Abstract
Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system using analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. We provide an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also covered. We also address the Findability, Accessibility, Interoperability and Reusability of software, and how these can be improved through repositories and semantic annotation, and now maintain the resource as an Open Source book with Continuous Integration as part of the RforMassSpectrometry initiative, which aims to provide efficient, thoroughly documented, tested and flexible R software for the analysis and interpretation of high throughput mass spectrometry data.