Search

Stable isotope assisted metabolomics (SIAM) measures the abundance levels of metabolites in a particular pathway using stable isotope tracers (e.g., 13 C, 18 O and/or 15 N). We report a method termed signature ion approach for analysis of SIAM data acquired on a GC-MS system equipped with an electron ionization (EI) ion source. The signature ion is a fragment ion in EI mass spectrum of a derivatized metabolite that contains all atoms of the underivatized metabolite, except the hydrogen atoms lost during derivatization. In this approach, GC-MS data of metabolite standards were used to recognize the signature ion from the EI mass spectra acquired from stable isotope labeled samples, and a linear regression model was used to deconvolute the intensity of overlapping isotopologues. A mixture score function was also employed for cross-sample chromatographic peak list alignment to recognize the chromatographic peaks generated by the same metabolite in different samples, by simultaneously evaluating the similarity of retention time and EI mass spectrum of two chromatographic peaks. Analysis of a mixture of 16 13 C-labeled and 16 unlabeled amino acids showed that the signature ion approach accurately identified and quantified all isotopologues. Analysis of polar metabolite extracts from cells respectively fed with uniform 13 C-glucose and 13 C-glutamine further demonstrated that this method can also be used to analyze the complex data acquired from biological samples., (Copyright © 2017 Elsevier B.V. All rights reserved.)

We report software entitled EIder (EI mass spectrum identifier) that provides users with eight literature reported spectrum matching algorithms for compound identification from gas chromatography mass spectrometry (GC-MS) data. EIder calculates retention index according to experimental conditions categorized by column class, column type and data type, where 9 empirical distribution functions of the absolute retention index deviation to its mean value were constructed using the National Institute of Standards and Technology (NIST) 2011 retention index database to improve the accuracy of compound identification. EIder filters compound candidates based on elementary composition and derivatization reagent, and automatically adds the molecular information of the native compound to each derivatized compound using a manually created database. When multiple samples are analyzed together, EIder performs cross-sample alignment and provides an option of using an average mass spectrum for compound identification. Furthermore, a suite of graphical user interfaces are implemented in EIder to allow users to both manually and automatically modify the identification results using experimental information at various analysis stages. Analysis of three types of GC-MS datasets indicates that the developed EIder software can improve the accuracy of compound identification., (Copyright © 2016 Elsevier B.V. All rights reserved.)

We report a compound identification method (SimMR), which simultaneously evaluates the mass spectrum similarity and the retention index distance using an empirical mixture score function, for the analysis of GC-MS data. The performance of the developed SimMR method was compared to that of two existing compound identification strategies. One is the mass spectrum matching method without incorporation of retention index information (SM). The other is the method that sequentially evaluates the mass spectrum similarity and retention index distance (SeqMR). For comparison purposes, we used the NIST/EPA/NIH Mass Spectral Library 2005. Our study demonstrates that SimMR performs the best among the three compound identification methods, by improving the overall identification accuracy up to 1.53% and 4.81% compared to SeqMR and SM, respectively.

We developed a method, iMatch2, for compound identification using retention indices (RI) in NIST11 library. Three-way ANOVA test and Kruskal-Wallis test respectively demonstrate that column class and temperature program type defined by the NIST library are the most dominant factors affecting the magnitude of retention index while the retention index data type does not cause significant difference. The developed linear regression transformation for merging retention indices with different data types, but the same column class and temperature program type, reduces the standard deviation of retention index up to 8%, compared to the simple union approach used in the original iMatch. As for outlier detection methods to remove retention indices having large difference with the remaining data of the same compound, Tietjen-Moore test and generalized extreme studentized deviate test are the strictest methods, while methods such as Dixon's test, Thompson tau approach, and Grubbs' test are more conservative. To improve the accuracy of retention index window, a concept of compound specific retention index window is introduced for compounds with a large number of retention indices in the NIST11 library, while the retention index window is calculated from empirical distributions for the compounds with a small number of retention indices. Analysis of the experimental data of a mixture of compound standards and the metabolite extract from mouse liver show significant improvement of retention index quality in the NIST11 library and the new data analysis methods., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Motivation: Due to the high complexity of metabolome, the comprehensive 2D gas chromatography time-of-flight mass spectrometry (GC×GC-TOF MS) is considered as a powerful analytical platform for metabolomics study. However, the applications of GC×GC-TOF MS in metabolomics are not popular owing to the lack of bioinformatics system for data analysis., Results: We developed a computational platform entitled metabolomics profiling pipeline (MetPP) for analysis of metabolomics data acquired on a GC×GC-TOF MS system. MetPP can process peak filtering and merging, retention index matching, peak list alignment, normalization, statistical significance tests and pattern recognition, using the peak lists deconvoluted from the instrument data as its input. The performance of MetPP software was tested with two sets of experimental data acquired in a spike-in experiment and a biomarker discovery experiment, respectively. MetPP not only correctly aligned the spiked-in metabolite standards from the experimental data, but also correctly recognized their concentration difference between sample groups. For analysis of the biomarker discovery data, 15 metabolites were recognized with significant concentration difference between the sample groups and these results agree with the literature results of histological analysis, demonstrating the effectiveness of applying MetPP software for disease biomarker discovery., Availability: The source code of MetPP is available at http://metaopen.sourceforge.net, Contact: xiang.zhang@louisville.edu, Supplementary Information: Supplementary data are available at Bioinformatics online.

