Explanatory signal interpretation and metabolite identification strategies for nominal mass FIE-MS metabolite fingerprints

David Patrick Overy; David Pierre Louis Enot; Kathleen Tailliart; Helen Ann Jenkins; David A Parker; Manfred Beckmann; John Draper

doi:10.1038/nprot.2007.512

Explanatory signal interpretation and metabolite identification strategies for nominal mass FIE-MS metabolite fingerprints

David Patrick Overy, David Pierre Louis Enot, Kathleen Tailliart, Helen Ann Jenkins, David A Parker, Manfred Beckmann, John Draper

Research output: Contribution to journal › Article › peer-review

42 Citations (Scopus)

Abstract

Flow injection electrospray mass spectrometry (FIE-MS) metabolite fingerprinting is widely used as a 'first pass' screen for compositional differences, where discrimination between samples can be achieved without any preconceptions. Powerful data analysis algorithms can be used to select and rank FIE-MS fingerprint variables highly explanatory of the biological problem under investigation. We describe how to create a species-specific FIE-MS/MSn metabolite database and how to then query the database to predict the identity of highly significant variables within FIE-MS fingerprints. The protocol details how to interpret m/z signals within the explanatory variable list based on a correlation analysis in conjunction with an investigation of mathematical relationships regarding (de) protonated molecular ions, salt adducts, neutral losses and dimeric associations routinely observed in FIE-MS fingerprints. Although designed for use by biologists/analytical chemists, collaboration with data-mining experts is generally advised. The protocol is applicable in any areas of bioscience research involving FIE-MS fingerprinting

Original language	English
Pages (from-to)	471-485
Number of pages	15
Journal	Nature Protocols
Volume	3
DOIs	https://doi.org/10.1038/nprot.2007.512
Publication status	Published - 28 Feb 2008

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title = "Explanatory signal interpretation and metabolite identification strategies for nominal mass FIE-MS metabolite fingerprints",

abstract = "Flow injection electrospray mass spectrometry (FIE-MS) metabolite fingerprinting is widely used as a 'first pass' screen for compositional differences, where discrimination between samples can be achieved without any preconceptions. Powerful data analysis algorithms can be used to select and rank FIE-MS fingerprint variables highly explanatory of the biological problem under investigation. We describe how to create a species-specific FIE-MS/MSn metabolite database and how to then query the database to predict the identity of highly significant variables within FIE-MS fingerprints. The protocol details how to interpret m/z signals within the explanatory variable list based on a correlation analysis in conjunction with an investigation of mathematical relationships regarding (de) protonated molecular ions, salt adducts, neutral losses and dimeric associations routinely observed in FIE-MS fingerprints. Although designed for use by biologists/analytical chemists, collaboration with data-mining experts is generally advised. The protocol is applicable in any areas of bioscience research involving FIE-MS fingerprinting",

author = "Overy, {David Patrick} and Enot, {David Pierre Louis} and Kathleen Tailliart and Jenkins, {Helen Ann} and Parker, {David A} and Manfred Beckmann and John Draper",

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AU - Jenkins, Helen Ann

AU - Parker, David A

AU - Beckmann, Manfred

AU - Draper, John

N1 - Overy, D. P., Enot, D. P., Tailliart, K., Jenkins, H., Parker, D., Beckmann, M., Draper, J. (2008). Explanatory signal interpretation and metabolite identification strategies for nominal mass FIE-MS metabolite fingerprints. Nature Protocols, 3, (3), 471-485. IMPF: 04.17 RONO: 00

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N2 - Flow injection electrospray mass spectrometry (FIE-MS) metabolite fingerprinting is widely used as a 'first pass' screen for compositional differences, where discrimination between samples can be achieved without any preconceptions. Powerful data analysis algorithms can be used to select and rank FIE-MS fingerprint variables highly explanatory of the biological problem under investigation. We describe how to create a species-specific FIE-MS/MSn metabolite database and how to then query the database to predict the identity of highly significant variables within FIE-MS fingerprints. The protocol details how to interpret m/z signals within the explanatory variable list based on a correlation analysis in conjunction with an investigation of mathematical relationships regarding (de) protonated molecular ions, salt adducts, neutral losses and dimeric associations routinely observed in FIE-MS fingerprints. Although designed for use by biologists/analytical chemists, collaboration with data-mining experts is generally advised. The protocol is applicable in any areas of bioscience research involving FIE-MS fingerprinting

AB - Flow injection electrospray mass spectrometry (FIE-MS) metabolite fingerprinting is widely used as a 'first pass' screen for compositional differences, where discrimination between samples can be achieved without any preconceptions. Powerful data analysis algorithms can be used to select and rank FIE-MS fingerprint variables highly explanatory of the biological problem under investigation. We describe how to create a species-specific FIE-MS/MSn metabolite database and how to then query the database to predict the identity of highly significant variables within FIE-MS fingerprints. The protocol details how to interpret m/z signals within the explanatory variable list based on a correlation analysis in conjunction with an investigation of mathematical relationships regarding (de) protonated molecular ions, salt adducts, neutral losses and dimeric associations routinely observed in FIE-MS fingerprints. Although designed for use by biologists/analytical chemists, collaboration with data-mining experts is generally advised. The protocol is applicable in any areas of bioscience research involving FIE-MS fingerprinting

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JF - Nature Protocols

ER -

Explanatory signal interpretation and metabolite identification strategies for nominal mass FIE-MS metabolite fingerprints

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