An Open Software Development-based Ecosystem of R Packages for Metabolomics Data Analysis
Author(s): Johannes Rainer,RforMassSpectrometry contributors
Affiliation(s): Institute for Biomedicine, Eurac Research, Bolzano, Italy
A frequent problem with scientific research software is the lack of support, maintenance and further development. In particular, development by a single researcher can easily result in orphaned and dysfunctional software packages, especially if combined with poor documentation, missing unit tests or lack of adherence to open software development standards. The RforMassSpectrometry initiative aims to develop an efficient, scalable, and stable infrastructure for mass spectrometry (MS) data analysis. As part of this initiative, a growing ecosystem of R software packages is being developed covering different aspects of metabolomics and proteomics data analysis. To avoid the aforementioned problems, community contributions are fostered, and open development, documentation and long-term support emphasized. At the heart of the package ecosystem is the Spectra package that provides the core infrastructure to handle and analyze MS data. Its design allows easy expansion to support additional file or data formats including data representations with minimal memory footprint or also remote data access. For metabolomics data analysis, xcms is one of the core software packages for the required preprocessing of LC-MS data. This Bioconductor package was recently updated to reuse the RforMassSpectrometry infrastructure, enabling now also the analysis of very large, or remote, data. This integration simplifies in addition complete analysis workflows which can include functionality from the MsFeatures package for compounding, and from the MetaboAnnotation package facilitating annotation of untargeted metabolomics experiments. Public annotation resources can be easily accessed through packages such as MsBackendMassbank, MsBackendMsp or CompoundDb, the latter also allowing to create and manage lab-specific compound databases. Finally, the MsCoreUtils and MetaboCoreUtils packages provide efficient implementations of commonly used algorithms, designed to be re-used in other R packages. Ultimately, and in contrast to a monolithic software design, the package ecosystem enables to build customized, modular, and reproducible analysis workflows. Future metabolomics-related development will focus on improved data structures and analysis methods for chromatographic data, and better interoperability with other open source software including a direct integration with Python MS libraries.