Fig. 3: Metaboverse identifies signatures of mitochondrial dysfunction from multi-omics data in yeast.
From: Metaboverse enables automated discovery and visualization of diverse metabolic regulatory patterns
a, Concept map of the study analysed by Metaboverse. Yeast were pre-incubated in S-glucose media and then grown over a time course in either S-glucose or S-raffinose media. Samples were analysed using RNA-seq (n = 4), proteomics (n = 3) and metabolomics (n = 6, except for the 3 h WT group, where n = 5) (subpanel created with BioRender.com). b–d, Reaction pattern catalysed by Leu5 (b) and Ctp1 (c and d) using late (12 h) RNA-seq, proteomics and metabolomics data (b and c) or early (3 h) RNA-seq and metabolomics data (d). Reaction pattern graph values are shown as node shading, where an increasingly blue shade indicates decreased abundance compared with WT, and an increasingly red shade indicates increased abundance compared with WT. Measured \({\log }_{2}\) (fold change) and statistical values for each entity are displayed below the node name. e, Log2 fold changes of citrate, malate and Ctp1 across timepoints. Benjamini–Hochberg corrected P values were used for proteomics and metabolomics data and false discovery rate was used for RNA-seq data. Source numerical data are available at ref. 55.
