated 17.6 Gb of high-quality sequencing data (∼2 Gb per sample) and catalogued the expressions profiles of 12,293 annotated cucumber genes in each sample. We identified numerous differentially expressed genes that exhibited distinct expression patterns among the three treatments. The gene expression patterns of the Chr and Phy treatments were more similar to each other than to the Phy × Chr treatment. The Phy × Chr treatment induced the highest number of differentially expressed genes. This dramatic transcriptional change after Phy × Chr treatment leaves reflects that physcion combined with chrysophanol treatment was most closely associated with induction of disease resistance. The analysis showed that the combination treatment caused expression changes of numerous defense-related genes. These genes have known or potential roles in structural, chemical and signaling defense responses and were enriched in functional gene categories potentially responsible for cucumber resistance. These results clearly demonstrated that disease resistance in cucumber leaves was significantly influenced by the combined physcion and chrysophanol treatment. Thus, physcion and chrysophanol are appealing candidates for further investigation of the gene expression and associated regulatory mechanisms related to the defense response.

scholarly journals DEgenes Hunter - A Flexible R Pipeline for Automated RNA-seq Studies in Organisms without Reference Genome

2017 ◽  
Vol 3 (3) ◽  
pp. 31 ◽  
Author(s):  
Isabel González Gayte ◽  
Rocío Bautista Moreno ◽  
Pedro Seoane Zonjic ◽  
M. Gonzalo Claros
Keyword(s):  
Differentially Expressed Genes ◽  
Reference Genome ◽  
Real Data ◽  
Ease Of Use ◽  
Differentially Expressed ◽  
P Value ◽  
Rna Seq ◽  
Functional Interpretation ◽  
Differential Gene ◽  
Real World Datasets

Differential gene expression based on RNA-seq is widely used. Bioinformatics skills are required since no algorithm is appropriate for all experimental designs. Moreover, when working with organisms without reference genome, functional analysis is less than straightforward in most situations. DEgenes Hunter, an attempt to automate the process, is based on two independent scripts, one for differential expression and one for functional interpretation. Based on replicates, the R script decides which of the edgeR, DEseq2, NOISeq and limma algorithms are appropriate. It performs quality control calculations and provides the prevalent, most reliable, set of differentially expressed genes, and lists all other possible candidates for further functional interpretation. It also provides a combined P-value that allows differentially expressed genes ranking. It has been tested with synthetic and real-world datasets, showing in both cases ease of use and reliable results. With real data, DEgenes Hunter offers straightforward functional interpretation.

scholarly journals bcbioRNASeq: R package for bcbio RNA-seq analysis

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1976 ◽  
Author(s):  
Michael J. Steinbaugh ◽  
Lorena Pantano ◽  
Rory D. Kirchner ◽  
Victor Barrera ◽  
Brad A. Chapman ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
R Package ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Control Analysis ◽  
Rna Seq ◽  
Sequencing Data ◽  
Post Process

RNA-seq analysis involves multiple steps from processing raw sequencing data to identifying, organizing, annotating, and reporting differentially expressed genes. bcbio is an open source, community-maintained framework providing automated and scalable RNA-seq methods for identifying gene abundance counts. We have developed bcbioRNASeq, a Bioconductor package that provides ready-to-render templates and wrapper functions to post-process bcbio output data. bcbioRNASeq automates the generation of high-level RNA-seq reports, including identification of differentially expressed genes, functional enrichment analysis and quality control analysis.

scholarly journals MarcoPolo: a clustering-free approach to the exploration of differentially expressed genes along with group information in single-cell RNA-seq data

2020 ◽  
Author(s):  
Chanwoo Kim ◽  
Hanbin Lee ◽  
Juhee Jeong ◽  
Keehoon Jung ◽  
Buhm Han
Keyword(s):  
Single Cell ◽  
Differentially Expressed Genes ◽  
Differential Expression Analysis ◽  
Feature Selection Method ◽  
Real Data ◽  
Cell Types ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Sequencing Data ◽  
Group Information

ABSTRACTA common approach to analyzing single-cell RNA-sequencing data is to cluster cells first and then identify differentially expressed genes based on the clustering result. However, clustering has an innate uncertainty and can be imperfect, undermining the reliability of differential expression analysis results. To overcome this challenge, we present MarcoPolo, a clustering-free approach to exploring differentially expressed genes. To find informative genes without clustering, MarcoPolo exploits the bimodality of gene expression to learn the group information of the cells with respect to the expression level directly from given data. Using simulations and real data analyses, we showed that our method puts biologically informative genes at higher ranks more accurately and robustly than other existing methods. As our method provides information on how cells can be grouped for each gene, it can help identify cell types that are not separated well in the standard clustering process. Our method can also be used as a feature selection method to improve the robustness against changes in the number of genes used in clustering.

