ArticleBMC bioinformatics2012
PyMS: a Python toolkit for processing of gas chromatography-mass spectrometry (GC-MS) data. Application and comparative study of selected tools.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
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- Mating strategy does not affect the diversification of abdominal chemicals in Heliconiini butterflies.Chemoecology · 2025Article
- Complexity of Chemical Emissions Increases Concurrently with Sexual Maturity in Heliconius Butterflies.Journal of chemical ecology · 2024Article
- AdductHunter: identifying protein-metal complex adducts in mass spectra.Journal of cheminformatics · 2024Article
- Loss of attachment promotes proline accumulation and excretion in cancer cells.Science advances · 2023Article
- Three Chemically Distinct Floral Ecotypes inPlants (Basel, Switzerland) · 2022Article
- Optimization of metabolomic data processing using NOREVA.Nature protocols · 2022Review
- Defining Blood Plasma and Serum Metabolome by GC-MS.Metabolites · 2021Review
- Characterization of terpene biosynthesis inPlant-environment interactions (Hoboken, N.J.) · 2021Article
- EirA Is a Novel Protein Essential for Intracellular Replication of Coxiella burnetii.Infection and immunity · 2020Article
- QUICK: Quality and Usability Investigation and Control Kit for Mass Spectrometric Data from Detection of Persistent Organic Pollutants.International journal of environmental research and public health · 2019Article
- A low-cost, open-source digital stripchart recorder for chromatographic detectors using a Raspberry Pi.Journal of chromatography. A · 2019Article
- WiPP: Workflow for Improved Peak Picking for Gas Chromatography-Mass Spectrometry (GC-MS) Data.Metabolites · 2019Article
- Review of recent developments in GC-MS approaches to metabolomics-based research.Metabolomics : Official journal of the Metabolomic Society · 2018Review
- DExSI: a new tool for the rapid quantitation of 13C-labelled metabolites detected by GC-MS.Bioinformatics (Oxford, England) · 2018Article
- Automating data analysis for two-dimensional gas chromatography/time-of-flight mass spectrometry non-targeted analysis of comparative samples.Journal of chromatography. A · 2018Article
- Metabolomic Profiles of a Midge (Procladius villosimanus, Kieffer) Are Associated with Sediment Contamination in Urban Wetlands.Metabolites · 2017Article
- From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics.GigaScience · 2017Review
- A NEW METHOD OF PEAK DETECTION FOR ANALYSIS OF COMPREHENSIVE TWO-DIMENSIONAL GAS CHROMATOGRAPHY MASS SPECTROMETRY DATA.The annals of applied statistics · 2014Article
- Computational mass spectrometry for small molecules.Journal of cheminformatics · 2013Article
Corrections and comments
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Authors and funding
13 authors.
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Abstract
backgroundGas chromatography-mass spectrometry (GC-MS) is a technique frequently used in targeted and non-targeted measurements of metabolites. Most existing software tools for processing of raw instrument GC-MS data tightly integrate data processing methods with graphical user interface facilitating interactive data processing. While interactive processing remains critically important in GC-MS applications, high-throughput studies increasingly dictate the need for command line tools, suitable for scripting of high-throughput, customized processing pipelines.
resultsPyMS comprises a library of functions for processing of instrument GC-MS data developed in Python. PyMS currently provides a complete set of GC-MS processing functions, including reading of standard data formats (ANDI- MS/NetCDF and JCAMP-DX), noise smoothing, baseline correction, peak detection, peak deconvolution, peak integration, and peak alignment by dynamic programming. A novel common ion single quantitation algorithm allows automated, accurate quantitation of GC-MS electron impact (EI) fragmentation spectra when a large number of experiments are being analyzed. PyMS implements parallel processing for by-row and by-column data processing tasks based on Message Passing Interface (MPI), allowing processing to scale on multiple CPUs in distributed computing environments. A set of specifically designed experiments was performed in-house and used to comparatively evaluate the performance of PyMS and three widely used software packages for GC-MS data processing (AMDIS, AnalyzerPro, and XCMS).
conclusionsPyMS is a novel software package for the processing of raw GC-MS data, particularly suitable for scripting of customized processing pipelines and for data processing in batch mode. PyMS provides limited graphical capabilities and can be used both for routine data processing and interactive/exploratory data analysis. In real-life GC-MS data processing scenarios PyMS performs as well or better than leading software packages. We demonstrate data processing scenarios simple to implement in PyMS, yet difficult to achieve with many conventional GC-MS data processing software. Automated sample processing and quantitation with PyMS can provide substantial time savings compared to more traditional interactive software systems that tightly integrate data processing with the graphical user interface.
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