FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry

被引：0

作者：

Palmer A. ^{[1
]}

Phapale P. ^{[1
]}

Chernyavsky I. ^{[2
]}

Lavigne R. ^{[3
,4
]}

Fay D. ^{[1
]}

Tarasov A. ^{[1
]}

Kovalev V. ^{[1
]}

Fuchser J. ^{[5
]}

Nikolenko S. ^{[1
,6
,7
]}

Pineau C. ^{[3
,4
]}

Becker M. ^{[5
]}

Alexandrov T. ^{[1
,8
,9
]}

机构：

[1] Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg

[2] Center for Industrial Mathematics, University of Bremen, Bremen

[3] Protim, Inserm U1085, IRSET, Rennes

[4] Université de Rennes i, Rennes

[5] Bruker Daltonik GmbH, Bremen

[6] National Research University Higher School of Economics, St.-Petersburg

[7] Higher Institute of Information Technologies and Information Systems, Kazan Federal University, Kazan

[8] SCiLS GmbH, Bremen

[9] Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, San Diego, CA

来源：

Nature Methods | 2017年 / 14卷 / 1期

基金：

欧盟地平线“2020”;

关键词：

D O I：

10.1038/nmeth.4072

中图分类号：

学科分类号：

摘要：

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.

引用

页码：57 / 60

页数：3

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