ADAP-GC 3.0: Improved Peak Detection and Deconvolution of Co-eluting Metabolites from GC/TOF-MS Data for Metabolomics Studies

Yan Ni, Mingming Su, Yunping Qiu, Wei Jia, Xiuxia Du

Research output: Contribution to journalArticle

30 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)8802-8811
Number of pages10
JournalAnalytical Chemistry
Volume88
Issue number17
DOIs
StatePublished - Sep 6 2016

ASJC Scopus subject areas

  • Analytical Chemistry

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