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Journal article
MStargetR: a reproducible, containerised workflow for end-to-end targeted (MRM/SRM) mass spectrometry data processing in R
Metabolomics, Vol.22(5), 160
2026
PMID: 42791429
Abstract
Introduction
Targeted metabolic phenotyping by liquid chromatography–tandem mass spectrometry (LC-MS/MS) relies on a fragmented toolchain of proprietary vendor formats, manual integration steps, and ad hoc quality-control (QC) scripts, introducing user- and laboratory-level variation that undermines reproducibility and confounds cross-laboratory and retrospective comparison.
Objectives
To provide an open-source, R-native workflow for targeted multiple reaction monitoring (MRM/SRM) mass spectrometry data that consolidates vendor file conversion, peak integration, and QC reporting into a single reproducible pipeline while preserving auditable, human-in-the-loop peak review.
Methods
MStargetR builds on msConvert and Skyline through three modules: msConvertR (vendor-to-mzML conversion), PeakForgeR (peak boundary optimisation and automated peak integration executed through Skyline), and qcCheckR (normalisation, concentration calculation, signal and batch correction, and reporting). Additionally, MStargetR has a standalone correction module and a Shiny graphical user interface. Third-party tools are pinned in version-controlled Docker images (with Apptainer support for high-performance computing), and each analytical plate emits a fully populated sky document for inspection and reimport.
Results
Applied to a published targeted lipidomics dataset of 128 human plasma samples targeting 1,161 lipid species, MStargetR processed all samples end-to-end, recovering all 1,161 targeted lipid features, 949 of which (81.7%) were detected and returned RSD < 30% across replicated long-term reference QCs. Analysis scaled linearly to 4,200 samples, averaging 4.1 s per sample.
Conclusion
MStargetR delivers automated batch processing, auditable peak review, and a documented QC layer in a single reproducible pipeline, supporting FAIR-aligned targeted metabolomics.
Details
- Title
- MStargetR: a reproducible, containerised workflow for end-to-end targeted (MRM/SRM) mass spectrometry data processing in R
- Authors/Creators
- Harrison Szemray - Murdoch UniversityVimalnath Nambiar - Murdoch UniversityDana Hicks - School of Medical, Molecular, and Forensic Science, Murdoch University, Perth, WA, AustraliaSam Lodge - Murdoch UniversityJulien Wist - Murdoch UniversityNathan Lawler - Murdoch UniversityLuke Whiley - Murdoch University
- Publication Details
- Metabolomics, Vol.22(5), 160
- Publisher
- Springer Nature
- Number of pages
- 13
- Identifiers
- 991005920317407891
- Copyright
- © The Author(s) 2026
- Murdoch Affiliation
- School of Allied Health; Centre for Computational and Systems Medicine
- Language
- English
- Resource Type
- Journal article
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