ArticleComputational and structural biotechnology journal2020
MaREA4Galaxy: Metabolic reaction enrichment analysis and visualization of RNA-seq data within Galaxy.
Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Ferroptosis-induced oxidative stress in therapy-resistant glioblastoma.Cell death discovery · 2026Article
- FGFR3 oncogenic activation drives oxidative metabolic reprogramming in bladder cancer: a systems metabolomics approach.Communications biology · 2026Article
- Lung adeno-squamous carcinoma modeling using oncogenic KRAS activation in human embryonic stem cell-derived alveolar organoids.Genes & diseases · 2023Article
- Comparative Transcriptomic Analyses of a Vero Cell Line in Suspension versus Adherent Culture Conditions.International journal of cell biology · 2023Article
- Host metabolism dysregulation and cell tropism identification in human airway and alveolar organoids upon SARS-CoV-2 infection.Protein & cell · 2021Article
- On the Use of Topological Features of Metabolic Networks for the Classification of Cancer Samples.Current genomics · 2021Article
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10 authors.
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Abstract
We present MaREA4Galaxy, a user-friendly tool that allows a user to characterize and to graphically compare groups of samples with different transcriptional regulation of metabolism, as estimated from cross-sectional RNA-seq data. The tool is available as plug-in for the widely-used Galaxy platform for comparative genomics and bioinformatics analyses. MaREA4Galaxy combines three modules. The Expression2RAS module, which, for each reaction of a specified set, computes a Reaction Activity Score (RAS) as a function of the expression level of genes encoding for the associated enzyme. The MaREA (Metabolic Reaction Enrichment Analysis) module that allows to highlight significant differences in reaction activities between specified groups of samples. The Clustering module which employs the RAS computed before as a metric for unsupervised clustering of samples into distinct metabolic subgroups; the Clustering tool provides different clustering techniques and implements standard methods to evaluate the goodness of the results.
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