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BiPACE 2D--graph-based multiple alignment for comprehensive 2D gas chromatography-mass spectrometry.

Nils Hoffmann1, Mathias Wilhelm, Anja Doebbe

  • 1Genome Informatics, Faculty of Technology and CeBiTec, Algae Biotechnology & Bioenergy, Faculty of Biology and CeBiTec, Proteomics and Metabolomics Research, Bielefeld University, 33501 Bielefeld, Germany.

Bioinformatics (Oxford, England)
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PubMed
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BiPACE 2D is a new automated algorithm for aligning peaks in comprehensive 2D gas chromatography-mass spectrometry data. It offers reliable retention time alignment for complex metabolomics analyses.

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Area of Science:

  • Analytical Chemistry
  • Metabolomics
  • Chromatography

Background:

  • Comprehensive 2D gas chromatography-mass spectrometry (GCxGC-MS) generates large datasets requiring automated data processing.
  • Accurate peak detection, matching, and alignment are crucial for analyzing complex mixtures in metabolomics.
  • Existing algorithms for retention time alignment in GCxGC-MS often lack scalability and reliability.

Purpose of the Study:

  • To introduce BiPACE 2D, an automated algorithm for retention time alignment in GCxGC-MS.
  • To evaluate the performance of BiPACE 2D against existing alignment algorithms.
  • To provide a new, publicly available dataset for method validation.

Main Methods:

  • Development of the BiPACE 2D algorithm within the Maltcms framework.
  • Evaluation of BiPACE 2D using three published GCxGC-MS datasets.
  • Comparison of BiPACE 2D with mSPA, SWPA, and Guineu algorithms.

Main Results:

  • BiPACE 2D demonstrates effective automated retention time alignment for GCxGC-MS data.
  • The algorithm was evaluated on multiple datasets, showcasing its performance.
  • A new dataset from a Chlamydomonas reinhardtii experiment is provided for future benchmarking.

Conclusions:

  • BiPACE 2D offers a scalable and reliable solution for automated peak alignment in GCxGC-MS.
  • The availability of the software and datasets facilitates further research and development in metabolomics data analysis.
  • This work contributes to advancing automated data processing in analytical chemistry.