Exploring Open-Source Software for Advanced GC×GC-MS Data Analysis

A paper recently published in Briefings in Bioinformatics (1) introduced an open-source data processing software that enables annotation, deconvolution, and analysis of data collected through two-dimensional gas chromatography coupled with mass spectrometry(GC×GC–MS). Called GcDUO, the software was created by a team comprised of researchers from the University of Rovira (Tarragona, Spain), the University of Amsterdam (The Netherlands), the Institut d'Investigació Sanitària Pere Virgili (Reus, Spain), and the University of Liège (Belgium).

GcDUO uses raw data to extract the features without any previous knowledge of the samples, and then deconvolutes and identifies them. This batch approach ensures consistency in peak detection, alignment, and annotation throughout all sample analysis (1).

Although GC×GC–MS has been widely accepted a powerful analytical tool for untargeted analysis of complex samples (2-4), difficulties in analyzing the amassed data has prevented more widespread use (5,6). Several software options are currently available for GC×GC–MS data analysis, with many commercial solutions designed to perform all steps through a graphical user interface (7). The main limitation of these tools is that samples are processed individually and then results are combined—a process that is both computationally expensive and prone to peak alignment errors, the authors wrote. Without batch processing, the identification and correction of systematic errors that may affect all samples in an analytical run becomes difficult. These factors made it clear that there was a need for open-source software with batch processing features, a feature not existing with GC×GC (1).

Operating through several key modules (data import, ROI selection, deconvolution, peak annotation, and data integration and visualization), the software accepts non-vendor-specific, standardized computable document format (CDF files), rearranging the data into 4D tensor structures while preserving the GC×GC–MS data structure in every dataset. GcDUO leverages advanced chemometric techniques, including Parallel Factor Analysis (PARAFAC) as well as PARAFAC2, its variant, to handle multidimensional data. The software includes features to optimize the analysis, such as noise reduction, signal enhancement, and peak alignment across multiple samples (1).

Validation of the effectiveness of GcDUO has been confirmed through the analysis of two datasets, identifying correctly more than 89% of the present peaks in both datasets. Comparing the findings with those of ChromaTOF, the software previously considered the gold standard, showed correlation of 0.909 between peak area measurements. This positive comparison demonstrates GcDUO’s capability and reinforces the fact that it can be considered a strong tool for GC×GC–MS data analysis, as it offers an open-source alternative to black-box commercial software packages. The software could potentially be useful for researchers in metabolomics and related fields. Overall, the authors state that GcDUO provides a powerful, flexible platform for comprehensive analysis of GC×GC–MS data, allowing for the easy extraction of meaningful insights from complex chemical datasets (1).

Scientist uses a computer and dashboard for analysis of information on complex data sets on computer. © Deemenwah studio - stock.adobe.com

References

1. Llambrich, M.; van der Kloet, F. M. Sementé, L.; Rodrigues, A.; Samanipour, S.; Stefanuto, P. H.; Westerhuis, J. A.; Cumeras, R.; Brezmes, J. GcDUO: An Open-Source Software for GC × GC-MS Data Analysis. Brief. Bioinform. 2025, 26 (2), bbaf080. DOI: 10.1093/bib/bbaf080

2. Trinklein, T. J.; Cain, C. N.; Ochoa, G. S.; Schöneich, S.; Mikaliunaite, L.; Synovec, R. E. Recent Advances in GC×GC and Chemometrics to Address Emerging Challenges in Nontargeted Analysis. Anal. Chem. 2023, 95 (1), 264-286. DOI: 10.1021/acs.analchem.2c04235

3. Franchina, F. A.; Purcaro, G.; Burklund, A.; Beccaria, M.; Hill, J. E. Evaluation of Different Adsorbent Materials for the Untargeted and Targeted Bacterial VOC Analysis Using GC×GC-MS. Anal. Chim. Acta 2019, 1066, 146-153. DOI: 10.1016/j.aca.2019.03.027

4. Morimoto, J.; Rosso, M. C.; Kfoury, N.; Bicchi, C.; Cordero, C.; Robbat Jr, A. Untargeted/Targeted 2D Gas Chromatography/Mass Spectrometry Detection of the Total Volatile Tea Metabolome. Molecules 2019, 24 (20), 3757. DOI: 10.3390/molecules24203757

5. Feschyan, S. M.; Simonyan, R. M.; Simonyan, G. M.; Simonyan, M. A.; Manukyan, A. L. NADPH Containing Superoxide-Producing Thermostable Complex from Raspberry, Apricot, Grape, and Grape Seeds: Isolation, Purification, and Properties. Plant Methods 2023, 19 (1), 1. DOI: 10.1186/s13007-022-00978-9

6. Berrier, K. L.; Prebihalo, S. E.; Synovec, R. E. Advanced Data Handling in Comprehensive Two-Dimensional Gas Chromatography;in Separation Science and Technology, Vol. 12, N. Snow, Ed. Academic Press, 2020, 229-268. DOI: 10.1016/B978-0-12-813745-1.00007-6

7. Wilde, M. J.; Zhao, B.; Cordell, R. L.; Ibrahim, W.; Singapuri, A. Greening N. J,. et al. Automating and Extending Comprehensive Two-Dimensional Gas Chromatography Data Processing by Interfacing Open-Source and Commercial Software. Anal. Chem. 2020, 92 (20), 13953-13960. DOI: 10.1021/acs.analchem.0c02844