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J Proteome Res. 2018 Jan 5;17(1):470-478. doi: 10.1021/acs.jproteome.7b00633. Epub 2017 Nov 7.

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

Author information

1
University of North Carolina at Charlotte , Charlotte, North Carolina 28223, United States.
2
University of Hawaii Cancer Center , Honolulu, Hawaii 96813, United States.
3
Emory University , Atlanta, Georgia 30322, United States.

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 First, all of the algorithms in ADAP-GC 3.0 written in R have been replaced by their analogues in Java and incorporated into MZmine 2 to make the workflow user-friendly.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.

KEYWORDS:

compound identification; compound quantitation; computational work flow; gas chromatography; high mass resolution; mass spectrometry; metabolomics; software; spectral deconvolution; visualization

PMID:
29076734
DOI:
10.1021/acs.jproteome.7b00633
[Indexed for MEDLINE]

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