PyMS: a Python toolkit for processing of gas chromatography-mass spectrometry (GC-MS) data. Application and comparative study of selected tools

O’Callaghan, Sean; Souza, David P. De; Isaac, Andrew P.; Wang, Qiao; Hodkinson, Luke; Olshansky, Moshe; Erwin, Tim; Appelbe, Bill; Tull, Dedreia; Roessner, Ute; Bacic, Antony; McConville, Malcolm J.; Likić, Vladimir A

doi:10.1186/1471-2105-13-115

Cited by 70 publications

(36 citation statements)

References 75 publications

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“…Metab [237] is an R package that automates the pipeline for analysis of metabolomics GC-MS datasets processed by AMDIS . PyMS [238] comprises several functions for processing raw GC-MS data, such as noise smoothing, baseline correction, peak detection, peak deconvolution, peak integration, and peak alignment. ADAP-GC 2.0 [239] helps with the deconvolution of coeluting metabolites, aligns components across samples and exports their qualitative and quantitative information.…”

Section: Software Packagesmentioning

confidence: 99%

Computational mass spectrometry for small molecules

2013

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The identification of small molecules from mass spectrometry (MS) data remains a major challenge in the interpretation of MS data. This review covers the computational aspects of identifying small molecules, from the identification of a compound searching a reference spectral library, to the structural elucidation of unknowns. In detail, we describe the basic principles and pitfalls of searching mass spectral reference libraries. Determining the molecular formula of the compound can serve as a basis for subsequent structural elucidation; consequently, we cover different methods for molecular formula identification, focussing on isotope pattern analysis. We then discuss automated methods to deal with mass spectra of compounds that are not present in spectral libraries, and provide an insight into de novo analysis of fragmentation spectra using fragmentation trees. In addition, this review shortly covers the reconstruction of metabolic networks using MS data. Finally, we list available software for different steps of the analysis pipeline.

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Section: Software Packagesmentioning

confidence: 99%

Computational mass spectrometry for small molecules

2013

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“…Instead, this is commonly achieved by using one of a number of software options on the market e.g. XCMS [5], metaMS [6], MetAlign [7], mzMine [8], ADAP-GC [9,10], PyMS [11] and eRah [12].…”

Section: Introductionmentioning

confidence: 99%

WiPP: Workflow for improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) data

Borgsmüller

Gloaguen

Opialla

et al. 2019

Preprint

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High false positive rates in GC-MS metabolomics peak detection is a common issue that impedes automated analysis of large-scale datasets. There is a growing need for improving the reliability and scalability of data analysis workflows. Many algorithms are available for peak detection [1], a crucial step for the data analysis, but performance and outcome can differ widely depending on both algorithmic approach and data acquisition method. This makes it difficult to compare and contrast between algorithms without extensive manual intervention.We present a workflow for improved peak picking (WiPP), a parameter optimizing, multi-algorithm peak detection workflow for GC-MS metabolomics, which automatically evaluates the quality of detected peaks using machine learning-based classification. First, the classifier is trained to distinguish between real compound related peaks and false positive peaks. Then the algorithm parameters are scored based on the quality of detected peaks and optimized accordingly. This procedure is repeated for two peak detection algorithms and subsequently both algorithms are run in parallel on the entire data set with the optimized parameters. The qualitative information returned by the classifier for every peak is then used to merge individual algorithm results into one final high confidence peak set.Using this approach, we show that automated detection and evaluation of peak quality is improved. The additional quantitative and qualitative information generated by the classifier allows: 1. a novel way to classify peaks based on seven classes and thus objectively to assess their quality 2. impartial performance comparison of different peak picking algorithms 3. automated parameter optimization for each individual peak picking algorithm 4. a final, improved high quality peak list to be generated for statistical or further analyses.It achieves this while minimising the operator-time required by packaging this within a fully automated workflow. The modular design allows extension, adjustment and improvement of the workflow using different or additional peak detection algorithms and classifiers. Importantly, due to the fully automated implementation, the workflow is suitable for large-scale studies.The pipeline supports mzML, mzData and NetCDF formats and is implemented in python using snakemake, a reproducible and scalable workflow management system, it is available on GitHub (https://github.com/bihealth/WiPP).

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“…Ruby, JavaScript, Groovy, Clojure, and the aforementioned Jython are supported, but these languages are little used in and somewhat limited for bioinformatics. Jython, for instance, is unable to run NumPy , “the fundamental package for scientific computing with Python” [14] or to call out to other compiled code.…”

Section: Introductionmentioning

confidence: 99%

RCytoscape: tools for exploratory network analysis

et al. 2013

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BackgroundBiomolecular pathways and networks are dynamic and complex, and the perturbations to them which cause disease are often multiple, heterogeneous and contingent. Pathway and network visualizations, rendered on a computer or published on paper, however, tend to be static, lacking in detail, and ill-equipped to explore the variety and quantities of data available today, and the complex causes we seek to understand.ResultsRCytoscape integrates R (an open-ended programming environment rich in statistical power and data-handling facilities) and Cytoscape (powerful network visualization and analysis software). RCytoscape extends Cytoscape's functionality beyond what is possible with the Cytoscape graphical user interface. To illustrate the power of RCytoscape, a portion of the Glioblastoma multiforme (GBM) data set from the Cancer Genome Atlas (TCGA) is examined. Network visualization reveals previously unreported patterns in the data suggesting heterogeneous signaling mechanisms active in GBM Proneural tumors, with possible clinical relevance.ConclusionsProgress in bioinformatics and computational biology depends upon exploratory and confirmatory data analysis, upon inference, and upon modeling. These activities will eventually permit the prediction and control of complex biological systems. Network visualizations -- molecular maps -- created from an open-ended programming environment rich in statistical power and data-handling facilities, such as RCytoscape, will play an essential role in this progression.

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PyMS: a Python toolkit for processing of gas chromatography-mass spectrometry (GC-MS) data. Application and comparative study of selected tools

Cited by 70 publications

References 75 publications

Computational mass spectrometry for small molecules

Computational mass spectrometry for small molecules

WiPP: Workflow for improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) data

RCytoscape: tools for exploratory network analysis

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