PyMS: A Python toolkit for processing of gas chromatography–mass spectrometry (GC-MS) data. Application and comparative study of selected tools. O'Callaghan, S., Desouza, D. P, Isaac, A., Wang, Q., Hodkinson, L., Olshansky, M., Erwin, T., Appelbe, B., Tull, D. L, Roessner, U., Bacic, A., McConville, M. J, & Likic, V. A BMC Bioinf, 13(1):115, 2012.
doi  abstract   bibtex   
ABSTRACT: BACKGROUND: Gas 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. RESULTS: PyMS 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 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). We show data processing scenarios simple to implement in PyMS, yet difficult to achieve with many conventional GC-MS data processing software. CONCLUSIONS: PyMS 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. We show that in real-life GC-MS data processing scenarios PyMS performs as well or better than leading software packages. Automated sample processing and quantitation by PyMS provides substantial time savings compared to more traditional software systems that tightly integrate data processing with the graphical user interface.
@Article{ocallaghan12pyms,
  author    = {Sean O'Callaghan and David P Desouza and Andrew Isaac and Qiao Wang and Luke Hodkinson and Moshe Olshansky and Tim Erwin and Bill Appelbe and Dedreia L Tull and Ute Roessner and Antony Bacic and Malcolm J McConville and Vladimir A Likic},
  title     = {{PyMS}: A {Python} toolkit for processing of gas chromatography--mass spectrometry ({GC-MS}) data. {Application} and comparative study of selected tools},
  journal   = {BMC Bioinf},
  year      = {2012},
  volume    = {13},
  number    = {1},
  pages     = {115},
  abstract  = {ABSTRACT: BACKGROUND: Gas 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. RESULTS: PyMS 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 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). We show data processing scenarios simple to implement in PyMS, yet difficult to achieve with many conventional GC-MS data processing software. CONCLUSIONS: PyMS 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. We show that in real-life GC-MS data processing scenarios PyMS performs as well or better than leading software packages. Automated sample processing and quantitation by PyMS provides substantial time savings compared to more traditional software systems that tightly integrate data processing with the graphical user interface.},
  doi       = {10.1186/1471-2105-13-115},
  file      = {OCallaghanEtAl_PyMSPythonToolkit_BMCBioinformatics_2012.pdf:2012/OCallaghanEtAl_PyMSPythonToolkit_BMCBioinformatics_2012.pdf:PDF},
  keywords  = {TrACReview; ms metabolites; mass spectrometry; GCMS},
  optmonth  = may,
  owner     = {fhufsky},
  pmid      = {22647087},
  timestamp = {2012.07.26},
}

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