scholarly journals SCORE: Smart Consensus Of RNA Expression—a consensus tool for detecting differentially expressed genes in bacteria

2020 ◽  
Author(s):  
Silver A Wolf ◽  
Lennard Epping ◽  
Sandro Andreotti ◽  
Knut Reinert ◽  
Torsten Semmler
Keyword(s):  
Differentially Expressed Genes ◽  
Current Method ◽  
Differentially Expressed ◽  
Supplementary Information ◽  
Rna Expression ◽  
Rna Seq ◽  
Bacterial Rna ◽  
Differential Gene ◽  
No Gold Standard ◽  
Selection Of

Abstract Summary RNA-sequencing (RNA-Seq) is the current method of choice for studying bacterial transcriptomes. To date, many computational pipelines have been developed to predict differentially expressed genes from RNA-Seq data, but no gold-standard has been widely accepted. We present the Snakemake-based tool Smart Consensus Of RNA Expression (SCORE) which uses a consensus approach founded on a selection of well-established tools for differential gene expression analysis. This allows SCORE to increase the overall prediction accuracy and to merge varying results into a single, human-readable output. SCORE performs all steps for the analysis of bacterial RNA-Seq data, from read preprocessing to the overrepresentation analysis of significantly associated ontologies. Development of consensus approaches like SCORE will help to streamline future RNA-Seq workflows and will fundamentally contribute to the creation of new gold-standards for the analysis of these types of data. Availability and implementation https://github.com/SiWolf/SCORE. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Performance of gene expression analyses using de novo assembled transcripts in polyploid species

2018 ◽  
Author(s):  
Ling-Yun Chen ◽  
Diego F. Morales-Briones ◽  
Courtney N. Passow ◽  
Ya Yang
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Reference Genes ◽  
De Novo ◽  
Differentially Expressed ◽  
Supplementary Information ◽  
Network Analyses ◽  
Gene Expression Analyses ◽  
Polyploidy Event ◽  
Differential Gene

AbstractMotivationQuality of gene expression analyses using de novo assembled transcripts in species experienced recent polyploidization is yet unexplored.ResultsFive plant species with various polyploidy history were used for differential gene expression (DGE) analyses. DGE analyses using putative genes inferred by Trinity performed similar to or better than Corset and Grouper in precision, but lower in sensitivity. In species that lack polyploidy event in the past few million years, DGE analyses using de novo assembled transcriptome identified 50–76% of the differentially expressed genes recovered by mapping reads to the reference genes. However, in species with more recent polyploidy event, the percentage decreased to 7–30%. In addition, 7–89% of differentially expressed genes from de novo assembly are contaminations. Gene co-expression network analyses using de novo assemblies vs. mapping to the reference genes recovered the same module that significantly correlated with treatment in one of the five species tested.Availability and ImplementationCommands and scripts used in this study are available at https://bitbucket.org/lychen83/chen_et_al_2018_benchmark_dge/; Analysis files are available at Dryad doi: [email protected] informationSupplementary data are available at Bioinformatics online

scholarly journals ViDGER: An R package for integrative interpretation of differential gene expression results of RNA-seq data

2018 ◽  
Author(s):  
Adam McDermaid ◽  
Brandon Monier ◽  
Jing Zhao ◽  
Qin Ma
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Differential Gene Expression ◽  
Differentially Expressed Genes ◽  
R Package ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Differential Gene

AbstractDifferential gene expression (DGE) is one of the most common applications of RNA-sequencing (RNA-seq) data. This process allows for the elucidation of differentially expressed genes (DEGs) across two or more conditions. Interpretation of the DGE results can be non-intuitive and time consuming due to the variety of formats based on the tool of choice and the numerous pieces of information provided in these results files. Here we present an R package, ViDGER (Visualization of Differential Gene Expression Results using R), which contains nine functions that generate information-rich visualizations for the interpretation of DGE results from three widely-used tools, Cuffdiff, DESeq2, and edgeR.

