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- Publisher Website: 10.1021/acs.jproteome.7b00633
- Scopus: eid_2-s2.0-85040170531
- PMID: 29076734
- WOS: WOS:000419749800043
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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
Title | ADAP-GC 3.2: Graphical Software Tool for Efficient Spectral Deconvolution of Gas Chromatography-High-Resolution Mass Spectrometry Metabolomics Data |
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Authors | |
Keywords | compound identification compound quantitation computational work flow gas chromatography high mass resolution mass spectrometry metabolomics software spectral deconvolution visualization |
Issue Date | 2018 |
Citation | Journal of Proteome Research, 2018, v. 17, n. 1, p. 470-478 How to Cite? |
Abstract | ADAP-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 First, all of the algorithms in ADAP-GC 3.0 written in R have been replaced by their analogues in Java and incorporated into MZmine 2 to make the workflow user-friendly.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 Identifier | http://hdl.handle.net/10722/342551 |
ISSN | 2023 Impact Factor: 3.8 2023 SCImago Journal Rankings: 1.299 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Smirnov, Aleksandr | - |
dc.contributor.author | Jia, Wei | - |
dc.contributor.author | Walker, Douglas I. | - |
dc.contributor.author | Jones, Dean P. | - |
dc.contributor.author | Du, Xiuxia | - |
dc.date.accessioned | 2024-04-17T07:04:37Z | - |
dc.date.available | 2024-04-17T07:04:37Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Journal of Proteome Research, 2018, v. 17, n. 1, p. 470-478 | - |
dc.identifier.issn | 1535-3893 | - |
dc.identifier.uri | http://hdl.handle.net/10722/342551 | - |
dc.description.abstract | ADAP-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.language | eng | - |
dc.relation.ispartof | Journal of Proteome Research | - |
dc.subject | compound identification | - |
dc.subject | compound quantitation | - |
dc.subject | computational work flow | - |
dc.subject | gas chromatography | - |
dc.subject | high mass resolution | - |
dc.subject | mass spectrometry | - |
dc.subject | metabolomics | - |
dc.subject | software | - |
dc.subject | spectral deconvolution | - |
dc.subject | visualization | - |
dc.title | ADAP-GC 3.2: Graphical Software Tool for Efficient Spectral Deconvolution of Gas Chromatography-High-Resolution Mass Spectrometry Metabolomics Data | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1021/acs.jproteome.7b00633 | - |
dc.identifier.pmid | 29076734 | - |
dc.identifier.scopus | eid_2-s2.0-85040170531 | - |
dc.identifier.volume | 17 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 470 | - |
dc.identifier.epage | 478 | - |
dc.identifier.eissn | 1535-3907 | - |
dc.identifier.isi | WOS:000419749800043 | - |