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Article: ADAP-GC 3.2: Graphical Software Tool for Efficient Spectral Deconvolution of Gas Chromatography-High-Resolution Mass Spectrometry Metabolomics Data

TitleADAP-GC 3.2: Graphical Software Tool for Efficient Spectral Deconvolution of Gas Chromatography-High-Resolution Mass Spectrometry Metabolomics Data
Authors
Keywordscompound identification
compound quantitation
computational work flow
gas chromatography
high mass resolution
mass spectrometry
metabolomics
software
spectral deconvolution
visualization
Issue Date2018
Citation
Journal of Proteome Research, 2018, v. 17, n. 1, p. 470-478 How to Cite?
AbstractADAP-GC is an automated computational workflow for extracting metabolite information from raw, untargeted gas chromatography-mass spectrometry metabolomics data. Deconvolution of coeluting analytes is a critical step in the workflow, and the underlying algorithm is able to extract fragmentation mass spectra of coeluting analytes with high accuracy. However, its latest version ADAP-GC 3.0 was not user-friendly. To make ADAP-GC easier to use, we have developed ADAP-GC 3.2 and describe here the improvements on three aspects. First, all of the algorithms in ADAP-GC 3.0 written in R have been replaced by their analogues in Java and incorporated into MZmine 2 to make the workflow user-friendly. Second, the clustering algorithm DBSCAN has replaced the original hierarchical clustering to allow faster spectral deconvolution. Finally, algorithms originally developed for constructing extracted ion chromatograms (EICs) and detecting EIC peaks from LC-MS data are incorporated into the ADAP-GC workflow, allowing the latter to process high mass resolution data. Performance of ADAP-GC 3.2 has been evaluated using unit mass resolution data from standard-mixture and urine samples. The identification and quantitation results were compared with those produced by ADAP-GC 3.0, AMDIS, AnalyzerPro, and ChromaTOF. Identification results for high mass resolution data derived from standard-mixture samples are presented as well.
Persistent Identifierhttp://hdl.handle.net/10722/342551
ISSN
2023 Impact Factor: 3.8
2023 SCImago Journal Rankings: 1.299
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorSmirnov, Aleksandr-
dc.contributor.authorJia, Wei-
dc.contributor.authorWalker, Douglas I.-
dc.contributor.authorJones, Dean P.-
dc.contributor.authorDu, Xiuxia-
dc.date.accessioned2024-04-17T07:04:37Z-
dc.date.available2024-04-17T07:04:37Z-
dc.date.issued2018-
dc.identifier.citationJournal of Proteome Research, 2018, v. 17, n. 1, p. 470-478-
dc.identifier.issn1535-3893-
dc.identifier.urihttp://hdl.handle.net/10722/342551-
dc.description.abstractADAP-GC is an automated computational workflow for extracting metabolite information from raw, untargeted gas chromatography-mass spectrometry metabolomics data. Deconvolution of coeluting analytes is a critical step in the workflow, and the underlying algorithm is able to extract fragmentation mass spectra of coeluting analytes with high accuracy. However, its latest version ADAP-GC 3.0 was not user-friendly. To make ADAP-GC easier to use, we have developed ADAP-GC 3.2 and describe here the improvements on three aspects. First, all of the algorithms in ADAP-GC 3.0 written in R have been replaced by their analogues in Java and incorporated into MZmine 2 to make the workflow user-friendly. Second, the clustering algorithm DBSCAN has replaced the original hierarchical clustering to allow faster spectral deconvolution. Finally, algorithms originally developed for constructing extracted ion chromatograms (EICs) and detecting EIC peaks from LC-MS data are incorporated into the ADAP-GC workflow, allowing the latter to process high mass resolution data. Performance of ADAP-GC 3.2 has been evaluated using unit mass resolution data from standard-mixture and urine samples. The identification and quantitation results were compared with those produced by ADAP-GC 3.0, AMDIS, AnalyzerPro, and ChromaTOF. Identification results for high mass resolution data derived from standard-mixture samples are presented as well.-
dc.languageeng-
dc.relation.ispartofJournal of Proteome Research-
dc.subjectcompound identification-
dc.subjectcompound quantitation-
dc.subjectcomputational work flow-
dc.subjectgas chromatography-
dc.subjecthigh mass resolution-
dc.subjectmass spectrometry-
dc.subjectmetabolomics-
dc.subjectsoftware-
dc.subjectspectral deconvolution-
dc.subjectvisualization-
dc.titleADAP-GC 3.2: Graphical Software Tool for Efficient Spectral Deconvolution of Gas Chromatography-High-Resolution Mass Spectrometry Metabolomics Data-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1021/acs.jproteome.7b00633-
dc.identifier.pmid29076734-
dc.identifier.scopuseid_2-s2.0-85040170531-
dc.identifier.volume17-
dc.identifier.issue1-
dc.identifier.spage470-
dc.identifier.epage478-
dc.identifier.eissn1535-3907-
dc.identifier.isiWOS:000419749800043-

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