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 Transcriptomic Analysis for the Identification of Metabolic Pathway Genes Related to Toluene Response in Ardisia pusilla

Plants ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1011
Author(s):  
Junping Xu ◽  
Chang Ho Ahn ◽  
Ju Young Shin ◽  
Pil Man Park ◽  
Hye Ryun An ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Metabolic Pathways ◽  
Differentially Expressed Gene ◽  
Carotenoid Biosynthesis ◽  
Raw Material ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Quantitative Real Time Pcr ◽  
Phenylpropanoid Biosynthesis ◽  
Differential Gene

Toluene is an industrial raw material and solvent that can be found abundantly in our daily life products. The amount of toluene vapor is one of the most important measurements for evaluating air quality. The evaluation of toluene scavenging ability of different plants has been reported, but the mechanism of plant response to toluene is only partially understood. In this study, we performed RNA sequencing (RNA-seq) analysis to detect differential gene expression in toluene-treated and untreated leaves of Ardisiapusilla. A total of 88,444 unigenes were identified by RNA-seq analysis, of which 49,623 were successfully annotated and 4101 were differentially expressed. Gene ontology analysis revealed several subcategories of genes related to toluene response, including cell part, cellular process, organelle, and metabolic processes. We mapped the main metabolic pathways of genes related to toluene response and found that the differentially expressed genes were mainly involved in glycolysis/gluconeogenesis, starch and sucrose metabolism, glycerophospholipid metabolism, carotenoid biosynthesis, phenylpropanoid biosynthesis, and flavonoid biosynthesis. In addition, 53 transcription factors belonging to 13 transcription factor families were identified. We verified 10 differentially expressed genes related to metabolic pathways using quantitative real-time PCR and found that the results of RNA-seq were positively correlated with them, indicating that the transcriptome data were reliable. This study provides insights into the metabolic pathways involved in toluene response in plants.

scholarly journals Fast detection of differential chromatin domains with SCIDDO

2020 ◽  
Author(s):  
Peter Ebert ◽  
Marcel H Schulz
Keyword(s):  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
Chromatin State ◽  
Supplementary Information ◽  
Differential Analysis ◽  
Chromatin Domains ◽  
Chromatin Dynamics ◽  
Histone Marks ◽  
Chromatin Immunoprecipitation Sequencing ◽  
Downstream Analysis

Abstract Motivation The generation of genome-wide maps of histone modifications using chromatin immunoprecipitation sequencing (ChIP-seq) is a standard approach to dissect the complexity of the epigenome. Interpretation and differential analysis of histone datasets remains challenging due to regulatory meaningful co-occurrences of histone marks and their difference in genomic spread. To ease interpretation, chromatin state segmentation maps are a commonly employed abstraction combining individual histone marks. We developed the tool SCIDDO as a fast, flexible, and statistically sound method for the differential analysis of chromatin state segmentation maps. Results We demonstrate the utility of SCIDDO in a comparative analysis that identifies differential chromatin domains (DCD) in various regulatory contexts and with only moderate computational resources. We show that the identified DCDs correlate well with observed changes in gene expression and can recover a substantial number of differentially expressed genes. We showcase SCIDDO’s ability to directly interrogate chromatin dynamics such as enhancer switches in downstream analysis, which simplifies exploring specific questions about regulatory changes in chromatin. By comparing SCIDDO to competing methods, we provide evidence that SCIDDO’s performance in identifying differentially expressed genes (DEG) via differential chromatin marking is more stable across a range of cell-type comparisons and parameter cut-offs. Availability The SCIDDO source code is openly available under github.com/ptrebert/sciddo Supplementary information Supplementary data are available at Bioinformatics online.

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.

scholarly journals Bioinformatics-based Screening of Key Genes for Saponin Metabolism in Quinoa

2021 ◽  
Author(s):  
Chengang Guo ◽  
Zhimin wei ◽  
Wei Lyu ◽  
Yanlou Geng
Keyword(s):  
Differentially Expressed Genes ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Hierarchical Cluster ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Differential Analysis ◽  
Rna Seq ◽  
Protein Coding ◽  
Key Genes

Abstract Quinoa saponins have complex, diverse and evident physiologic activities. However, the key regulatory genes for quinoa saponin metabolism are not yet well studied. The purpose of this study was to explore genes closely related to quinoa saponin metabolism. In this study, the significantly differentially expressed genes in yellow quinoa were firstly screened based on RNA-seq technology. Then, the key genes for saponin metabolism were selected by gene set enrichment analysis (GSEA) and principal component analysis (PCA) statistical methods. Finally, the specificity of the key genes was verified by hierarchical clustering. The results of differential analysis showed that 1654 differentially expressed genes were achieved after pseudogenes deletion. Therein, there were 142 long non-coding genes and 1512 protein-coding genes. Based on GSEA analysis, 116 key candidate genes were found to be significantly correlated with quinoa saponin metabolism. Through PCA dimension reduction analysis, 57 key genes were finally obtained. Hierarchical cluster analysis further demonstrated that these key genes can clearly separate the four groups of samples. The present results could provide references for the breeding of sweet quinoa and would be helpful for the rational utilization of quinoa saponins.

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