Evidence map›Paper›PMID 26909353›Full record

ArticleFrontiers in molecular biosciences2016

A Computational Solution to Automatically Map Metabolite Libraries in the Context of Genome Scale Metabolic Networks.

Benjamin Merlet, Nils Paulhe, Florence Vinson, Clément Frainay, Maxime Chazalviel, Nathalie Poupin, Yoann Gloaguen, Franck Giacomoni, Fabien Jourdan

Abstract read
In one paragraph

Article in Frontiers in molecular biosciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Benjamin MerletTOXALIM (Research Centre in Food Toxicology), Institut National de la Recherche Agronomique, UMR1331, Université de Toulouse Toulouse, France.
Nils PaulheNutrition Humaine, Plateforme d'Exploration du Métabolisme, Institut National de la Recherche Agronomique, Centre Clermont-Ferrand-Theix, UMR 1019 Saint-Genès-Champanelle, France.
Florence VinsonTOXALIM (Research Centre in Food Toxicology), Institut National de la Recherche Agronomique, UMR1331, Université de Toulouse Toulouse, France.
Clément FrainayTOXALIM (Research Centre in Food Toxicology), Institut National de la Recherche Agronomique, UMR1331, Université de Toulouse Toulouse, France.
Maxime ChazalvielTOXALIM (Research Centre in Food Toxicology), Institut National de la Recherche Agronomique, UMR1331, Université de Toulouse Toulouse, France.
Nathalie PoupinTOXALIM (Research Centre in Food Toxicology), Institut National de la Recherche Agronomique, UMR1331, Université de Toulouse Toulouse, France.
Yoann GloaguenGlasgow Polyomics, College of Medical, Veterinary and Life Sciences, University of Glasgow Glasgow, UK.
Franck GiacomoniNutrition Humaine, Plateforme d'Exploration du Métabolisme, Institut National de la Recherche Agronomique, Centre Clermont-Ferrand-Theix, UMR 1019 Saint-Genès-Champanelle, France.
Fabien JourdanTOXALIM (Research Centre in Food Toxicology), Institut National de la Recherche Agronomique, UMR1331, Université de Toulouse Toulouse, France.

Funding

Wellcome Trust
6 · The paper itself

Abstract

This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc) and flat file formats (SBML and Matlab files). We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics) and Glasgow Polyomics (GP) on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks. In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks. In order to achieve this goal, we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities.

Indexed as

chemical librarymetabolic networksmetabolome mappingSaaS (Software As A Service)web services

Identifiers

PMID26909353
PMCPMC4754433

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.