Dataset Information

MetaboRank: network-based recommendation system to interpret and enrich metabolomics results.

ABSTRACT:

Motivation

Metabolomics has shown great potential to improve the understanding of complex diseases, potentially leading to therapeutic target identification. However, no single analytical method allows monitoring all metabolites in a sample, resulting in incomplete metabolic fingerprints. This incompleteness constitutes a stumbling block to interpretation, raising the need for methods that can enrich those fingerprints. We propose MetaboRank, a new solution inspired by social network recommendation systems for the identification of metabolites potentially related to a metabolic fingerprint.

Results

MetaboRank method had been used to enrich metabolomics data obtained on cerebrospinal fluid samples from patients suffering from hepatic encephalopathy (HE). MetaboRank successfully recommended metabolites not present in the original fingerprint. The quality of recommendations was evaluated by using literature automatic search, in order to check that recommended metabolites could be related to the disease. Complementary mass spectrometry experiments and raw data analysis were performed to confirm these suggestions. In particular, MetaboRank recommended the overlooked ?-ketoglutaramate as a metabolite which should be added to the metabolic fingerprint of HE, thus suggesting that metabolic fingerprints enhancement can provide new insight on complex diseases.

Availability and implementation

Method is implemented in the MetExplore server and is available at www.metexplore.fr. A tutorial is available at https://metexplore.toulouse.inra.fr/com/tutorials/MetaboRank/2017-MetaboRank.pdf.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Frainay C

PROVIDER: S-EPMC6330003 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

MetaboRank: network-based recommendation system to interpret and enrich metabolomics results.

Frainay Clément C Aros Sandrine S Chazalviel Maxime M Garcia Thomas T Vinson Florence F Weiss Nicolas N Colsch Benoit B Sedel Frédéric F Thabut Dominique D Junot Christophe C Jourdan Fabien F

Bioinformatics (Oxford, England) 20190101 2

<h4>Motivation</h4>Metabolomics has shown great potential to improve the understanding of complex diseases, potentially leading to therapeutic target identification. However, no single analytical method allows monitoring all metabolites in a sample, resulting in incomplete metabolic fingerprints. This incompleteness constitutes a stumbling block to interpretation, raising the need for methods that can enrich those fingerprints. We propose MetaboRank, a new solution inspired by social network rec ...[more]

PMID: 29982278

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Dataset Information

MetaboRank: network-based recommendation system to interpret and enrich metabolomics results.

Motivation

Results

Availability and implementation

Supplementary information

Publications

MetaboRank: network-based recommendation system to interpret and enrich metabolomics results.

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