Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
Center for Industrial Mathematics, University of Bremen, Bremen, Germany.
Nat Methods. 2017 Jan;14(1):57-60. doi: 10.1038/nmeth.4072. Epub 2016 Nov 14.
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.
高分辨率成像质谱有望以细胞分辨率定位组织、细胞培养物和琼脂平板中的数百种代谢物,但由于缺乏用于自动代谢物鉴定的生物信息学工具而受到阻碍。我们报告了 pySM,这是一种用于分子总和公式水平的错误发现率 (FDR) 控制代谢物注释的框架,用于高分辨率成像质谱 (https://github.com/alexandrovteam/pySM)。我们引入了代谢物信号匹配分数和空间代谢组学的目标诱饵 FDR 估计。