@INBOOK{IPB-2456, author = {Neumann, S. and Yanes, O. and Mumm, R. and Franceschi, P.}, title = {{Metabolomics: Practical Guide to Design and Analysis}}, year = {2019}, chapter = {{Mass Spectrometry Data Processing}}, editor = {Wehrens, R. \& Salek, R., eds.}, doi = {10.1201/9781315370583-4}, url = {https://dx.doi.org/10.1201/9781315370583-4}, abstract = {The chapter “Mass Spectrometry Data Processing” focuses on the mass spectrometry data processing workflow. The first step consists of processing the raw MS data using conversion of vendor formats to open standards, followed by feature detection, optionally retention time correction and grouping of features across samples leading to a feature matrix amenable for statistical analysis. The metabolomics community has developed several open source software packages capable of processing large-scale data commonly occurring in metabolomics studies. In the second stage, features of interest are identified, i.e., annotated with names of metabolites, or compound classes. Tandem MS or LC-MS/MS fragmentation data provides structural hints. The MS/MS spectra can be used to search in open and commercial spectral libraries. If no reference spectra are available, in-silico annotation tools or more recently machine learning approaches can be used.} }