Journal: Nature Communications

Article Title: NMR and MS reveal characteristic metabolome atlas and optimize esophageal squamous cell carcinoma early detection

doi: 10.1038/s41467-024-46837-0

Figure Lengend Snippet: A Pie chart of quantified metabolite categories. Colors represent different compound super-classes; coloration follows the legend counterclockwise per each pie chart. B Volcano plot analysis performed on early-ESCC tissue samples vs. normal controls. P -values were determined by two-sided t -test without adjustments. Differentially-expressed metabolites are indicated by blue dots (down-regulation relative to normal controls) and red dots (up-regulation relative to normal controls), respectively. Gray dots indicate no significant difference. C Treemap of the most enriched KEGG pathways in early-ESCC tissue samples. Relative betweenness centrality was the selected node importance measure for pathway topological analysis. Each square represents a metabolic pathway; the square size represents the impact factor in the topological analysis; the color of the squares indicates the p -value of the enrichment analysis; and the darker the color, the more significant the enrichment. D O2PLS loading plots of the key differential metabolites analyzed by NMR-based and MS-based metabolomics. The top 30 metabolites in early-ESCC tissues detected by NMR and MS are labeled in purple and blue, respectively. Source data are provided as a Source Data file.

Article Snippet: KEGG metabolic pathway enrichment analysis was further performed (Fig. ), and we found that ‘Alanine, aspartate and glutamate metabolism’ consistently exhibited the most significant changes from tumor-bearing to tumor-resected phase and to healthy status in biofluids (Supplementary Fig. ).

Techniques: Labeling

Journal: Advances and Applications in Bioinformatics and Chemistry : AABC

Article Title: Tools for visualization and analysis of molecular networks, pathways, and -omics data

doi: 10.2147/AABC.S63534

Figure Lengend Snippet: Visualization of differential expression data on the Glycolysis pathway using different software packages. Notes: ( A ) KEGG Mapper and KEGGViewer were used to render -omics data on the KEGG Glycolysis pathway. Upregulated genes are shown in yellow, and downregulated genes are highlighted in blue. ( B ) PathVisio was used to visualize -omics data on the Glycolysis pathway from WikiPathways. Upregulated genes are colored in red shades, and downregulated genes are shown in green shading. PathVisio is able to combine two datasets on the same pathway map. ( C ) The Reactome analysis toolbox was used to visualize -omics data on the Glycolysis pathway. Upregulated genes are highlighted in yellow, and downregulated genes are highlighted in blue.

Article Snippet: KEGG-based Pathway Visualization Tool for Complex Omics Data , Web , KEGG.

Techniques: Quantitative Proteomics, Software

Journal: Advances and Applications in Bioinformatics and Chemistry : AABC

Article Title: Tools for visualization and analysis of molecular networks, pathways, and -omics data

doi: 10.2147/AABC.S63534

Figure Lengend Snippet: Visualization of differential expression data on the Glycolysis pathway using different software packages. Notes: ( A ) KEGG Mapper and KEGGViewer were used to render -omics data on the KEGG Glycolysis pathway. Upregulated genes are shown in yellow, and downregulated genes are highlighted in blue. ( B ) PathVisio was used to visualize -omics data on the Glycolysis pathway from WikiPathways. Upregulated genes are colored in red shades, and downregulated genes are shown in green shading. PathVisio is able to combine two datasets on the same pathway map. ( C ) The Reactome analysis toolbox was used to visualize -omics data on the Glycolysis pathway. Upregulated genes are highlighted in yellow, and downregulated genes are highlighted in blue.

Article Snippet: The KEGG-based Pathway Visualization Tool for Complex Omics Data is another practical, user-friendly method to visualize expression data on KEGG pathways.

Techniques: Quantitative Proteomics, Software