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Bioinformatics. 2011 Apr 15;27(8):1108-12. doi: 10.1093/bioinformatics/btr079. Epub 2011 Feb 16.

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

Author information

  • 1School of Biomedicine, The University of Manchester, Manchester M13 9PT, UK.

Erratum in

  • Bioinformatics. 2012 Jan 1;28(1):149.

Abstract

MOTIVATION:

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.

RESULTS:

Three workflows have been developed to allow for the rapid, automated and high-throughput annotation and putative metabolite identification of electrospray LC-MS-derived metabolomic datasets. The collection of workflows are defined as PUTMEDID_LCMS and perform feature annotation, matching of accurate m/z to the accurate mass of neutral molecules and associated molecular formula and matching of the molecular formulae to a reference file of metabolites. The software is independent of the instrument and data pre-processing applied. The number of false positives is reduced by eliminating the inaccurate matching of many artifact, isotope, multiply charged and complex adduct peaks through complex interrogation of experimental data.

AVAILABILITY:

The workflows, standard operating procedure and further information are publicly available at http://www.mcisb.org/resources/putmedid.html.

CONTACT:

warwick.dunn@manchester.ac.uk.

PMID:
21325300
[PubMed - indexed for MEDLINE]
PMCID:
PMC3709197
Free PMC Article
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