Baitmet, a computational approach for GC–MS library-driven metabolite profiling

Current computational tools for gas chromatography – mass spectrometry (GC – MS) metabolomics profiling do not focus on metabolite identification, that still remains as the entire workflow bottleneck and it relies on manual d ata reviewing. Metabolomics ad vent has fostered the development of public metabolite repositories containing mass spectra and retentio n indices, two orthogonal prop erties needed for metabol ite identification. Such libraries can be used for library - driven compound profiling of large datasets produced in metabolomics, a complementary approach to current GC – MS non - targeted data analysis solutions that can eventually help to assess metabolite i dentities more efficiently. Results: This paper introduces Baitmet, an integrated open - source computational tool written in R enclosing a complete workflow to perform high - throughput library - driven GC – MS profiling in complex samples. Baitmet capabilities w ere assa yed in a metabolomics study in volving 182 human serum samples where a set of 61 metabolites were profiled given a reference library. Conclusions: Baitmet allows high - thr oughput and wide scope interro gation on the metabolic composition of complex sa mples analyzed using GC – MS via freely available spectral dataPeer ReviewedPostprint (author's final draft

A new method for the automated selection of the number of components for deconvolving overlapping chromatographic peaks

Mathematical deconvolution methods can separate co-eluting peaks in samples for which (chromatographic) separation fail. However, these methods often heavily rely on manual user-input and interpretation. This is not only time-consuming but also error-prone and automation is needed if such methods are to be applied in a routine manner. One major hurdle when automating deconvolution methods is the selection of the correct number of components used for building the model. We propose a new method for the automatic determination of the optimum number of components when applying multivariate curve resolution (MCR) to comprehensive two-dimensional gas chromatography-mass spectrometry (GC x GC-MS) data. It is based on a two-fold cross-validation scheme. The obtained overall cross-validation error decreases when adding components and increases again once over-fitting of the data starts to occur. The turning point indicates that the optimum number of components has been reached. Overall, the method is at least as good as and sometimes superior to the inspection of the eigenvalues when performing singular-value decomposition. However, its strong point is that it can be fully automated and it is thus more efficient and less prone to subjective interpretation. The developed method has been applied to two different-sized regions in a GC x GC-MS chromatogram. In both regions, the cross-validation scheme resulted in selecting the correct number of components for applying MCR. The pure concentration and mass spectral profiles obtained can then be used for identification and/or quantification of the compounds. While the method has been developed for applying MCR to GC x GC-MS data, a transfer to other deconvolution methods and other analytical systems should only require minor modifications. (c) 2013 Elsevier B.V. All rights reserved

A New Bayesian Approach for Estimating the Presence of a Suspected Compound in Routine Screening Analysis

A novel method for compound identification in liquid chromatography-high resolution mass spectrometry (LC-HRMS) is proposed. The method, based on Bayesian statistics, accommodates all possible uncertainties involved, from instrumentation up to data analysis into a single model yielding the probability of the compound of interest being present/absent in the sample. This approach differs from the classical methods in two ways. First, it is probabilistic (instead of deterministic); hence, it computes the probability that the compound is (or is not) present in a sample. Second, it answers the hypothesis “the compound is present”, opposed to answering the question “the compound feature is present”. This second difference implies a shift in the way data analysis is tackled, since the probability of interfering compounds (i.e., isomers and isobaric compounds) is also taken into account

Application of Fragment Ion Information as Further Evidence in Probabilistic Compound Screening Using Bayesian Statistics and Machine Learning : A Leap Toward Automation

In this work, we introduce an automated, efficient, and elegant model to combine all pieces of evidence (e.g., expected retention times, peak shapes, isotope distributions, fragment-to-parent ratio) obtained from liquid chromatography-tandem mass spectrometry (LC-MS/MS/MS) data for screening purposes. Combining all these pieces of evidence requires a careful assessment of the uncertainties in the analytical system as well as all possible outcomes. To-date, the majority of the existing algorithms are highly dependent on user input parameters. Additionally, the screening process is tackled as a deterministic problem. In this work we present a Bayesian framework to deal with the combination of all these pieces of evidence. Contrary to conventional algorithms, the information is treated in a probabilistic way, and a final probability assessment of the presence/absence of a compound feature is computed. Additionally, all the necessary parameters except the chromatographic band broadening for the method are learned from the data in training and learning phase of the algorithm, avoiding the introduction of a large number of user-defined parameters. The proposed method was validated with a large data set and has shown improved sensitivity and specificity in comparison to a threshold-based commercial software package.</p

Class-Conditional Feature Modeling For Ignitable Liquid Classification With Substantial Substrate Contribution In Fire Debris Analysis

