Literature DB >> 29076734

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

Aleksandr Smirnov1, Wei Jia2, Douglas I Walker3, Dean P Jones3, Xiuxia Du1.   

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 First, all of the algorithms in ADAP-GC 3.0 written in R have been replaced by their analogues in Java and incorporated into MZmine 2 to make the workflow user-friendly.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

Entities:  

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

Mesh:

Year:  2017        PMID: 29076734     DOI: 10.1021/acs.jproteome.7b00633

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  5 in total

1.  ADAP-GC 4.0: Application of Clustering-Assisted Multivariate Curve Resolution to Spectral Deconvolution of Gas Chromatography-Mass Spectrometry Metabolomics Data.

Authors:  Aleksandr Smirnov; Yunping Qiu; Wei Jia; Douglas I Walker; Dean P Jones; Xiuxia Du
Journal:  Anal Chem       Date:  2019-07-05       Impact factor: 6.986

2.  WiPP: Workflow for Improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) Data.

Authors:  Nico Borgsmüller; Yoann Gloaguen; Tobias Opialla; Eric Blanc; Emilie Sicard; Anne-Lise Royer; Bruno Le Bizec; Stéphanie Durand; Carole Migné; Mélanie Pétéra; Estelle Pujos-Guillot; Franck Giacomoni; Yann Guitton; Dieter Beule; Jennifer Kirwan
Journal:  Metabolites       Date:  2019-08-21

3.  NMF-Based Spectral Deconvolution with a Web Platform GC Mixture Touch.

Authors:  Yasuyuki Zushi
Journal:  ACS Omega       Date:  2021-01-19

Review 4.  Operationalizing the Exposome Using Passive Silicone Samplers.

Authors:  Zoe Coates Fuentes; Yuri Levin Schwartz; Anna R Robuck; Douglas I Walker
Journal:  Curr Pollut Rep       Date:  2022-01-04

5.  Digging deeper - A new data mining workflow for improved processing and interpretation of high resolution GC-Q-TOF MS data in archaeological research.

Authors:  Ansgar Korf; Simon Hammann; Robin Schmid; Matti Froning; Heiko Hayen; Lucy J E Cramp
Journal:  Sci Rep       Date:  2020-01-21       Impact factor: 4.379

  5 in total

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