Mining for Peaks in LC-HRMS Datasets Using Finnee - A Case Study with Exhaled Breath Condensates from Healthy, Asthmatic, and COPD Patients

Detalhes bibliográficos
Autor(a) principal: Erny, GL
Data de Publicação: 2020
Outros Autores: Gomes, RA, Santos, MSF, Santos, L, Neuparth, N, Carreiro-Martins, P, Marques, JG, Guerreiro, ACL, Gomes-Alves, P
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.17/3536
Resumo: Separation techniques hyphenated to high-resolution mass spectrometry are essential in untargeted metabolomic analyses. Due to the complexity and size of the resulting data, analysts rely on computer-assisted tools to mine for features that may represent a chromatographic signal. However, this step remains problematic, and a high number of false positives are often obtained. This work reports a novel approach where each step is carefully controlled to decrease the likelihood of errors. Datasets are first corrected for baseline drift and background noise before the MS scans are converted from profile to centroid. A new alignment strategy that includes purity control is introduced, and features are quantified using the original data with scans recorded as profile, not the extracted features. All the algorithms used in this work are part of the Finnee Matlab toolbox that is freely available. The approach was validated using metabolites in exhaled breath condensates to differentiate individuals diagnosed with asthma from patients with chronic obstructive pulmonary disease. With this new pipeline, twice as many markers were found with Finnee in comparison to XCMS-online, and nearly 50% more than with MS-Dial, two of the most popular freeware for untargeted metabolomics analysis.
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spelling Mining for Peaks in LC-HRMS Datasets Using Finnee - A Case Study with Exhaled Breath Condensates from Healthy, Asthmatic, and COPD PatientsSpectrometryAsthmaHDE ALERSeparation techniques hyphenated to high-resolution mass spectrometry are essential in untargeted metabolomic analyses. Due to the complexity and size of the resulting data, analysts rely on computer-assisted tools to mine for features that may represent a chromatographic signal. However, this step remains problematic, and a high number of false positives are often obtained. This work reports a novel approach where each step is carefully controlled to decrease the likelihood of errors. Datasets are first corrected for baseline drift and background noise before the MS scans are converted from profile to centroid. A new alignment strategy that includes purity control is introduced, and features are quantified using the original data with scans recorded as profile, not the extracted features. All the algorithms used in this work are part of the Finnee Matlab toolbox that is freely available. The approach was validated using metabolites in exhaled breath condensates to differentiate individuals diagnosed with asthma from patients with chronic obstructive pulmonary disease. With this new pipeline, twice as many markers were found with Finnee in comparison to XCMS-online, and nearly 50% more than with MS-Dial, two of the most popular freeware for untargeted metabolomics analysis.American Chemical Society PublicationsRepositório do Centro Hospitalar Universitário de Lisboa Central, EPEErny, GLGomes, RASantos, MSFSantos, LNeuparth, NCarreiro-Martins, PMarques, JGGuerreiro, ACLGomes-Alves, P2021-01-22T11:55:52Z2020-07-072020-07-07T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.17/3536engACS Omega . 2020 Jun 23;5(26):16089-1609810.1021/acsomega.0c01610info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-10T09:43:28Zoai:repositorio.chlc.min-saude.pt:10400.17/3536Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:20:50.831695Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Mining for Peaks in LC-HRMS Datasets Using Finnee - A Case Study with Exhaled Breath Condensates from Healthy, Asthmatic, and COPD Patients
title Mining for Peaks in LC-HRMS Datasets Using Finnee - A Case Study with Exhaled Breath Condensates from Healthy, Asthmatic, and COPD Patients
spellingShingle Mining for Peaks in LC-HRMS Datasets Using Finnee - A Case Study with Exhaled Breath Condensates from Healthy, Asthmatic, and COPD Patients
Erny, GL
Spectrometry
Asthma
HDE ALER
title_short Mining for Peaks in LC-HRMS Datasets Using Finnee - A Case Study with Exhaled Breath Condensates from Healthy, Asthmatic, and COPD Patients
title_full Mining for Peaks in LC-HRMS Datasets Using Finnee - A Case Study with Exhaled Breath Condensates from Healthy, Asthmatic, and COPD Patients
title_fullStr Mining for Peaks in LC-HRMS Datasets Using Finnee - A Case Study with Exhaled Breath Condensates from Healthy, Asthmatic, and COPD Patients
title_full_unstemmed Mining for Peaks in LC-HRMS Datasets Using Finnee - A Case Study with Exhaled Breath Condensates from Healthy, Asthmatic, and COPD Patients
title_sort Mining for Peaks in LC-HRMS Datasets Using Finnee - A Case Study with Exhaled Breath Condensates from Healthy, Asthmatic, and COPD Patients
author Erny, GL
author_facet Erny, GL
Gomes, RA
Santos, MSF
Santos, L
Neuparth, N
Carreiro-Martins, P
Marques, JG
Guerreiro, ACL
Gomes-Alves, P
author_role author
author2 Gomes, RA
Santos, MSF
Santos, L
Neuparth, N
Carreiro-Martins, P
Marques, JG
Guerreiro, ACL
Gomes-Alves, P
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Repositório do Centro Hospitalar Universitário de Lisboa Central, EPE
dc.contributor.author.fl_str_mv Erny, GL
Gomes, RA
Santos, MSF
Santos, L
Neuparth, N
Carreiro-Martins, P
Marques, JG
Guerreiro, ACL
Gomes-Alves, P
dc.subject.por.fl_str_mv Spectrometry
Asthma
HDE ALER
topic Spectrometry
Asthma
HDE ALER
description Separation techniques hyphenated to high-resolution mass spectrometry are essential in untargeted metabolomic analyses. Due to the complexity and size of the resulting data, analysts rely on computer-assisted tools to mine for features that may represent a chromatographic signal. However, this step remains problematic, and a high number of false positives are often obtained. This work reports a novel approach where each step is carefully controlled to decrease the likelihood of errors. Datasets are first corrected for baseline drift and background noise before the MS scans are converted from profile to centroid. A new alignment strategy that includes purity control is introduced, and features are quantified using the original data with scans recorded as profile, not the extracted features. All the algorithms used in this work are part of the Finnee Matlab toolbox that is freely available. The approach was validated using metabolites in exhaled breath condensates to differentiate individuals diagnosed with asthma from patients with chronic obstructive pulmonary disease. With this new pipeline, twice as many markers were found with Finnee in comparison to XCMS-online, and nearly 50% more than with MS-Dial, two of the most popular freeware for untargeted metabolomics analysis.
publishDate 2020
dc.date.none.fl_str_mv 2020-07-07
2020-07-07T00:00:00Z
2021-01-22T11:55:52Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.17/3536
url http://hdl.handle.net/10400.17/3536
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv ACS Omega . 2020 Jun 23;5(26):16089-16098
10.1021/acsomega.0c01610
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv American Chemical Society Publications
publisher.none.fl_str_mv American Chemical Society Publications
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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