A KNIME Pipeline for the Analysis of GC-MS Data in Metabolomics

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.