Year: 2023
Type: conference-paper
Abstract: 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 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 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 remote data access. The xcms package for LC-MS data preprocessing was updated to reuse this infrastructure, enabling now also the analysis of very large, or remote, data. This integration simplifies in addition complete analysis workflows which can include the MsFeatures package for compounding, and the MetaboAnnotation package for annotation of untargeted metabolomics experiments. Public annotation resources can be easily accessed through packages such as MsBackendMassbank, MsBackendMgf, 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 development will focus on improved data structures and analysis methods for chromatographic data, and better interoperability with other open source softwares including a direct integration with Python MS libraries.
Authors: Rainer, Johannes, Louail, Philippine, Vicini, Andrea, Gine, Roger, Badia, Josep M, Stravs, Michele, Garcia-Aloy, Mar, Huber, Carolin, Salzer, Liesa, Stanstrup, Jan, Shahaf, Nir, Panse, Christian, Naake, Thomas, Kumler, William, Vangeenderhuysen, Pablo, Brunius, Carl, Hecht, Helge, Neumann, Steffen, Witting, Michael, Gibb, Sebastian, Gatto, Laurent
Institutions: Eurac Research, Institute for Biomedicine, de Duve Institute, UCLouvain, Universitat Rovira i Virgili, Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red Diabetes y Enfermedades Metabólicas Asociadas, ETH Zurich, Institute for Molecular Systems Biology, Fondazione Edmund Mach, Helmholtz Centre for Environmental Research, Helmholtz Munich, University of Copenhagen, Weizmann Institute of Science, SIB Swiss Institute of Bioinformatics, University of Zurich, Functional Genomics Center Zurich, European Molecular Biology Laboratory, European Molecular Biology Laboratory, University of Washington, Ghent University, Chalmers University of Technology, Masaryk University, RECETOX, Leibniz Institute of Plant Biochemistry, Universitätsmedizin Greifswald, Universität Greifswald
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W6950557488