scholarly journals bcbioRNASeq: R package for bcbio RNA-seq analysis

F1000Research ◽  
2018 ◽  
Vol 6 ◽  
pp. 1976 ◽  
Author(s):  
Michael J. Steinbaugh ◽  
Lorena Pantano ◽  
Rory D. Kirchner ◽  
Victor Barrera ◽  
Brad A. Chapman ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
R Package ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Bioconductor Package ◽  
Rna Seq ◽  
Sequencing Data ◽  
Output Data ◽  
Post Process ◽  
High Level

RNA-seq analysis involves multiple steps, from processing raw sequencing data to identifying, organizing, annotating, and reporting differentially expressed genes. bcbio is an open source, community-maintained framework providing automated and scalable RNA-seq methods for identifying gene abundance counts. We have developed bcbioRNASeq, a Bioconductor package that provides ready-to-render templates, objects and wrapper functions to post-process bcbio RNA sequencing output data. bcbioRNASeq helps automate the generation of high-level RNA-seq reports, facilitating the quality control analyses, identification of differentially expressed genes and functional enrichment analyses.

scholarly journals SARTools: a DESeq2- and edgeR-based R pipeline for comprehensive differential analysis of RNA-Seq data

2015 ◽  
Author(s):  
Hugo Varet ◽  
Jean-Yves Coppée ◽  
Marie-Agnès Dillies
Keyword(s):  
Quality Control ◽  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Biological Factor ◽  
Model Parameters ◽  
Differential Analysis ◽  
Rna Seq ◽  
Diagnostic Plots ◽  
Analysis Process ◽  
R Packages

Background Several R packages exist for the detection of differentially expressed genes from RNA-Seq data. The analysis process includes three main steps, namely normalization, dispersion estimation and test for differential expression. Quality control steps along this process are recommended but not mandatory, and failing to check the characteristics of the dataset may lead to spurious results. In addition, normalization methods and statistical models are not exchangeable across the packages without adequate transformations the users are often not aware of. Thus, dedicated analysis pipelines are needed to include systematic quality control steps and prevent errors from misusing the proposed methods. Results SARTools is an R pipeline for differential analysis of RNA-Seq count data. It can handle designs involving two or more conditions of a single biological factor with or without a blocking factor (such as a batch effect or a sample pairing). It is based on DESeq2 and edgeR and is composed of an R package and two R script templates (for DESeq2 and edgeR respectively). Tuning a small number of parameters and executing one of the R scripts, users have access to the full results of the analysis, including lists of differentially expressed genes and a HTML report that (i) displays diagnostic plots for quality control and model hypotheses checking and (ii) keeps track of the whole analysis process, parameter values and versions of the R packages used. Conclusions SARTools provides systematic quality controls of the dataset as well as diagnostic plots that help to tune the model parameters. It gives access to the main parameters of DESeq2 and edgeR and prevents untrained users from misusing some functionalities of both packages. By keeping track of all the parameters of the analysis process it fits the requirements of reproducible research.

scholarly journals Transcriptomic and ChIP-seq Integrative Analysis Reveals Important Roles of Epigenetically Regulated lncRNAs in Placental Development in Meishan Pigs

Genes ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 397
Author(s):  
Dadong Deng ◽  
Xihong Tan ◽  
Kun Han ◽  
Ruimin Ren ◽  
Jianhua Cao ◽  
...  
Keyword(s):  
Differentially Expressed ◽  
Rna Seq ◽  
Sequencing Data ◽  
Placental Development ◽  
Cytoskeleton Organization ◽  
New Class ◽  
Chromatin Immunoprecipitation Sequencing ◽  
Non Coding Rnas ◽  
Two Stages ◽  
Regulatory Functions