Compound identification in gas chromatography-mass spectrometry (GC-MS) is usually achieved by matching query spectra to spectra present in a reference library. Although several spectral similarity measures have been developed and compared using a small reference library, it still remains unknown how the relationship between the spectral similarity measure and the size of reference library affects on the identification accuracy as well as the optimal weight factor. We used three reference libraries to investigate the dependency of the optimal weight factor, spectral similarity measure and the size of reference library. Our study demonstrated that the optimal weight factor depends on not only spectral similarity measure but also the size of reference library. The mixture semi-partial correlation measure outperforms all existing spectral similarity measures in all tested reference libraries, in spite of the computational expense. Furthermore, the accuracy of compound identification using a larger reference library in future is estimated by varying the size of reference library. Simulation study indicates that the mixture semi-partial correlation measure will have the best performance with the increase of reference library in future., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Compound identification is a key component of data analysis in the applications of gas chromatography-mass spectrometry (GC-MS). Currently, the most widely used compound identification is mass spectrum matching, in which the dot product and its composite version are employed as spectral similarity measures. Several forms of transformations for fragment ion intensities have also been proposed to increase the accuracy of compound identification. In this study, we introduced partial and semipartial correlations as mass spectral similarity measures and applied them to identify compounds along with different transformations of peak intensity. The mixture versions of the proposed method were also developed to further improve the accuracy of compound identification. To demonstrate the performance of the proposed spectral similarity measures, the National Institute of Standards and Technology (NIST) mass spectral library and replicate spectral library were used as the reference library and the query spectra, respectively. Identification results showed that the mixture partial and semipartial correlations always outperform both the dot product and its composite measure. The mixture similarity with semipartial correlation has the highest accuracy of 84.6% in compound identification with a transformation of (0.53,1.3) for fragment ion intensity and m/z value, respectively.

Motivation: The compound identification in gas chromatography-mass spectrometry (GC-MS) is achieved by matching the experimental mass spectrum to the mass spectra in a spectral library. It is known that the intensities with higher m/z value in the GC-MS mass spectrum are the most diagnostic. Therefore, to increase the relative significance of peak intensities of higher m/z value, the intensities and m/z values are usually transformed with a set of weight factors. A poor quality of weight factors can significantly decrease the accuracy of compound identification. With the significant enrichment of the mass spectral database and the broad application of GC-MS, it is important to re-visit the methods of discovering the optimal weight factors for high confident compound identification., Results: We developed a novel approach to finding the optimal weight factors only through a reference library for high accuracy compound identification. The developed approach first calculates the ratio of skewness to kurtosis of the mass spectral similarity scores among spectra (compounds) in a reference library and then considers a weight factor with the maximum ratio as the optimal weight factor. We examined our approach by comparing the accuracy of compound identification using the mass spectral library maintained by the National Institute of Standards and Technology. The results demonstrate that the optimal weight factors for fragment ion peak intensity and m/z value found by the developed approach outperform the current weight factors for compound identification., Availability: The results and R package are available at http://stage.louisville.edu/faculty/x0zhan17/software/ software-development.

The high-throughput gas chromatography/mass spectrometry (GC/MS) technology offers a powerful means of analyzing a large number of chemical and biological samples. One of the important analyses of GC/MS data is compound identification. In this work, novel spectral similarity measures based on the discrete wavelet and Fourier transforms were proposed. The proposed methods are composite similarities that are composed of weighted intensities and wavelet/Fourier coefficients using cosine correlation. The performance of the proposed approaches along with the existing similarity measures was evaluated using the NIST Chemistry WebBook mass database maintained by the National Institute of Standards and Technology (NIST) as a library of reference spectra and repetitive mass spectral data as query spectra. The analysis results showed that the identification accuracies of the wavelet- and Fourier-transform-based methods were improved by 2.02% and 1.95%, respectively, compared to that of the weighted dot product (cosine correlation) and by 3.01% and 3.08%, respectively, compared to that of the composite similarity measure. The improved identification accuracy demonstrates that the proposed approaches outperformed the existing similarity measures in the literature.

Background: Comprehensive 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., Results: We 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., Conclusions: The 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.