An automated data analysis pipeline for GC-TOF-MS metabonomics studies

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

Research output: Contribution to journalArticlepeer-review

54 Scopus citations


Recent technological advances have made it possible to carry out high-throughput metabonomics studies using gas chromatography coupled with time-of-flight mass spectrometry. Large volumes of data are produced from these studies and there is a pressing need for algorithms that can efficiently process and analyze data in a high-throughput fashion as well. We present an Automated Data Analysis Pipeline (ADAP) that has been developed for this purpose. ADAP consists of peak detection, deconvolution, peak alignment, and library search. It allows data to flow seamlessly through the analysis steps without any human intervention and features two novel algorithms in the analysis. Specifically, clustering is successfully applied in deconvolution to resolve coeluting compounds that are very common in complex samples and a two-phase alignment process has been implemented to enhance alignment accuracy. ADAP is written in standard C++ and R and uses parallel computing via Message Passing Interface for fast peak detection and deconvolution. ADAP has been applied to analyze both mixed standards samples and serum samples and identified and quantified metabolites successfully. ADAP is available at

Original languageEnglish (US)
Pages (from-to)5974-5981
Number of pages8
JournalJournal of Proteome Research
Issue number11
StatePublished - Nov 5 2010
Externally publishedYes


  • alignment
  • clustering analysis
  • deconvolution
  • extracted ion chromatogram
  • gas chromatography-mass spectrometry

ASJC Scopus subject areas

  • Biochemistry
  • Chemistry(all)


Dive into the research topics of 'An automated data analysis pipeline for GC-TOF-MS metabonomics studies'. Together they form a unique fingerprint.

Cite this