Abstract
Lack of support, maintenance and further development is common with scientific research software. In particular, development by a single researcher can easily result in orphaned software packages, especially if combined with poor documentation or lack of adherence to open software development standards.
The RforMassSpectrometry initiative aims to develop an efficient, thoroughly documented and stable infrastructure for mass spectrometry (MS) data analysis. As part of this initiative, a growing ecosystem of R software packages was and is being developed covering different aspects of metabolomics and proteomics data analysis. To avoid the aforementioned problems open shared development, documentation, support and stability are emphasized.
At the heart of the package ecosystem is the Spectra package, that provides the core infrastructure to handle MS data. Core functionality, which can be easily re-used by other R software packages, is provided by the MsCoreUtils and MetaboCoreUtils packages. Version 4 of the xcms package for LC-MS data pre-processing is now based mainly on this new infrastructure hence gaining support for additional data types, better data handling and support for ion mobility data. Integration of the xcms package into the package ecosystem simplifies complete analysis workflows which can include the MsFeatures package for feature grouping, and the MetaboAnnotation package for annotation of untargeted metabolomics data. Seamless integration of publicly available annotation resources is possible through packages such as MsBackendMassbank, MsBackendMsp or CompoundDb, the latter also allowing to create and manage lab-specific annotation resources. MsQuality enables rapid, efficient, and standardized quality assessment of MS data.
Finally, integration of Python based functionality, such as provided by the matchms package, is possible through the SpectriPy package, and the SpectraQL adds support for the MassQL common query language to R/Spectra.