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

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• 作者：Sean O’Callaghan (1) (2)
  David P De Souza (1) (2)
  Andrew Isaac (3)
  Qiao Wang (4)
  Luke Hodkinson (5)
  Moshe Olshansky (6)
  Tim Erwin (7)
  Bill Appelbe (8)
  Dedreia L Tull (1) (2)
  Ute Roessner (2) (7)
  Antony Bacic (1) (2) (9)
  Malcolm J McConville (1) (10) (2)
  Vladimir A Liki? (1) (2)
  
• 刊名：BMC Bioinformatics
• 出版年：2012
• 出版时间：December 2012
• 年：2012
• 卷：13
• 期：1
• 全文大小：708KB
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• 作者单位：Sean O’Callaghan (1) (2)
  David P De Souza (1) (2)
  Andrew Isaac (3)
  Qiao Wang (4)
  Luke Hodkinson (5)
  Moshe Olshansky (6)
  Tim Erwin (7)
  Bill Appelbe (8)
  Dedreia L Tull (1) (2)
  Ute Roessner (2) (7)
  Antony Bacic (1) (2) (9)
  Malcolm J McConville (1) (10) (2)
  Vladimir A Liki? (1) (2)
  
  1. Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Victoria, 3010, Australia
  2. Metabolomics Australia, Bio21 Institute, The University of Melbourne, Parkville, Victoria, 3010, Australia
  3. Victorian Life Science Computational Initiative, The University of Melbourne, Parkville, Victoria, 3010, Australia
  4. National ICT Australia (NICTA), The University of Melbourne, Parkville, Victoria, 3010, Australia
  5. Centre for Astrophysics & Supercomputing, Swinburne University of Technology, Hawthorn, Victoria, 3122, Australia
  6. Walter and Eliza Hall Institute of Medical Research, 1 G Royal Parade, Parkville, Victoria, 3052, Australia
  7. Australian Centre for Plant and Functional Genomics, School of Botany, The University of Melbourne, Parkville, Victoria, 3010, Australia
  8. Victorian Partnership for Advanced Computing, 110 Victoria Street, Carlton South, Victoria, 3053, Australia
  9. ARC Centre of Excellence in Plant Cell Walls, School of Botany, The University of Melbourne, Parkville, Victoria, 3010, Australia
  10. Department of Biochemistry and Molecular Biology, The University of Melbourne, Parkville, Victoria, 3010, Australia
  
• ISSN：1471-2105

文摘

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 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). 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. 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.