Cargando…
Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets
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: Thre...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2011
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3709197/ https://www.ncbi.nlm.nih.gov/pubmed/21325300 http://dx.doi.org/10.1093/bioinformatics/btr079 |
_version_ | 1782276721493409792 |
---|---|
author | Brown, Marie Wedge, David C. Goodacre, Royston Kell, Douglas B. Baker, Philip N. Kenny, Louise C. Mamas, Mamas A. Neyses, Ludwig Dunn, Warwick B. |
author_facet | Brown, Marie Wedge, David C. Goodacre, Royston Kell, Douglas B. Baker, Philip N. Kenny, Louise C. Mamas, Mamas A. Neyses, Ludwig Dunn, Warwick B. |
author_sort | Brown, Marie |
collection | PubMed |
description | 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 |
format | Online Article Text |
id | pubmed-3709197 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-37091972013-07-12 Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets Brown, Marie Wedge, David C. Goodacre, Royston Kell, Douglas B. Baker, Philip N. Kenny, Louise C. Mamas, Mamas A. Neyses, Ludwig Dunn, Warwick B. Bioinformatics Original Papers 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 Oxford University Press 2011-04-15 2011-02-16 /pmc/articles/PMC3709197/ /pubmed/21325300 http://dx.doi.org/10.1093/bioinformatics/btr079 Text en © The Author 2011. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc/3.0/) which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Papers Brown, Marie Wedge, David C. Goodacre, Royston Kell, Douglas B. Baker, Philip N. Kenny, Louise C. Mamas, Mamas A. Neyses, Ludwig Dunn, Warwick B. Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets |
title | Automated workflows for accurate mass-based putative metabolite
identification in LC/MS-derived metabolomic datasets |
title_full | Automated workflows for accurate mass-based putative metabolite
identification in LC/MS-derived metabolomic datasets |
title_fullStr | Automated workflows for accurate mass-based putative metabolite
identification in LC/MS-derived metabolomic datasets |
title_full_unstemmed | Automated workflows for accurate mass-based putative metabolite
identification in LC/MS-derived metabolomic datasets |
title_short | Automated workflows for accurate mass-based putative metabolite
identification in LC/MS-derived metabolomic datasets |
title_sort | automated workflows for accurate mass-based putative metabolite
identification in lc/ms-derived metabolomic datasets |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3709197/ https://www.ncbi.nlm.nih.gov/pubmed/21325300 http://dx.doi.org/10.1093/bioinformatics/btr079 |
work_keys_str_mv | AT brownmarie automatedworkflowsforaccuratemassbasedputativemetaboliteidentificationinlcmsderivedmetabolomicdatasets AT wedgedavidc automatedworkflowsforaccuratemassbasedputativemetaboliteidentificationinlcmsderivedmetabolomicdatasets AT goodacreroyston automatedworkflowsforaccuratemassbasedputativemetaboliteidentificationinlcmsderivedmetabolomicdatasets AT kelldouglasb automatedworkflowsforaccuratemassbasedputativemetaboliteidentificationinlcmsderivedmetabolomicdatasets AT bakerphilipn automatedworkflowsforaccuratemassbasedputativemetaboliteidentificationinlcmsderivedmetabolomicdatasets AT kennylouisec automatedworkflowsforaccuratemassbasedputativemetaboliteidentificationinlcmsderivedmetabolomicdatasets AT mamasmamasa automatedworkflowsforaccuratemassbasedputativemetaboliteidentificationinlcmsderivedmetabolomicdatasets AT neysesludwig automatedworkflowsforaccuratemassbasedputativemetaboliteidentificationinlcmsderivedmetabolomicdatasets AT dunnwarwickb automatedworkflowsforaccuratemassbasedputativemetaboliteidentificationinlcmsderivedmetabolomicdatasets |