Éditeur(s) : HAL CCSD
Résumé : International audience
High-mass-resolution imaging mass spectrometry promises to localize hundreds of metabolites in tissues, cell cultures, and agar plates with cellular resolution, but it is hampered by the lack of bioinformatics tools for automated metabolite identification. We report pySM, a framework for false discovery rate (FDR)-controlled metabolite annotation at the level of the molecular sum formula, for high-mass-resolution imaging mass spectrometry (https://github.com/alexandrovteam/pySM). We introduce a metabolite-signal match score and a target-decoy FDR estimate for spatial metabolomics. © 2017 Nature America, Inc., part of Springer Nature. All rights reserved.
Nature Methods

hal-01467553
https://hal-univ-rennes1.archives-ouvertes.fr/hal-01467553
DOI : 10.1038/nmeth.4072
PUBMED : 27842059