TCC-GUI: a Shiny-based application for differential expression analysis of RNA-Seq count data

Su, Wei; Sun, Jianqiang; Shimizu, Kentaro; Kadota, Koji

doi:10.1186/s13104-019-4179-2

Cited by 100 publications

(79 citation statements)

References 21 publications

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“…With this the analysis can be performed in a web-browser and results can be downloaded as a zip-le. In line with this more and more shiny applications are published currently, helping with the analysis of different kind of data [14][15][16][17][18][19][20]. Other obstacles can be too strictly de ned input formats; therefore, we aimed to keep the inputs as open and well described as possible.…”

Section: Discussionmentioning

confidence: 99%

Metabolite AutoPlotter - an application to process and visualise metabolite data in the web-browser

Pietzke

Vazquez

2020

Preprint

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Background Metabolomics is gaining popularity as a standard tool for the investigation of biological systems. Yet, parsing metabolomics data in the absence of in-house computational scientists can be overwhelming and time consuming. As a consequence of manual data processing the results are often not analysed in full depth, so potential novel findings might get lost. Methods To tackle this problem, we developed Metabolite AutoPlotter, a tool to process and visualise quantified metabolite data. Other than with bulk data visualisations, such as heat maps, the aim of the tool is to generate single plots for each metabolite. For this purpose it reads as input pre-processed metabolite-intensity tables and accepts different experimental designs, with respect to number of metabolites, conditions and replicates. The code was written in the R-scripting language and wrapped into a shiny-application that can be run online in a web-browser on https://mpietzke.shinyapps.io/autoplotter. Results We demonstrate the main features and the ease of use with two different metabolite datasets, for quantitative experiments and for stable isotope tracing experiments. We show how the plots generated by the tool can be interactively modified with respect to plot type, colours, text labels and the shown statistics. We also demonstrate the application towards 13C-tracing experiments and the seamless integration of natural abundance correction, which facilitates the better interpretation of stable isotope tracing experiments. The output of the tool is a zip-file containing one single plot for each metabolite as well as restructured tables that can be used for further analysis.Conclusion With the help of Metabolite AutoPlotter it is now possible to simplify data processing and visualisation for a wide audience. High quality plots from complex data can be generated in a short time with pressing a few buttons. This offers dramatic improvements over manual analysis. It is significantly faster and allows researchers to spend more time interpreting the results or to perform follow-up experiments. Further this eliminates potential copy-and paste errors or tedious repetitions when things need to be changed. We are sure that this tool will help to improve and speed up scientific discoveries.

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Section: Discussionmentioning

confidence: 99%

Metabolite AutoPlotter - an application to process and visualise metabolite data in the web-browser

Pietzke

Vazquez

2020

Preprint

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“…The variations and closeness between the replicates of each group were assessed by calculating the coefficient of variance and correlation. The differentially expressed proteins (DEPs) between the two groups were identified by using a web based EdgeR package [73]. A p-value < 0.05 and log 2 FC [ 5% 20% ] > 20% were considered to be significant.…”

Section: Bioinformatics Analysismentioning

confidence: 99%

“…A p-value < 0.05 and log 2 FC [ 5% 20% ] > 20% were considered to be significant. The log 2 FC value is based on m value [73]; if log2FC is 0, it shows no change, and if log2FC is 0.2, it means 20% change. The DEPs are listed in Supplementary Table S4.…”

Section: Bioinformatics Analysismentioning

confidence: 99%

Proteomics Analysis Reveals that Warburg Effect along with Modification in Lipid Metabolism Improves In Vitro Embryo Development under Low Oxygen

Shahzad

Wadood

et al. 2020

IJMS

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The molecular mechanism regulating embryo development under reduced oxygen tension remains elusive. This study aimed to identify the molecular mechanism impacting embryo development under low oxygen conditions. Buffalo embryos were cultured under 5% or 20% oxygen and were evaluated according to their morphological parameters related to embryo development. The protein profiles of these embryos were compared using iTRAQ-based quantitative proteomics. Physiological O2 (5%) significantly promoted blastocyst yield, hatching rate, embryo quality and cell count as compared to atmospheric O2 (20%). The embryos in the 5% O2 group had an improved hatching rate of cryopreserved blastocysts post-warming (p < 0.05). Comparative proteome profiles of hatched blastocysts cultured under 5% vs. 20% O2 levels identified 43 differentially expressed proteins (DEPs). Functional analysis indicated that DEPs were mainly associated with glycolysis, fatty acid degradation, inositol phosphate metabolism and terpenoid backbone synthesis. Our results suggest that embryos under physiological oxygen had greater developmental potential due to the pronounced Warburg Effect (aerobic glycolysis). Moreover, our proteomic data suggested that higher lipid degradation, an elevated cholesterol level and a higher unsaturated to saturated fatty acid ratio might be involved in the better cryo-survival ability reported in embryos cultured under low oxygen. These data provide new information on the early embryo protein repertoire and general molecular mechanisms of embryo development under varying oxygen levels.

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“…Total RNA was extracted using the RNeasy Mini kit (Qiagen and statistically analyzed by the edgeR package using TCC-GUI (10). Differentially expressed genes (DEGs) compared with each group were identified with a q value < 0.1.…”

Section: Rna-seq and Enrichment Analysismentioning

confidence: 99%

TMEM180 contributes to colorectal cancer proliferation through intracellular metabolic pathways

Anzai

Saijou

Ohnuki

et al. 2020

Preprint

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TMEM180, a novel colon cancer specific membrane protein with a 12 transmembrane topology, is upregulated at low oxygen. Previously, we established a humanized monoclonal antibody against TMEM180 aimed at clinical trials. Prior to such trials, it is necessary to clarify the function of this molecule in cancer. To compare SW480 human colon cancer cells and their TMEM180 knockdown derivatives, we analyzed proliferation and oxygen consumption, and also performed phosphorylation proteomics, metabolomics, and next-generation sequencing. The results revealed that TMEM180 promoted the growth of colon cancer but had almost no effect on oxygen consumption or expression of phosphorylated proteins. By contrast, glycolysis differed dramatically between SW480 and TMEM180 knockdown cells. TMEM180 promotes nitric oxide synthesis, suggesting that it promotes glucose metabolism and glutamine metabolism, thereby contributing to cancer growth. Overall, the results of this study support the clinical development of an anti TMEM180 antibody.

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TCC-GUI: a Shiny-based application for differential expression analysis of RNA-Seq count data

Cited by 100 publications

References 21 publications

Metabolite AutoPlotter - an application to process and visualise metabolite data in the web-browser

Metabolite AutoPlotter - an application to process and visualise metabolite data in the web-browser

Proteomics Analysis Reveals that Warburg Effect along with Modification in Lipid Metabolism Improves In Vitro Embryo Development under Low Oxygen

TMEM180 contributes to colorectal cancer proliferation through intracellular metabolic pathways

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