Pathway Activity Profiling (PAPi) is a method developed to correlate levels of metabolites to the activity of metabolic pathways operating within biological systems. Based solely on a metabolomics data set and the Kyoto Encyclopedia of Genes and Genomes, PAPi predicts and compares the activity of metabolic pathways across experimental conditions, which considerably improves the hypothesis generation process for achieving the biological interpretation of biological studies. In this chapter, we describe how to apply PAPi to a metabolomics data set using the R-software.
Metabolic pathway activity Metabolomics and systems biology
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Cakir T et al (2006) Integration of metabolome data with metabolic networks reveals reporter reactions. Mol Syst Biol 2:50CrossRefGoogle Scholar
Kopka J et al (2005) GMD@CSB.DB: the Golm Metabolome Database. Bioinformatics 21:1635–1638CrossRefGoogle Scholar
Ogata H et al (1999) KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res 27:29–34CrossRefGoogle Scholar
Arita M (2004) Computational resources for metabolomics. Brief Funct Genomic Proteomic 3:84–93CrossRefGoogle Scholar
R_Development_Core_Team (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
Tenenbaum, D (2013) KEGGREST: Client-side REST access to KEGG.Google Scholar
Aggio RBM, Ruggiero K, Villas-Boas SG (2010) Pathway Activity Profiling (PAPi): from the metabolite profile to the metabolic pathway activity. Bioinformatics 26:2969–2976CrossRefGoogle Scholar