File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1021/acs.analchem.6b02222
- Scopus: eid_2-s2.0-84985993575
- PMID: 27461032
- WOS: WOS:000382805900064
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: ADAP-GC 3.0: Improved Peak Detection and Deconvolution of Co-eluting Metabolites from GC/TOF-MS Data for Metabolomics Studies
Title | ADAP-GC 3.0: Improved Peak Detection and Deconvolution of Co-eluting Metabolites from GC/TOF-MS Data for Metabolomics Studies |
---|---|
Authors | |
Issue Date | 2016 |
Citation | Analytical Chemistry, 2016, v. 88, n. 17, p. 8802-8811 How to Cite? |
Abstract | ADAP-GC is an automated computational pipeline for untargeted, GC/MS-based metabolomics studies. It takes raw mass spectrometry data as input and carries out a sequence of data processing steps including construction of extracted ion chromatograms, detection of chromatographic peak features, deconvolution of coeluting compounds, and alignment of compounds across samples. Despite the increased accuracy from the original version to version 2.0 in terms of extracting metabolite information for identification and quantitation, ADAP-GC 2.0 requires appropriate specification of a number of parameters and has difficulty in extracting information on compounds that are in low concentration. To overcome these two limitations, ADAP-GC 3.0 was developed to improve both the robustness and sensitivity of compound detection. In this paper, we report how these goals were achieved and compare ADAP-GC 3.0 against three other software tools including ChromaTOF, AnalyzerPro, and AMDIS that are widely used in the metabolomics community. |
Persistent Identifier | http://hdl.handle.net/10722/342528 |
ISSN | 2021 Impact Factor: 8.008 2020 SCImago Journal Rankings: 2.117 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ni, Yan | - |
dc.contributor.author | Su, Mingming | - |
dc.contributor.author | Qiu, Yunping | - |
dc.contributor.author | Jia, Wei | - |
dc.contributor.author | Du, Xiuxia | - |
dc.date.accessioned | 2024-04-17T07:04:27Z | - |
dc.date.available | 2024-04-17T07:04:27Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | Analytical Chemistry, 2016, v. 88, n. 17, p. 8802-8811 | - |
dc.identifier.issn | 0003-2700 | - |
dc.identifier.uri | http://hdl.handle.net/10722/342528 | - |
dc.description.abstract | ADAP-GC is an automated computational pipeline for untargeted, GC/MS-based metabolomics studies. It takes raw mass spectrometry data as input and carries out a sequence of data processing steps including construction of extracted ion chromatograms, detection of chromatographic peak features, deconvolution of coeluting compounds, and alignment of compounds across samples. Despite the increased accuracy from the original version to version 2.0 in terms of extracting metabolite information for identification and quantitation, ADAP-GC 2.0 requires appropriate specification of a number of parameters and has difficulty in extracting information on compounds that are in low concentration. To overcome these two limitations, ADAP-GC 3.0 was developed to improve both the robustness and sensitivity of compound detection. In this paper, we report how these goals were achieved and compare ADAP-GC 3.0 against three other software tools including ChromaTOF, AnalyzerPro, and AMDIS that are widely used in the metabolomics community. | - |
dc.language | eng | - |
dc.relation.ispartof | Analytical Chemistry | - |
dc.title | ADAP-GC 3.0: Improved Peak Detection and Deconvolution of Co-eluting Metabolites from GC/TOF-MS Data for Metabolomics Studies | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1021/acs.analchem.6b02222 | - |
dc.identifier.pmid | 27461032 | - |
dc.identifier.scopus | eid_2-s2.0-84985993575 | - |
dc.identifier.volume | 88 | - |
dc.identifier.issue | 17 | - |
dc.identifier.spage | 8802 | - |
dc.identifier.epage | 8811 | - |
dc.identifier.eissn | 1520-6882 | - |
dc.identifier.isi | WOS:000382805900064 | - |