This book provides a snapshot of currently available open-source software (OSS) for the analysis of mass spectrometry (MS) data, with a primary focus on metabolomics and proteomics. Different tools can be modified and reshuffled to customise workflows to best suit individual needs. The ongoing development and integration of analytical methods, such as combined LC-MS/NMR metabolomics, provides new challenges for data handling and understanding.¹ 

The evaluation of multidimensional datasets, including (for example) ion mobility separation (IMS), different types of MS fragmentation, and NMR data, and the merging of different levels of information, such as those derived from genomics, metabolomics, proteomics, and phenomics, are becoming severe computational problems. To generate meaningful knowledge rather than just ‘big data’, it is therefore crucial to (a) strictly apply the criteria of The Scientific Method,²  and (b) employ advanced statistics and data mining strategies. Supervised and unsupervised machine learning algorithms will play key roles in dealing with complex tasks, such as predicting chemical structure from MS data and interpreting massive omics datasets.^3,4