Metabolomics

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FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry imaging MS (MALDI imaging assay)


ABSTRACT: High-mass-resolution imaging mass spectrometry promises to localize hundreds of metabolites directly from tissues, cell cultures, and agar plates with cellular resolution, but is hampered by the lack of bioinformatics for automated metabolite identification. We developed the first bioinformatics framework for False Discovery Rate (FDR)-controlled metabolite annotation for high-mass-resolution imaging mass spectrometry (https://github.com/alexandrovteam/pySM) introducing a Metabolite-Signal Match (MSM) score and a target-decoy FDR-estimate for spatial metabolomics. MALDI-FTICR datasets acquired from wild type adult mouse brain were used for the development of spatial metabolomics annotation bioinformatics. This study together with MTBLS317 and MTBLS378 provide the accompanying data for FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry.

INSTRUMENT(S): solarix

SUBMITTER: Andrew Palmer 

PROVIDER: MTBLS313 | MetaboLights | 2017-04-10

REPOSITORIES: MetaboLights

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Publications


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  ...[more]

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