Baitmet, a computational approach for GC–MS library-driven metabolite profiling

Current computational tools for gas chromatography – mass spectrometry (GC – MS) metabolomics profiling do not focus on metabolite identification, that still remains as the entire workflow bottleneck and it relies on manual d ata reviewing. Metabolomics ad vent has fostered the development of public...

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Detalles Bibliográficos
Autores: Domingo, Xavier, Brezmes, Jesus, Venturini, G, Vivó-Truyols, Gabriel, Perera Lluna, Alexandre|||0000-0001-6427-851X, Vinaixa, Maria
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/105910
Acceso en línea:https://hdl.handle.net/2117/105910
https://dx.doi.org/10.1007/s11306-017-1223-x
Access Level:acceso abierto
Palabra clave:Gas chromatography
Mass spectrometry
Compound profiling
Gas
chromatography
Metabolomics
Cromatografia de gasos
Espectrofotometria
Àrees temàtiques de la UPC::Informàtica
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oai_identifier_str oai:upcommons.upc.edu:2117/105910
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repository_id_str
spelling Baitmet, a computational approach for GC–MS library-driven metabolite profilingDomingo, XavierBrezmes, JesusVenturini, GVivó-Truyols, GabrielPerera Lluna, Alexandre|||0000-0001-6427-851XVinaixa, MariaGas chromatographyMass spectrometryCompound profilingGaschromatographyMass spectrometryMetabolomicsCromatografia de gasosEspectrofotometriaÀrees temàtiques de la UPC::InformàticaCurrent computational tools for gas chromatography – mass spectrometry (GC – MS) metabolomics profiling do not focus on metabolite identification, that still remains as the entire workflow bottleneck and it relies on manual d ata reviewing. Metabolomics ad vent has fostered the development of public metabolite repositories containing mass spectra and retentio n indices, two orthogonal prop erties needed for metabol ite identification. Such libraries can be used for library - driven compound profiling of large datasets produced in metabolomics, a complementary approach to current GC – MS non - targeted data analysis solutions that can eventually help to assess metabolite i dentities more efficiently. Results: This paper introduces Baitmet, an integrated open - source computational tool written in R enclosing a complete workflow to perform high - throughput library - driven GC – MS profiling in complex samples. Baitmet capabilities w ere assa yed in a metabolomics study in volving 182 human serum samples where a set of 61 metabolites were profiled given a reference library. Conclusions: Baitmet allows high - thr oughput and wide scope interro gation on the metabolic composition of complex sa mples analyzed using GC – MS via freely available spectral dataPeer Reviewed20172017-06-2420172017-06-28journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/105910https://dx.doi.org/10.1007/s11306-017-1223-xreponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1059102026-05-27T15:37:01Z
dc.title.none.fl_str_mv Baitmet, a computational approach for GC–MS library-driven metabolite profiling
title Baitmet, a computational approach for GC–MS library-driven metabolite profiling
spellingShingle Baitmet, a computational approach for GC–MS library-driven metabolite profiling
Domingo, Xavier
Gas chromatography
Mass spectrometry
Compound profiling
Gas
chromatography
Mass spectrometry
Metabolomics
Cromatografia de gasos
Espectrofotometria
Àrees temàtiques de la UPC::Informàtica
title_short Baitmet, a computational approach for GC–MS library-driven metabolite profiling
title_full Baitmet, a computational approach for GC–MS library-driven metabolite profiling
title_fullStr Baitmet, a computational approach for GC–MS library-driven metabolite profiling
title_full_unstemmed Baitmet, a computational approach for GC–MS library-driven metabolite profiling
title_sort Baitmet, a computational approach for GC–MS library-driven metabolite profiling
dc.creator.none.fl_str_mv Domingo, Xavier
Brezmes, Jesus
Venturini, G
Vivó-Truyols, Gabriel
Perera Lluna, Alexandre|||0000-0001-6427-851X
Vinaixa, Maria
author Domingo, Xavier
author_facet Domingo, Xavier
Brezmes, Jesus
Venturini, G
Vivó-Truyols, Gabriel
Perera Lluna, Alexandre|||0000-0001-6427-851X
Vinaixa, Maria
author_role author
author2 Brezmes, Jesus
Venturini, G
Vivó-Truyols, Gabriel
Perera Lluna, Alexandre|||0000-0001-6427-851X
Vinaixa, Maria
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Gas chromatography
Mass spectrometry
Compound profiling
Gas
chromatography
Mass spectrometry
Metabolomics
Cromatografia de gasos
Espectrofotometria
Àrees temàtiques de la UPC::Informàtica
topic Gas chromatography
Mass spectrometry
Compound profiling
Gas
chromatography
Mass spectrometry
Metabolomics
Cromatografia de gasos
Espectrofotometria
Àrees temàtiques de la UPC::Informàtica
description Current computational tools for gas chromatography – mass spectrometry (GC – MS) metabolomics profiling do not focus on metabolite identification, that still remains as the entire workflow bottleneck and it relies on manual d ata reviewing. Metabolomics ad vent has fostered the development of public metabolite repositories containing mass spectra and retentio n indices, two orthogonal prop erties needed for metabol ite identification. Such libraries can be used for library - driven compound profiling of large datasets produced in metabolomics, a complementary approach to current GC – MS non - targeted data analysis solutions that can eventually help to assess metabolite i dentities more efficiently. Results: This paper introduces Baitmet, an integrated open - source computational tool written in R enclosing a complete workflow to perform high - throughput library - driven GC – MS profiling in complex samples. Baitmet capabilities w ere assa yed in a metabolomics study in volving 182 human serum samples where a set of 61 metabolites were profiled given a reference library. Conclusions: Baitmet allows high - thr oughput and wide scope interro gation on the metabolic composition of complex sa mples analyzed using GC – MS via freely available spectral data
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-06-24
2017
2017-06-28
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/105910
https://dx.doi.org/10.1007/s11306-017-1223-x
url https://hdl.handle.net/2117/105910
https://dx.doi.org/10.1007/s11306-017-1223-x
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,198674

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