TY - JOUR
T1 - ADAP-GC 3.2
T2 - Graphical Software Tool for Efficient Spectral Deconvolution of Gas Chromatography-High-Resolution Mass Spectrometry Metabolomics Data
AU - Smirnov, Aleksandr
AU - Jia, Wei
AU - Walker, Douglas I.
AU - Jones, Dean P.
AU - Du, Xiuxia
N1 - Publisher Copyright:
© 2017 American Chemical Society.
PY - 2018/1/5
Y1 - 2018/1/5
N2 - 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 First, all of the algorithms in ADAP-GC 3.0 written in R have been replaced by their analogues in Java and incorporated into MZmine 2 to make the workflow user-friendly.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.
AB - 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 First, all of the algorithms in ADAP-GC 3.0 written in R have been replaced by their analogues in Java and incorporated into MZmine 2 to make the workflow user-friendly.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.
KW - compound identification
KW - compound quantitation
KW - computational work flow
KW - gas chromatography
KW - high mass resolution
KW - mass spectrometry
KW - metabolomics
KW - software
KW - spectral deconvolution
KW - visualization
UR - https://www.scopus.com/pages/publications/85040170531
U2 - 10.1021/acs.jproteome.7b00633
DO - 10.1021/acs.jproteome.7b00633
M3 - Article
C2 - 29076734
AN - SCOPUS:85040170531
SN - 1535-3893
VL - 17
SP - 470
EP - 478
JO - Journal of Proteome Research
JF - Journal of Proteome Research
IS - 1
ER -