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Figure 1 VarStack 2 uses APIs to retrieve information from COSMIC, ClinVar, cBioPortal, UCSC Genome Browser, ClinicalTrials.gov, and CIViC. An iframe was used for UCSC Genome Browser and ClinVar. VarStack2 uses APIs to retrieve information from COSMIC, ClinVar, cBioPortal, UCSC Genome Browser, ClinicalTr

Figure 1 Histogram of renal clearance values (L/h). For image description, please refer to the figure legend and surrounding text.

Figure 3 Histogram showing the frequency with which a particular route of administration was chosen for a drug. For image description, please refer to the figure legend and surrounding text.

Figure 2 Case study to interpret KRAS p.G12V in Colorectal Adenocarcinoma using VarStack 2 . The variant and tumour type are entered in search window (a) VarStack 2 retrieves information from (b) COSMIC, (c) ClinVar, (d) cBioPortal, (e) UCSC Genome Browser, and (f) ClinicalTrials.gov, (g) CIViC and displays

Figure 2 Categories of health status and their counts. For image description, please refer to the figure legend and surrounding text.

Figure 4 Counts of analytical methods presented in scientific articles. HPLC—high-performance liquid chromatography; LC-MS/MS—liquid chromatography–tandem mass spectrometry. For image description, please refer to the figure legend and surrounding text.

Figure 4 Differential expression and functional enrichment analyses across disease stages. (a, b) Differential expression volcano plot (a) and GO Biological Process enrichment bubble plot (b) for obese vs. normal. (c, d) Differential expression volcano plot (c) and GO Biological Process enrichment bubble plo

Figure 3 Analysis of cell-type composition and rewired cell–cell communication (CCC) ligand–receptor interaction (LRI) pairs between eight major brain cell types in comparative analysis from BDCD. (a) Cell type proportions of sc/snRNA-seq datasets across eight disease conditions, including four subtypes of A

Figure 1 Overview of the MASH-GA dataset composition. (a) Countries contributing human datasets (n  = 17). (b) Dataset counts per country. (c) Year-by-year accrual of studies (2010–2025 H1). (d) Proportion of NGS (n  = 138) versus microarray (n  = 28) datasets. (e) Distribution of sequencing platforms among

Figure 2 Overview of the MASH-GA pipeline and analysis modules. GEO datasets are curated into the MASH-GA database, which is served through an R Shiny web interface. The Human module offers six analytical views, whereas the Mouse module visualizes gene expression across eight NASH models. Two functionally

Figure 1 Overview of data collection, computational analysis, and downstream annotation of BDCD. AD: Alzheimer’s disease. ST: spatial transcriptomics. CCC: cell–cell communication. LRI: ligand–receptor interaction. GWAS: genome-wide association studies. LR: ligand receptor. SNP: single nucleotide polymorphis

Figure 2 Overview of BDCD web interface. BDCD portal offers an intuitive interface to explore brain disease-related cell–cell communication (CCC) data. Users can browse CCC data by brain region (a), dataset (b), or cell type (c). The platform offers interactive views of ligand–receptor interactions (LRI) (d)

Figure 3 Multilevel assessment of COL1A1 expression and coexpression in MASH-GA. (a) COL1A1 increases stepwise from normal controls (NC) to obese and NAFLD and peaks in NASH (human cohort, P  < .001). (b) Across 10 independent datasets, COL1A1 rises with fibrosis stage, F0 → F4. (c) Cross-cohort comparis

Figure 4 Functional characterization and validation of dysregulated LRIs. (a) Distribution of protein class of ligand/receptor genes in sc/snRNA-seq datasets. (b) Distribution of protein class of ligand/receptor genes in spatial transcriptomics datasets. (c) KEGG pathway enrichment of rewired ligand/receptor