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An alternative approach for the interpretation of omics data (e.g., differentially expressed genes) that captures the simplicity of enrichment analyses, while providing deeper mechanistic insights into how differential expression impacts specific cellular functions. This approach enables the investigation of hundreds of metabolic tasks curated from literature covering 7 major metabolic activities of a cell (energy generation, nucleotide, carbohydrate, amino acid, lipid, vitamin & cofactor and glycan metabolism) and 4 mammalian organisms (human, rat, mouse and CHO cells). This platform can be used to predict the activity of these metabolic functions from transcriptomic data to comprehensively quantify the propensity of a cell line or tissue to express a metabolic function.
See below the content of the wiki :
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An explanation of the computation framework of the metabolic task scores
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An explanation on the threshold definition used in the computation framework
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Information about the curation process of the metabolic tasks
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A description of the genome-scale models available to compute the metabolic tasks
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A tutorial of the GenePattern Module with a description of input parameters and output results
We welcome any comments, bug reports, and feature requests. Please send all feedback to [email protected]
For issues with the GenePattern module, please contact [email protected]