An Open Software Development-based Ecosystem of R Packages for Metabolomics Data Analysis
Creators: name: Vicini, Andrea
name: Gine, Roger
name: Stravs, Michele
name: Badia, Josep M
name: Huber, Carolin
name: Salzer, Liesa
name: Stanstrup, Jan
name: Shahaf, Nir
name: Naake, Thomas
name: Hecht, Helge
name: Neumann, Steffen
name: Witting, Michael
name: Gibb, Sebastian
name: Gatto, Laurent
name: Rainer, Johannes
Publisher: name: Zenodo
Year: 2023
Subjects: Metabolomics
Mass Spectrometry
Open Software Development
Annotation

description: 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 <code>Spectra</code> 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 <code>MsCoreUtils</code> and <code>MetaboCoreUtils</code> packages. Version 4 of the <code>xcms</code> 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 <code>xcms</code> package into the package ecosystem simplifies complete analysis workflows which can include the <code>MsFeatures</code> package for feature grouping, and the <code>MetaboAnnotation</code> package for annotation of untargeted metabolomics data. Seamless integration of publicly available annotation resources is possible through packages such as <code>MsBackendMassbank</code>, <code>MsBackendMsp</code> or <code>CompoundDb</code>, the latter also allowing to create and manage lab-specific annotation resources. <code>MsQuality</code> enables rapid, efficient, and standardized quality assessment of MS data. Finally, integration of Python based functionality, such as provided by the <code>matchms</code> package, is possible through the <code>SpectriPy</code> package, and the <code>SpectraQL</code> adds support for the MassQL common query language to R/<code>Spectra</code>.
description: contact: johannes.rainer@eurac.edu
description: {"references": ["Naake, Thomas et al (2023). MsQuality \u2013 an interoperable open-source package for the calculation of standardized quality metrics of mass spectrometry data. bioRxiv https://doi.org/10.1101/2023.05.12.540477", "Rainer, Johannes et al. (2022). A Modular and Expandable Ecosystem for Metabolomics Data Annotation in R. Metabolites. https://doi.org/10.3390/metabo12020173", "Kockmann, Thomas et al. (2021). The rawrr R Package: Direct Access to Orbitrap Data and Beyond. Journal of Proteome Research. https://doi.org/10.1021/acs.jproteome.0c00866", "Huber, Florian et al. (2020). matchms \u2013 processing and similarity evaluation of mass spectrometry data. JOSS https://doi.org/10.21105/joss.02411", "Jarmusch, Alan K et al. (2022). A Universal Language for Finding Mass Spectrometry Data Patterns. bioRxiv https://doi.org/10.1101/2022.08.06.503000"]}