scholarly journals Dashboard-style interactive plots for RNA-seq analysis are R Markdown ready with Glimma 2.0

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Hasaru Kariyawasam ◽  
Shian Su ◽  
Oliver Voogd ◽  
Matthew E Ritchie ◽  
Charity W Law
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Interactive Graphics ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Differential Gene Expression Analysis ◽  
Differential Gene ◽  
High Level ◽  
User Friendly

Abstract Glimma 1.0 introduced intuitive, point-and-click interactive graphics for differential gene expression analysis. Here, we present a major update to Glimma that brings improved interactivity and reproducibility using high-level visualization frameworks for R and JavaScript. Glimma 2.0 plots are now readily embeddable in R Markdown, thus allowing users to create reproducible reports containing interactive graphics. The revamped multidimensional scaling plot features dashboard-style controls allowing the user to dynamically change the colour, shape and size of sample points according to different experimental conditions. Interactivity was enhanced in the MA-style plot for comparing differences to average expression, which now supports selecting multiple genes, export options to PNG, SVG or CSV formats and includes a new volcano plot function. Feature-rich and user-friendly, Glimma makes exploring data for gene expression analysis more accessible and intuitive and is available on Bioconductor and GitHub.

scholarly journals Dashboard-style interactive plots for RNA-seq analysis are R Markdown ready with Glimma 2.0

2021 ◽  
Author(s):  
Has Kariyawasam ◽  
Shian Su ◽  
Oliver Voogd ◽  
Matthew E Ritchie ◽  
Charity W Law
Keyword(s):  
Gene Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Interactive Graphics ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Differential Gene Expression Analysis ◽  
Differential Gene ◽  
High Level ◽  
User Friendly

Glimma 1.0 introduced intuitive, point-and-click interactive graphics for differential gene expression analysis. Here, we present a major update to Glimma which brings improved interactivity and reproducibility using high-level visualisation frameworks for R and JavaScript. Glimma 2.0 plots are now readily embeddable in R Markdown, thus allowing users to create reproducible reports containing interactive graphics. The revamped multidimensional scaling plot features dashboard-style controls allowing the user to dynamically change the colour, shape and size of sample points according to different experimental conditions. Interactivity was enhanced in the MA-style plot for comparing differences to average expression, which now supports selecting multiple genes, export options to PNG, SVG or CSV formats and includes a new volcano plot function. Feature-rich and user-friendly, Glimma makes exploring data for gene expression analysis more accessible and intuitive and is available on Bioconductor and GitHub.

scholarly journals Computational Methods to Study Human Transcript Variants in COVID-19 Infected Lung Cancer Cells

2021 ◽  
Vol 22 (18) ◽  
pp. 9684
Author(s):  
Jiao Sun ◽  
Naima Ahmed Fahmi ◽  
Heba Nassereddeen ◽  
Sze Cheng ◽  
Irene Martinez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Computational Methods ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Rna Seq ◽  
Differential Gene Expression Analysis ◽  
Transcript Variants ◽  
Infected Cells ◽  
Differential Gene

Microbes and viruses are known to alter host transcriptomes by means of infection. In light of recent challenges posed by the COVID-19 pandemic, a deeper understanding of the disease at the transcriptome level is needed. However, research about transcriptome reprogramming by post-transcriptional regulation is very limited. In this study, computational methods developed by our lab were applied to RNA-seq data to detect transcript variants (i.e., alternative splicing (AS) and alternative polyadenylation (APA) events). The RNA-seq data were obtained from a publicly available source, and they consist of mock-treated and SARS-CoV-2 infected (COVID-19) lung alveolar (A549) cells. Data analysis results show that more AS events are found in SARS-CoV-2 infected cells than in mock-treated cells, whereas fewer APA events are detected in SARS-CoV-2 infected cells. A combination of conventional differential gene expression analysis and transcript variants analysis revealed that most of the genes with transcript variants are not differentially expressed. This indicates that no strong correlation exists between differential gene expression and the AS/APA events in the mock-treated or SARS-CoV-2 infected samples. These genes with transcript variants can be applied as another layer of molecular signatures for COVID-19 studies. In addition, the transcript variants are enriched in important biological pathways that were not detected in the studies that only focused on differential gene expression analysis. Therefore, the pathways may lead to new molecular mechanisms of SARS-CoV-2 pathogenesis.

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