MetWork: a web server for natural products anticipation.


Journal

Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944

Informations de publication

Date de publication:
15 05 2019
Historique:
received: 23 07 2018
revised: 25 09 2018
accepted: 04 10 2018
pubmed: 9 10 2018
medline: 11 6 2020
entrez: 9 10 2018
Statut: ppublish

Résumé

The annotation of natural products and more generally small molecules is one of the major drawbacks in untargeted mass spectrometry analysis. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments. Despite the great potential of this tool, the annotation is usually performed manually by the expert as only few spectral libraries are available. Herein we propose a web server of in silico metabolization of metabolites that represents a full implementation of the metabolome consistency concept. The workflow is based on MS/MS data, organized in molecular network using the Global Natural Products Social Molecular Networking (GNPS) platform, a collaborative library of reactions and a MS/MS spectra prediction module. Having one node identified in the molecular network, the server generates putative structures and predict the associated MS/MS spectra when the exact mass is detected in the network. A similarity comparison between the MS/MS spectra is then performed in order to annotate the node. The web server is available at: https://metwork.pharmacie.parisdescartes.fr.

Identifiants

pubmed: 30295702
pii: 5116145
doi: 10.1093/bioinformatics/bty864
doi:

Substances chimiques

Biological Products 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1795-1796

Informations de copyright

© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Auteurs

Yann Beauxis (Y)

C-TAC UMR CNRS 8638 COMETE, Faculté de Pharmacie de Paris, Université Paris Descartes, Paris, France.

Grégory Genta-Jouve (G)

C-TAC UMR CNRS 8638 COMETE, Faculté de Pharmacie de Paris, Université Paris Descartes, Paris, France.

Articles similaires

Selecting optimal software code descriptors-The case of Java.

Yegor Bugayenko, Zamira Kholmatova, Artem Kruglov et al.
1.00
Software Algorithms Programming Languages

Exploring blood-brain barrier passage using atomic weighted vector and machine learning.

Yoan Martínez-López, Paulina Phoobane, Yanaima Jauriga et al.
1.00
Blood-Brain Barrier Machine Learning Humans Support Vector Machine Software
Humans Meta-Analysis as Topic Sample Size Models, Statistical Computer Simulation
Cephalometry Humans Anatomic Landmarks Software Internet

Classifications MeSH