Forensic chemical analysis of fire debris addresses the question of whether ignitable liquid residue is present in a sample and, if so, what type. Evidence evaluation regarding this question is complicated by interference from pyrolysis products of the substrate materials present in a fire.A method is developed to derive a set of class-conditional features for the evaluation of such complex samples. The use of a forensic reference collection allows characterization of the variation in complex mixtures of substrate materials and ignitable liquids even when the dominant feature is not specific to an ignitable liquid. Making use of a novel method for data imputation under complex mixing conditions, a distribution is modeled for the variation between pairs of samples containing similar ignitable liquid residues. Examining the covariance of variables within the different classes allows different weights to be placed on features more important in discerning the presence of a particular ignitable liquid residue. Performance of the method is evaluated using a database of total ion spectrum (TIS) measurements of ignitable liquid and fire debris samples. These measurements include 119 nominal masses measured by GC-MS and averaged across a chromatographic profile. Ignitable liquids are labeled using the American Society for Testing and Materials (ASTM) E1618 standard class definitions. Statistical analysis is performed in the class-conditional feature space wherein new forensic traces are represented based on their likeness to known samples contained in a forensic reference collection. The demonstrated method uses forensic reference data as the basis of probabilistic statements concerning the likelihood of the obtained analytical results given the presence of ignitable liquid residue of each of the ASTM classes (including a substrate only class). When prior probabilities of these classes can be assumed, these likelihoods can be connected to class probabilities. In order to compare the performance of this method to previous work, a uniform prior was assumed, resulting in an 81% accuracy for an independent test of 129 real burn samples

Untargeted Comprehensive Two-Dimensional Liquid Chromatography Coupled with High-Resolution Mass Spectrometry Analysis of Rice Metabolome Using Multivariate Curve Resolution

In this work, a new strategy for the chemometric analysis of two-dimensional liquid chromatography-high-resolution mass spectrometry (LC × LC-HRMS) data is proposed. This approach consists of a preliminary compression step along the mass spectrometry (MS) spectral dimension based on the selection of the regions of interest (ROI), followed by a further data compression along the chromatographic dimension by wavelet transforms. In a secondary step, the multivariate curve resolution alternating least squares (MCR-ALS) method is applied to previously compressed data sets obtained in the simultaneous analysis of multiple LC × LC-HRMS chromatographic runs from multiple samples. The feasibility of the proposed approach is demonstrated by its application to a large experimental data set obtained in the untargeted LC × LC-HRMS study of the effects of different environmental conditions (watering and harvesting time) on the metabolism of multiple rice samples. An untargeted chromatographic setup coupling two different liquid chromatography (LC) columns [hydrophilic interaction liquid chromatography (HILIC) and reversed-phase liquid chromatography (RPLC)] together with an HRMS detector was developed and applied to analyze the metabolites extracted from rice samples at the different experimental conditions. In the case of the metabolomics study taken as example in this work, a total number of 154 metabolites from 15 different families were properly resolved after the application of MCR-ALS. A total of 139 of these metabolites could be identified by their HRMS spectra. Statistical analysis of their concentration changes showed that both watering and harvest time experimental factors had significant effects on rice metabolism. The biochemical insight of the effects of watering and harvesting experimental factors on the changes in concentration of these detected metabolites in the investigated rice samples is attempted. © 2017 American Chemical Society.The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement No. 320737. The authors would like to thank CRAG for kindly supplying Japanese rice seeds. CTQ2015- 66254-C2-1-P project from MINCO (Spain) is also acknowledged.Peer reviewe

Detection and Characterization of Ignitable Liquid Residues in Forensic Fire Debris Samples by Comprehensive Two-Dimensional Gas Chromatography

This study covers an extensive experimental design that was developed for creating simulated fire debris samples under controlled conditions for the detection and identification of ignitable liquids (IL) residues. This design included 19 different substrates, 45 substrate combinations with and without ignitable liquids, and 45 different ILs from three classes (i.e., white spirit, gasoline, and lamp oil). Chemical analysis was performed with comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC&times;GC-TOFMS) for improved separation and compound identification. The enhanced peak capacity offered by GC&times;GC-TOFMS allowed the use of a target compound list in combination with a simple binary decision model to arrive at quite acceptable results with respect to IL detection (89% true positive and 7% false positive rate) and classification (100% correct white spirit, 79% correct gasoline, and 77% correct lamp oil assignment). Although these results were obtained in a limited set of laboratory controlled fire experiments including only three IL classes, this study confirms the conclusions of other studies that GC&times;GC-TOFMS can be a powerful tool in the challenging task of forensic fire debris analysis