Comprehensive Two-Dimensional Gas Chromatography-Mass Spectrometry Analysis of Exhaled Breath Compounds after Whole Grain Diets.
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ABSTRACT:
SUBMITTER: Raninen K
PROVIDER: S-EPMC8125105 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundComprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC × GC-MS) is a powerful technique which has gained increasing attention over the last two decades. The GC × GC-MS provides much increased separation capacity, chemical selectivity and sensitivity for complex sample analysis and brings more accurate information about compound retention times and mass spectra. Despite these advantages, the retention times of the resolved peaks on the two-dimensional gas chromatographic columns are always shifted due to experimental variations, introducing difficulty in the data processing for metabolomics analysis. Therefore, the retention time variation must be adjusted in order to compare multiple metabolic profiles obtained from different conditions.ResultsWe developed novel peak alignment algorithms for both homogeneous (acquired under the identical experimental conditions) and heterogeneous (acquired under the different experimental conditions) GC × GC-MS data using modified Smith-Waterman local alignment algorithms along with mass spectral similarity. Compared with literature reported algorithms, the proposed algorithms eliminated the detection of landmark peaks and the usage of retention time transformation. Furthermore, an automated peak alignment software package was established by implementing a likelihood function for optimal peak alignment.ConclusionsThe proposed Smith-Waterman local alignment-based algorithms are capable of aligning both the homogeneous and heterogeneous data of multiple GC × GC-MS experiments without the transformation of retention times and the selection of landmark peaks. An optimal version of the SW-based algorithms was also established based on the associated likelihood function for the automatic peak alignment. The proposed alignment algorithms outperform the literature reported alignment method by analyzing the experiment data of a mixture of compound standards and a metabolite extract of mouse plasma with spiked-in compound standards.
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Project description:Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC/TOF-MS) has been used for metabolite profiling in metabolomics. However, there is still much experimental variation to be controlled including both within-experiment and between-experiment variation. For efficient analysis, an ideal peak alignment method to deal with such variations is in great need.Using experimental data of a mixture of metabolite standards, we demonstrated that our method has better performance than other existing method which is not model-based. We then applied our method to the data generated from the plasma of a rat, which also demonstrates good performance of our model.We developed a model-based peak alignment method to process both homogeneous and heterogeneous experimental data. The unique feature of our method is the only model-based peak alignment method coupled with metabolite identification in an unified framework. Through the comparison with other existing method, we demonstrated that our method has better performance. Data are available at http://stage.louisville.edu/faculty/x0zhan17/software/software-development/mspa. The R source codes are available at http://www.biostat.iupui.edu/~ChangyuShen/CodesPeakAlignment.zip.2136949528613691.
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Project description:MotivationComprehensive two-dimensional gas chromatography mass spectrometry (GC × GC-MS) brings much increased separation capacity, chemical selectivity and sensitivity for metabolomics and provides more accurate information about metabolite retention times and mass spectra. However, there is always a shift of retention times in the two columns that makes it difficult to compare metabolic profiles obtained from multiple samples exposed to different experimental conditions.ResultsThe existing peak alignment algorithms for GC × GC-MS data use the peak distance and the spectra similarity sequentially and require predefined either distance-based window and/or spectral similarity-based window. To overcome the limitations of the current alignment methods, we developed an optimal peak alignment using a novel mixture similarity by employing the peak distance and the spectral similarity measures simultaneously without any variation windows. In addition, we examined the effect of the four different distance measures such as Euclidean, Maximum, Manhattan and Canberra distances on the peak alignment. The performance of our proposed peak alignment algorithm was compared with the existing alignment methods on the two sets of GC × GC-MS data. Our analysis showed that Canberra distance performed better than other distances and the proposed mixture similarity peak alignment algorithm prevailed against all literature reported methods.AvailabilityThe data and software mSPA are available at http://stage.louisville.edu/faculty/x0zhan17/software/software-development.
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Project description:Compared to other analytical platforms, comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC×GC-MS) has much increased separation power for analysis of complex samples and thus is increasingly used in metabolomics for biomarker discovery. However, accurate peak detection remains a bottleneck for wide applications of GC×GC-MS. Therefore, the normal-exponential-Bernoulli (NEB) model is generalized by gamma distribution and a new peak detection algorithm using the normal-gamma-Bernoulli (NGB) model is developed. Unlike the NEB model, the NGB model has no closed-form analytical solution, hampering its practical use in peak detection. To circumvent this difficulty, three numerical approaches, which are fast Fourier transform (FFT), the first-order and the second-order delta methods (D1 and D2), are introduced. The applications to simulated data and two real GC×GC-MS data sets show that the NGB-D1 method performs the best in terms of both computational expense and peak detection performance.
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