Mining for peaks in lc-hrms datasets using finnee - a case study with exhaled breath condensates from healthy, asthmatic, and copd patients
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2020 |
| Outros Autores: | , , , , , , , |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Texto Completo: | http://hdl.handle.net/10362/101581 |
Resumo: | This work was financially supported by the projects: (i) UID/ EQU/00511/2019 - Laboratory for Process Engineering, Environment, Biotechnology and Energy − LEPABE funded by national funds through FCT/MCTES (PIDDAC); (ii) POCI-01-0145-FEDER-029702 and POCI-01-0145-FEDER031297 funded by FEDER funds through COMPETE2020 − Programa Operacional Competitividade e Internacionalizaca̧ õ (POCI) and by national funds (PIDDAC) through FCT/ MCTES; (iii) AstraZeneca − Projecto OLDER (CEDOC/ 2015/59); (iv) iNOVA4Health - UID/Multi/04462/2013, financially supported by FCT/Ministerio da Educação e Ciência, and co-funded by FEDER under the PT2020 Partnership Agreement. |
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Mining for peaks in lc-hrms datasets using finnee - a case study with exhaled breath condensates from healthy, asthmatic, and copd patientsChemistry(all)Chemical Engineering(all)This work was financially supported by the projects: (i) UID/ EQU/00511/2019 - Laboratory for Process Engineering, Environment, Biotechnology and Energy − LEPABE funded by national funds through FCT/MCTES (PIDDAC); (ii) POCI-01-0145-FEDER-029702 and POCI-01-0145-FEDER031297 funded by FEDER funds through COMPETE2020 − Programa Operacional Competitividade e Internacionalizaca̧ õ (POCI) and by national funds (PIDDAC) through FCT/ MCTES; (iii) AstraZeneca − Projecto OLDER (CEDOC/ 2015/59); (iv) iNOVA4Health - UID/Multi/04462/2013, financially supported by FCT/Ministerio da Educação e Ciência, and co-funded by FEDER under the PT2020 Partnership Agreement.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.NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)Centro de Estudos de Doenças Crónicas (CEDOC)Comprehensive Health Research Centre (CHRC) - pólo NMSInstituto de Tecnologia Química e Biológica António Xavier (ITQB)RUNErny, Guillaume L.Gomes, Ricardo A.Santos, Mónica S.F.Santos, LúciaNeuparth, NCarreiro-Martins, PedroMarques, João GasparGuerreiro, Ana C.L.Gomes-Alves, Patrícia2020-07-27T22:45:25Z2020-07-072020-07-07T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/101581eng2470-1343PURE: 19218651https://doi.org/10.1021/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:RCAAP2024-03-11T04:47:49Zoai:run.unl.pt:10362/101581Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:39:35.805582Repositório 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, Guillaume L. Chemistry(all) Chemical Engineering(all) |
| 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, Guillaume L. |
| author_facet |
Erny, Guillaume L. Gomes, Ricardo A. Santos, Mónica S.F. Santos, Lúcia Neuparth, N Carreiro-Martins, Pedro Marques, João Gaspar Guerreiro, Ana C.L. Gomes-Alves, Patrícia |
| author_role |
author |
| author2 |
Gomes, Ricardo A. Santos, Mónica S.F. Santos, Lúcia Neuparth, N Carreiro-Martins, Pedro Marques, João Gaspar Guerreiro, Ana C.L. Gomes-Alves, Patrícia |
| author2_role |
author author author author author author author author |
| dc.contributor.none.fl_str_mv |
NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM) Centro de Estudos de Doenças Crónicas (CEDOC) Comprehensive Health Research Centre (CHRC) - pólo NMS Instituto de Tecnologia Química e Biológica António Xavier (ITQB) RUN |
| dc.contributor.author.fl_str_mv |
Erny, Guillaume L. Gomes, Ricardo A. Santos, Mónica S.F. Santos, Lúcia Neuparth, N Carreiro-Martins, Pedro Marques, João Gaspar Guerreiro, Ana C.L. Gomes-Alves, Patrícia |
| dc.subject.por.fl_str_mv |
Chemistry(all) Chemical Engineering(all) |
| topic |
Chemistry(all) Chemical Engineering(all) |
| description |
This work was financially supported by the projects: (i) UID/ EQU/00511/2019 - Laboratory for Process Engineering, Environment, Biotechnology and Energy − LEPABE funded by national funds through FCT/MCTES (PIDDAC); (ii) POCI-01-0145-FEDER-029702 and POCI-01-0145-FEDER031297 funded by FEDER funds through COMPETE2020 − Programa Operacional Competitividade e Internacionalizaca̧ õ (POCI) and by national funds (PIDDAC) through FCT/ MCTES; (iii) AstraZeneca − Projecto OLDER (CEDOC/ 2015/59); (iv) iNOVA4Health - UID/Multi/04462/2013, financially supported by FCT/Ministerio da Educação e Ciência, and co-funded by FEDER under the PT2020 Partnership Agreement. |
| publishDate |
2020 |
| dc.date.none.fl_str_mv |
2020-07-27T22:45:25Z 2020-07-07 2020-07-07T00:00:00Z |
| dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/101581 |
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http://hdl.handle.net/10362/101581 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
2470-1343 PURE: 19218651 https://doi.org/10.1021/acsomega.0c01610 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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