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

S O'Callaghan; DP De Souza; A Isaac; Q Wang; L Hodkinson; M Olshansky; T Erwin; B Appelbe; DL Tull; U Roessner; A Bacic; MJ McConville; VA Likić

Journal article

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

S O'Callaghan, DP De Souza, A Isaac, Q Wang, L Hodkinson, M Olshansky, T Erwin, B Appelbe, DL Tull, U Roessner, A Bacic, MJ McConville, VA Likić

BMC Bioinformatics | BMC | Published : 2012

DOI: 10.1186/1471-2105-13-115

Download PDF

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..

View full abstract

University of Melbourne Researchers

David de Souza Author

Andrew Isaac Author

Malcolm McConville Author

Related Projects (2)

Metabolomic analysis of Leishmania

Leishmania are single-celled parasites that target a major class of immune cell, affecting millions and killing thousands of people worldwid..

Characterization of metabolic networks in a microbial pathogen

New methods are needed to understand complex cellular processes such as metabolism. This proposal will support the development of methods in..

Grants

Awarded by University of Melbourne

Funding Acknowledgements

The authors are grateful to the Victorian Node of Metabolomics Australia which is funded through Bioplatforms Australia Pty Ltd, a National Collaborative Research Infrastructure Strategy, 5.1 Evolving Biomolecular Platforms and Informatics investment and co-investment from the Victorian State government and The University of Melbourne UR thanks the Australian Centre for Plant Functional Genomics which is funded by grants from the Australian Research Council (ARC) and the Grains Research and Development Corporation, the South Australian Government, and the University of Adelaide, the University of Queensland, and The University of Melbourne. AB acknowledges the support of the ARC Centre of Excellence in Plant Cell Walls. MJM is a National Health and Medical Research Council (NHMRC) Principal Research Fellow. MJM and VAL acknowledge funding from the NHMRC Project Grant 1006023 and the ARC Discovery Grant DP0878227.