Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets

Marie Brown; David C Wedge; Royston Goodacre; Douglas B Kell; Philip N Baker; Louise C Kenny; Mamas A Mamas; Ludwig Neyses; Warwick B Dunn

doi:10.1093/bioinformatics/btr079

Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets

Marie Brown, David C Wedge, Royston Goodacre, Douglas B Kell, Philip N Baker, Louise C Kenny, Mamas A Mamas, Ludwig Neyses, Warwick B Dunn

Biosciences

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

145 Citations (Scopus)

Abstract

The study of metabolites (metabolomics) is increasingly being applied to investigate microbial, plant, environmental and mammalian systems. One of the limiting factors is that of chemically identifying metabolites from mass spectrometric signals present in complex datasets.

Original language	English
Pages (from-to)	1108-12
Number of pages	5
Journal	Bioinformatics
Volume	27
Issue number	8
DOIs	https://doi.org/10.1093/bioinformatics/btr079
Publication status	Published - 15 Apr 2011

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title = "Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets",

abstract = "The study of metabolites (metabolomics) is increasingly being applied to investigate microbial, plant, environmental and mammalian systems. One of the limiting factors is that of chemically identifying metabolites from mass spectrometric signals present in complex datasets.",

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AU - Baker, Philip N

AU - Kenny, Louise C

AU - Mamas, Mamas A

AU - Neyses, Ludwig

AU - Dunn, Warwick B

PY - 2011/4/15

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N2 - The study of metabolites (metabolomics) is increasingly being applied to investigate microbial, plant, environmental and mammalian systems. One of the limiting factors is that of chemically identifying metabolites from mass spectrometric signals present in complex datasets.

AB - The study of metabolites (metabolomics) is increasingly being applied to investigate microbial, plant, environmental and mammalian systems. One of the limiting factors is that of chemically identifying metabolites from mass spectrometric signals present in complex datasets.

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DO - 10.1093/bioinformatics/btr079

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JO - Bioinformatics

JF - Bioinformatics

IS - 8

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Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets

Abstract

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