22 November 2016

SCiLS contributed to the following publication about automated metabolite annotation in high-mass-resolution imaging mass spectrometry:

Palmer, A.; Phapale, P.; Chernyavsky, I.; Lavigne, R.; Fay, D.; Tarasov, A.; Kovalev, V.; Fuchser, J.; Nikolenko, S.; Pineau, C.; Becker, M. & Alexandrov, T. (2016): “FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry”, Nature Methods, advance online publication.

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 pySMSM, 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. We introduce a metabolite-signal match score and a target–decoy FDR estimate for spatial metabolomics.

For more information, visit the METASPACE2020.eu consortium website or contact the authors of the paper.

New publication about automated metabolite annotation