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PLoS One. 2017 Dec 6;12(12):e0189012. doi: 10.1371/journal.pone.0189012. eCollection 2017.

Null diffusion-based enrichment for metabolomics data.

Author information

1
Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, Spain.
2
Networking Biomedical Research Centre in the subject area of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.
3
Institut de Recerca Pediàtrica Hospital Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain.
4
Takeda Cambridge Ltd, Cambridge, United Kingdom.
5
Centre for Omic Sciences, Rovira i Virgili University, Reus, Spain.
6
Department of Electronic Engineering, Rovira i Virgili University, Tarragona, Spain.
7
Metabolomics Platform, Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders, Madrid, Spain.
8
Institute for Research in Biomedicine, Barcelona Institute of Science and Technology, Barcelona, Spain.

Abstract

Metabolomics experiments identify metabolites whose abundance varies as the conditions under study change. Pathway enrichment tools help in the identification of key metabolic processes and in building a plausible biological explanation for these variations. Although several methods are available for pathway enrichment using experimental evidence, metabolomics does not yet have a comprehensive overview in a network layout at multiple molecular levels. We propose a novel pathway enrichment procedure for analysing summary metabolomics data based on sub-network analysis in a graph representation of a reference database. Relevant entries are extracted from the database according to statistical measures over a null diffusive process that accounts for network topology and pathway crosstalk. Entries are reported as a sub-pathway network, including not only pathways, but also modules, enzymes, reactions and possibly other compound candidates for further analyses. This provides a richer biological context, suitable for generating new study hypotheses and potential enzymatic targets. Using this method, we report results from cells depleted for an uncharacterised mitochondrial gene using GC and LC-MS data and employing KEGG as a knowledge base. Partial validation is provided with NMR-based tracking of 13C glucose labelling of these cells.

PMID:
29211807
PMCID:
PMC5718512
DOI:
10.1371/journal.pone.0189012
[Indexed for MEDLINE]
Free PMC Article

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