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A KNIME Pipeline for the Analysis of GC-MS Data in Metabolomics
Poster Title: A KNIME Pipeline for the Analysis of GC-MS Data in Metabolomics
Submitted on 07 Dec 2015
Author(s): Sonia Liggi1, Maria Laura Santoru1, Cristina Piras1, Antonio Murgia2, Pierluigi Caboni2, Luigi Atzori1
Affiliations: 1Department of Biomedical Sciences, Section of Pathology, University of Cagliari, Italy; 2Department of Life and Environmental Sciences, High Resolution Mass Spectrometry Laboratory, University of Cagliari, Italy
This poster was presented at Metabomeeting 2015
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Poster Information
Abstract: Elucidation of the metabolic changes taking place in pathological conditions can help in the identification of new biomarkers, prediction of response to therapy and better understanding of the pathogenesis [1]. Gas Chromatography coupled with Mass Spectrometry (GC-MS) is one of the leading analytical techniques utilised to deconvolute the metabolic profile of biofluids and tissues. However, the large number of experiments deriving from high-throughput studies along with the complex set of steps required to pre-process and analyse the results obtained from GC-MS measurements represents a bottleneck. Indeed, several programs need to be used to accomplish a number of tasks (namely retention time correction, peak extraction, metabolites deconvolution, blanks removal, normalisation and last but not least statistical analysis), requiring computational competences and resources not always present in an experimental group. In this context, the KNIME Analytics Platform [2] was used to develop a pipeline joining the GC-MS pre-processing R [3] library XCMS [4], in-house Python scripts and KNIME functionalities to perform the aforementioned steps even by users unfamiliar with programming. Here, the pipeline was utilised to obtain a matrix of all the signals found in the chromatograms of samples deriving from patients affected by Inflammatory Bowel Diseases.Summary: A KNIME pipeline was developed to perform pre-processing of GC-MS data in an automated way and applied to a Inflammatory bowel diseases case study References: 1. R. Madsen, T. Lundstedt, J. Trygg, Chemometrics in metabolomics—A review in human disease diagnosis, Analytica Chimica Acta, 659 (2010) 23–33
2. M.R. Berthold, N. Cebron, F. Dill, T.R. Gabriel, T. Kotter, T. Meinl, P. Ohl, C. Sieb, K. Thiel, B. Wiswedel, KNIME: The Konstanz Information Miner, Studies in Classification, Data Analysis, and Knowledge Organization (GfKL 2007), Springer, ISBN 978-3-540-78239-1, ISSN 1431-8814, 2007
3. R Core Team (2015), R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL
4. C.A. Smith, E.J. Want, G. O'Maille, R. Abagyan, G. Siuzdak XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification Analytical Chemistry, 78 (2006), 779–787
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