Metabolomics

Dataset Information

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FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry imaging MS (LC-MS 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. LC-MS(/MS) 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): QExactive Plus

SUBMITTER: Andrew Palmer 

PROVIDER: MTBLS378 | MetaboLights | 2016-12-05

REPOSITORIES: MetaboLights

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Dataset's files

Source:
Action DRS
MTBLS378 Other
FILES Other
a_MTBLS378_fdrc-lc-ms_metabolite_profiling_mass_spectrometry.txt Txt
i_Investigation.txt Txt
m_MTBLS378_fdrc-lc-ms_metabolite_profiling_mass_spectrometry_v2_maf.tsv Tabular
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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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