Metexplore
High-throughput metabolomic experiments aim at identifying and ultimately quantifying all metabolites present in biological systems. The metabolites are interconnected through metabolic reactions, generally grouped into metabolic pathways. Classical metabolic maps provide a relational context to help interpret metabolomics experiments and a wide range of tools have been developed to help place metabolites within metabolic pathways. However, the representation of metabolites within separate disconnected pathways overlooks most of the connectivity of the metabolome. By definition, reference pathways cannot integrate novel pathways nor show relationships between metabolites that may be linked by common neighbours without being considered as joint members of a classical biochemical pathway. MetExplore is a web server that offers the possibility to link metabolites identified in untargeted metabolomics experiments within the context of genome-scale reconstructed metabolic networks. The analysis pipeline comprises mapping metabolomics data onto the specific metabolic network of an organism, then applying graph-based methods and advanced visualization tools to enhance data analysis.
Publication:
Ludovic Cottret, David Wildridge, Florence Vinson, Michael P. Barrett, Huber Charles, Marie-France Sagot et Fabien Jourdan. MetExplore: a web server to link metabolomic experiments and genome-scale metabolic networks. (2010) Nucleic Acids Research. 1;38 Suppl:W132-7
Some articles using MetExplore:
Bakalov V, Amathieu R, Triba M, et al. Metabolomics with Nuclear Magnetic Resonance Spectroscopy in a Drosophila melanogaster Model of Surviving Sepsis. Metabolites 2016
Peyraud R, Cottret L, Marmiesse L, et al. A Resource Allocation Trade-Off between Virulence and Proliferation Drives Metabolic Versatility in the Plant Pathogen Ralstonia solanacearum. PLOS Pathog. 2016; 12:e1005939
Zalko D, Soto AM, Canlet C, et al. Bisphenol A Exposure Disrupts Neurotransmitters Through Modulation of Transaminase Activity in the Brain of Rodents. Endocrinology 2016; 157:1736–9
Julien-Laferrière A, Bulteau L, Parrot D, et al. A Combinatorial Algorithm for Microbial Consortia Synthetic Design. Sci. Rep. 2016; 6:29182
Shameer S, Logan-Klumpler FJ, Vinson F, et al. TrypanoCyc: a community-led biochemical pathways database for Trypanosoma brucei. Nucleic Acids Res. 2015; 43:D637–44