Introduction

Navigate to the left side of the page to select genes of interest, categorized into four unique expression patterns in relation to the severity of COVID-19 (genes that increase, decrease, first increase then decrease, and genes that first decrease then increase as the disease severity progresses). After conducting your search, click on 'Analysis'. Subsequently, the right side of the screen will display the expression levels of various genes at different severity levels, including in healthy individuals.

The horizontal axis signifies the type of population, while the vertical axis represents the scaled gene expression levels. The title provides key information about the gene name and its respective expression pattern. This interface has been designed to provide an intuitive, comprehensive view of gene expression, facilitating the exploration of the intricate relationship between gene patterns and the progression of COVID-19.

Boxplot: Severity-related Genes

Abscissa (X-axis): This represents different patient groups based on the severity of their COVID-19 symptoms. For example, you might have groups for moderate (mh), severe (sh), and ICU (ih) patients.
Ordinate (Y-axis): This represents the gene expression amount after scaling. Scaling gene expression data is often done to standardize measurements, making it easier to compare across different genes or conditions. The gene expression levels are typically represented in a log2 scale or as Z-scores, where a higher value indicates a higher expression level.
Boxes in the Boxplot: Each box in the boxplot represents the interquartile range (IQR) of the gene expression values for a particular patient group. The line in the middle of the box is the median. The "whiskers" or lines extending from the box indicate variability outside the upper and lower quartiles.
Subtitles of the Graph: Each graph is subtitled with a specific gene expression pattern, categorized into four types: upregulation with increasing severity, downregulation with increasing severity, initial downregulation followed by upregulation, and initial upregulation followed by downregulation as the severity of the disease progresses.

Introduction

Allowing users to upload custom differential gene expression analysis results (obtained from DEseq2, edgeR, or limma, with the first column being the gene symbol and the second column as the logFC) on the left side of the page. The file format should be either a tab-separated txt or xlsx file.

Intersecting these genes with our database's background gene set, sourced from patients with various levels of COVID-19 severity, thereby revealing any commonalities.

Upon establishing an intersection with COVID-19 patients across different severity levels, the webpage offers gene set enrichment (GO, KEGG) analysis to investigate crucial molecular functions and pathway statuses. Moreover, it uses the GSEA algorithm to evaluate the extent of enrichment of the user-input gene set features across different levels of COVID-19 severity.

Key gene common with Moderate patient

genes: Gene Symbol(Overlap of user-inputted genes and those related to Moderate COVID-19.)
logFC: The log fold change of user-inputted genes.
Entrez: The entrez ID corresponding to the gene symbol.
Gene_Name: Gene full name.

Key gene common with Severe patient

genes: Gene Symbol(Overlap of user-inputted genes and those related to Severe COVID-19.)
logFC: The log fold change of user-inputted genes.
Entrez: The entrez ID corresponding to the gene symbol.
Gene_Name: Gene full name.

Key gene common with Severe patient

genes: Gene Symbol(Overlap of user-inputted genes and those related to ICU COVID-19.)
logFC: The log fold change of user-inputted genes.
Entrez: The entrez ID corresponding to the gene symbol.
Gene_Name: Gene full name.

Common Gene GO enrichment

Size of dots: The larger the dot, the higher the proportion of genes associated with the specific GO term in the gene set of interest.
Color of dots: The color of the dot indicates the significance of the GO term, usually the lighter the color, the lower the corrected P value, meaning the more statistically significant the GO term is. A corrected P value is the result of a statistical adjustment for multiple testing, reducing the likelihood of false positives.
Abscissa (X-axis): The X-axis represents different patient groups classified based on the severity of COVID-19 - moderate (mh), severe (sh), and ICU-level (ih).
Ordinate (Y-axis): The Y-axis represents the enriched GO terms that the input gene set and the differential genes from different patient severity levels share. This means that the genes that are differentially expressed among varying severities of COVID-19 patients share these functional roles.

Common Gene KEGG enrichment

Size of dots: The size of the dot signifies the gene ratio, i.e., the number of genes in your set that are involved in a specific KEGG pathway relative to the total number of genes in that pathway. A larger dot means a higher ratio.
Color of dots: The color of the dot indicates the corrected P value, which measures the statistical significance of the enrichment of your gene set in that pathway. Lighter color usually means higher significance (lower corrected P value).
Abscissa (X-axis): The X-axis represents different patient groups classified based on the severity of COVID-19 - moderate (mh), severe (sh), and ICU-level (ih).
Ordinate (Y-axis): The Y-axis represents the enriched GO terms that the input gene set and the differential genes from different patient severity levels share. This means that the genes that are differentially expressed among varying severities of COVID-19 patients share these functional roles.

Common Gene Severity enrichment

Severity: COVID-19 severity-related gene enrichment scores from user data.
pval: P value.
padj: The adjusted P value.
ES: Enrichment Score.
NES: Normalized Enrichment Score.
size: Gene set count.
leadingEdge: Leading edge genes that drive the enrichment.
Note: In general, a larger Normalized Enrichment Score (NES) and a smaller p-value in the Gene Set Enrichment Analysis (GSEA) indicate that the user-input differentially expressed genes (DEGs) are more closely aligned with the transcriptomic profile of COVID-19 patients of a given severity level (Moderate, Severe, ICU).

PubMed Literature Insights

Genes: The specific gene symbols.
Relation.to.COVID.19: Descriptive insights into the gene's role in the context of COVID-19.
pmid: PubMed ID of the associated literature.
doi: Digital Object Identifier for the referenced article.
title: Title of the article.
abstract: Abstract content of the article.
year: Publication year of the article.
month: Publication month of the article.
day: Publication day of the article.
jabbrv: Abbreviated journal name.
journal: Full name of the journal where the article was published.

This section enriches the gene expression analysis by linking the differentially expressed genes to relevant literature from PubMed.

Using our integrated PubMed search, users can explore articles related to the genes of interest. The database focuses on the role of these genes in the context of COVID-19.

Each gene is manually curated, and its relevance to COVID-19 is highlighted based on available research. The aim is to offer users an in-depth perspective into the molecular and cellular pathways influenced by COVID-19, aiding in a comprehensive understanding of its effect on the cellular transcriptome.

Consistently upregulated genes

Consistently downregulated genes

Genes with upregulation followed by downregulation

Genes with downregulation followed by upregulation