RMet: An automated R based software for analyzing GC-MS and GC×GC-MS untargeted metabolomic data

Moayedpour, Saeed; Parastar, Hadi

doi:10.1016/j.chemolab.2019.103866

Cited by 14 publications

(5 citation statements)

References 23 publications

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“…Only very recently have a small number of FOSS GC×GC data tools been published that attempt to offer an end-to-end workflow or pipeline that competes with commercial software functionality for large-scale datasets (Table S1). − However, these tools often still require preprocessing within the vendor software before being imported. The user also does not benefit from the powerful proprietary functions developed within commercial software.…”

Section: Resultsmentioning

confidence: 99%

Automating and Extending Comprehensive Two-Dimensional Gas Chromatography Data Processing by Interfacing Open-Source and Commercial Software

et al. 2020

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Comprehensive two-dimensional gas chromatography (GC×GC) is a powerful analytical tool for both nontargeted and targeted analyses. However, there is a need for more integrated workflows for processing and managing the resultant highcomplexity datasets. End-to-end workflows for processing GC×GC data are challenging and often require multiple tools or software to process a single dataset. We describe a new approach, which uses an existing underutilized interface within commercial software to integrate free and open-source/external scripts and tools, tailoring the workflow to the needs of the individual researcher within a single software environment. To demonstrate the concept, the interface was successfully used to complete a firstpass alignment on a large-scale GC×GC metabolomics dataset. The analysis was performed by interfacing bespoke and published external algorithms within a commercial software environment to automatically correct the variation in retention times captured by a routine reference standard. Variation in 1 t R and 2 t R was reduced on average from 8 and 16% CV prealignment to less than 1 and 2% post alignment, respectively. The interface enables automation and creation of new functions and increases the interconnectivity between chemometric tools, providing a window for integrating data-processing software with larger informatics-based data management platforms.

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Section: Resultsmentioning

confidence: 99%

Automating and Extending Comprehensive Two-Dimensional Gas Chromatography Data Processing by Interfacing Open-Source and Commercial Software

et al. 2020

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“…Previous open software performing peak identification such as TargetSearch, uses ion extraction for peak finding [ 41 ] while in gcProfileMakeR, this is performed by the GC/MS software with the NIST library and CAS numbers are used. The RMet package [ 42 ] uses a different processes such as segmentation to reduce unwanted peaks and defines the total number of metabolites. Importantly, RMet and TargetSearch give as output a list of total metabolites for a single sample, while gcProfileMakeR creates profiles based on large sets of samples (see results).…”

Section: Discussionmentioning

confidence: 99%

gcProfileMakeR: An R Package for Automatic Classification of Constitutive and Non-Constitutive Metabolites

et al. 2021

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Metabolomes comprise constitutive and non-constitutive metabolites produced due to physiological, genetic or environmental effects. However, finding constitutive metabolites and non-constitutive metabolites in large datasets is technically challenging. We developed gcProfileMakeR, an R package using standard Excel output files from an Agilent Chemstation GC-MS for automatic data analysis using CAS numbers. gcProfileMakeR has two filters for data preprocessing removing contaminants and low-quality peaks. The first function NormalizeWithinFiles, samples assigning retention times to CAS. The second function NormalizeBetweenFiles, reaches a consensus between files where compounds in close retention times are grouped together. The third function getGroups, establishes what is considered as Constitutive Profile, Non-constitutive by Frequency i.e., not present in all samples and Non-constitutive by Quality. Results can be plotted with the plotGroup function. We used it to analyse floral scent emissions in four snapdragon genotypes. These included a wild type, Deficiens nicotianoides and compacta affecting floral identity and RNAi:AmLHY targeting a circadian clock gene. We identified differences in scent constitutive and non-constitutive profiles as well as in timing of emission. gcProfileMakeR is a very useful tool to define constitutive and non-constitutive scent profiles. It also allows to analyse genotypes and circadian datasets to identify differing metabolites.

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“…Using the chocolate data set, Weggler et al demonstrated that both the number of peaks aligned in the final peak tables and clustering on the PCA scores plot differed among eight commercial software packages for GC×GC data analysis . Because it is difficult to ascertain the differences in proprietary vendor software, there has been increased interest in developing free and open-source software (FOSS). − With FOSS, the user can see exactly how the software treats their data and make changes to the software if necessary. FOSS methods provide access to various preprocessing (e.g., baseline correction and alignment) and nontargeted chemometric methods (e.g., PCA, clustering algorithms, and PLS-DA) for either peak-table or pixel-based approaches. − Another approach is to use functionalities in several commercial software packages (such as ChromaTOF and GC Image) for the user to integrate custom scripting in a “hybrid” approach.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

“…Because it is difficult to ascertain the differences in proprietary vendor software, there has been increased interest in developing free and open-source software (FOSS). − With FOSS, the user can see exactly how the software treats their data and make changes to the software if necessary. FOSS methods provide access to various preprocessing (e.g., baseline correction and alignment) and nontargeted chemometric methods (e.g., PCA, clustering algorithms, and PLS-DA) for either peak-table or pixel-based approaches. − Another approach is to use functionalities in several commercial software packages (such as ChromaTOF and GC Image) for the user to integrate custom scripting in a “hybrid” approach. An exemplary report by Wilde et al described strategies for using this approach to create tailored chemometric workflows for GC×GC data analysis .…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Recent Advances in GC×GC and Chemometrics to Address Emerging Challenges in Nontargeted Analysis

et al. 2023

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RMet: An automated R based software for analyzing GC-MS and GC×GC-MS untargeted metabolomic data

Cited by 14 publications

References 23 publications

Automating and Extending Comprehensive Two-Dimensional Gas Chromatography Data Processing by Interfacing Open-Source and Commercial Software

Automating and Extending Comprehensive Two-Dimensional Gas Chromatography Data Processing by Interfacing Open-Source and Commercial Software

gcProfileMakeR: An R Package for Automatic Classification of Constitutive and Non-Constitutive Metabolites

Recent Advances in GC×GC and Chemometrics to Address Emerging Challenges in Nontargeted Analysis

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