Studies about the metabolic alterations during tumorigenesis have increased our knowledge of the underlying mechanisms and consequences, which are important for diagnostic and therapeutic investigations. In this scenario and in the era of systems biology, metabolic networks have become a powerful tool to unravel the complexity of the cancer metabolic machinery and the heterogeneity of this disease. Here, we present TumorMet, a repository of tumor metabolic networks extracted from context-specific Genome-Scale Metabolic Models, as a benchmark for graph machine learning algorithms and network analyses. This repository has an extended scope for use in graph classification, clustering, community detection, and graph embedding studies. Along with the data, we developed and provided Met2Graph, an R package for creating three different types of metabolic graphs, depending on the desired nodes and edges: Metabolites-, Enzymes-, and Reactions-based graphs. This package allows the easy generation of datasets for downstream analysis.
© 2022. The Author(s).