The development of the placental fold, which increases the maternal–fetal interacting surface area, is of primary importance for the growth of the fetus throughout the whole pregnancy. However, the mechanisms involved remain to be fully elucidated. Increasing evidence has revealed that long non-coding RNAs (lncRNAs) are a new class of RNAs with regulatory functions and could be epigenetically regulated by histone modifications. In this study, 141 lncRNAs (including 73 up-regulated and 68 down-regulated lncRNAs) were identified to be differentially expressed in the placentas of pigs during the establishment and expanding stages of placental fold development. The differentially expressed lncRNAs and genes (DElncRNA-DEgene) co-expression network analysis revealed that these differentially expressed lncRNAs (DElncRNAs) were mainly enriched in pathways of cell adhesion, cytoskeleton organization, epithelial cell differentiation and angiogenesis, indicating that the DElncRNAs are related to the major events that occur during placental fold development. In addition, we integrated the RNA-seq (RNA sequencing) data with the ChIP-seq (chromatin immunoprecipitation sequencing) data of H3K4me3/H3K27ac produced from the placental samples of pigs from the two stages (gestational days 50 and 95). The analysis revealed that the changes in H3K4me3 and/or H3K27ac levels were significantly associated with the changes in the expression levels of 37 DElncRNAs. Furthermore, several H3K4me3/H3K27ac-lncRNAs were characterized to be significantly correlated with genes functionally related to placental development. Thus, this study provides new insights into understanding the mechanisms for the placental development of pigs.

scholarly journals HIV-1 Infection Transcriptomics: Meta-Analysis of CD4+ T Cells Gene Expression Profiles

Viruses ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 244 ◽  
Author(s):  
Antonio Victor Campos Coelho ◽  
Rossella Gratton ◽  
João Paulo Britto de Melo ◽  
José Leandro Andrade-Santos ◽  
Rafael Lima Guimarães ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Differentially Expressed Genes ◽  
Cd4 T Cells ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Infected Cells ◽  
Hiv 1

HIV-1 infection elicits a complex dynamic of the expression various host genes. High throughput sequencing added an expressive amount of information regarding HIV-1 infections and pathogenesis. RNA sequencing (RNA-Seq) is currently the tool of choice to investigate gene expression in a several range of experimental setting. This study aims at performing a meta-analysis of RNA-Seq expression profiles in samples of HIV-1 infected CD4+ T cells compared to uninfected cells to assess consistently differentially expressed genes in the context of HIV-1 infection. We selected two studies (22 samples: 15 experimentally infected and 7 mock-infected). We found 208 differentially expressed genes in infected cells when compared to uninfected/mock-infected cells. This result had moderate overlap when compared to previous studies of HIV-1 infection transcriptomics, but we identified 64 genes already known to interact with HIV-1 according to the HIV-1 Human Interaction Database. A gene ontology (GO) analysis revealed enrichment of several pathways involved in immune response, cell adhesion, cell migration, inflammation, apoptosis, Wnt, Notch and ERK/MAPK signaling.

scholarly journals Phytophthora infestans Sporangia Produced in Culture and on Tomato Leaflet Lesions Show Marked Differences in Indirect Germination Rates, Aggressiveness, and Global Transcription Profiles

2019 ◽  
Vol 32 (5) ◽  
pp. 515-526 ◽  
Author(s):  
William E. Fry ◽  
Sean P. Patev ◽  
Kevin L. Myers ◽  
Kan Bao ◽  
Zhangjun Fei
Keyword(s):  
Phytophthora Infestans ◽  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Moist Chamber ◽  
Field Isolates ◽  
Rxlr Effectors ◽  
Transcription Profiles ◽  
Independent Field ◽  
Pure Cultures

Sporangia of Phytophthora infestans from pure cultures on agar plates are typically used in lab studies, whereas sporangia from leaflet lesions drive natural infections and epidemics. Multiple assays were performed to determine if sporangia from these two sources are equivalent. Sporangia from plate cultures showed much lower rates of indirect germination and produced much less disease in field and moist-chamber tests. This difference in aggressiveness was observed whether the sporangia had been previously incubated at 4°C (to induce indirect germination) or at 21°C (to prevent indirect germination). Furthermore, lesions caused by sporangia from plates produced much less sporulation. RNA-Seq analysis revealed that thousands of the >17,000 P. infestans genes with a RPKM (reads per kilobase of exon model per million mapped reads) >1 were differentially expressed in sporangia obtained from plate cultures of two independent field isolates compared with sporangia of those isolates from leaflet lesions. Among the significant differentially expressed genes (DEGs), putative RxLR effectors were overrepresented, with almost half of the 355 effectors with RPKM >1 being up- or downregulated. DEGs of both isolates include nine flagellar-associated genes, and all were down-regulated in plate sporangia. Ten elicitin genes were also detected as DEGs in both isolates, and nine (including INF1) were up-regulated in plate sporangia. These results corroborate previous observations that sporangia produced from plates and leaflets sometimes yield different experimental results and suggest hypotheses for potential mechanisms. We caution that use of plate sporangia in assays may not always produce results reflective of natural infections and epidemics.

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