MRMhub Workflow Examples

Reproducible example reports of the MRMhub targeted LC-MS data-processing platform

These notebooks reproduce the MRMhub-based data processing pipeline end-to-end on real targeted LC-MS datasets, from INTEGRATOR peak-integration results through normalization, quantification, quality control and reporting. Each dataset is processed in a self-contained Quarto notebook combining descriptions, executable R scripts and outputs.

Workflows

  • Dataset 1 ↗ — SPERFECT study: longitudinal LC-MRM-MS-based plasma lipidomics in participants at risk of coronary artery disease, Tan et al. (Tan et al. 2022); 482 features (464 transitions), 937 samples. Feature annotation QC, normalization and quantification, drift and batch correction, detailed QC plots, feature filtering and data export.
  • Dataset 3 ↗ — DYNAMO study: LC-MRM-MS-based plasma lipidomics study on biomarkers of renal function decline in type 2 diabetes, Chen et al. (Chen et al. 2025); 828 features (748 transitions), 4,591 samples. The same overall workflow as Dataset 1, but with detailed outlier detection and additional QC plots.
  • Dataset 4 ↗ — Steroid panel: fully quantitative LC-MRM-MS-based serum steroid assay with 15 analytes (Jansen et al. 2014). External calibration curves, quantification, assay variability and bias calculation.

Related:

  • Dataset 4 · MRMhub vs MassHunter ↗ — compares the end-to-end processing (peak integration and postprocessing) of MRMhub with Agilent MassHunter for the Dataset 4 steroid panel data.
  • Manuscript figure ↗ — assembly of a multipanel figure with QC plots from the Dataset 1 pipeline notebook, used in the Supplementary Notes of the publication (Burla et al., 2026).

Source: github.com/SLINGhub/MRMhub-workflows  |  Code & data: 10.5281/zenodo.15370293

Authors

  • Bo Burla ORCID iD
  • Guo Shou Teo ORCID iD
  • Hyungwon Choi ORCID iD

Data

The datasets and the full code used to generate this resource are deposited together in the Zenodo record 10.5281/zenodo.15370293 (MRMhub-workflows). The GitHub repository does not contain the Dataset 1 and 3 data due to their sizes.

Citation

Contact

For questions or feedback, contact bo.burla@nus.edu.sg and hyung_won_choi@nus.edu.sg.

References

Burla, Bo, Guo Shou Teo, Peter I. Benke, et al. 2025. “MRMhub: One-Stop Solution for Automated Processing of Large-Scale Targeted Metabolomics Data.” bioRxiv, ahead of print, December 23. https://doi.org/10.64898/2025.12.20.695370.
Chen, Yuqing, Federico Torta, Hiromi W. L. Koh, et al. 2025. “Metabolomics Profiling in Multi-Ancestral Individuals with Type 2 Diabetes in Singapore Identified Metabolites Associated with Renal Function Decline.” Diabetologia 68 (3): 557–75. https://doi.org/10.1007/s00125-024-06324-z.
Jansen, Rob, Nuthar Jassam, Annette Thomas, et al. 2014. “A Category 1 EQA Scheme for Comparison of Laboratory Performance and Method Performance: An International Pilot Study in the Framework of the Calibration 2000 Project.” Clinica Chimica Acta 432 (May): 90–98. https://doi.org/10.1016/j.cca.2013.11.003.
Tan, Sock Hwee, Hiromi W. L. Koh, Jing Yi Chua, et al. 2022. “Variability of the Plasma Lipidome and Subclinical Coronary Atherosclerosis.” Arteriosclerosis, Thrombosis, and Vascular Biology 42 (1): 100–112. https://doi.org/10.1161/ATVBAHA.121.316847.