MRMhub: one-stop solution for automated processing of large-scale targeted metabolomics data

Burla, B.; Teo, G.; Benke, P. I.; Lu, Z.; Tan, S. H.; Ji, S.; Oh, J.; Lim, P. Y.; Vaitheeswari,; Cazenave-Gassiot, A.; Venkataraman, K.; Tai, E. S.; Torta, F.; Wenk, M. R.; Chan, M. Y. Y.; Choi, H.

doi:10.64898/2025.12.20.695370

bioRxiv · 10.64898/2025.12.20.695370

MRMhub: one-stop solution for automated processing of large-scale targeted metabolomics data

Burla, B.·Teo, G.·Benke, P. I.·Lu, Z.·Tan, S. H.·Ji, S.·Oh, J.·Lim, P. Y.·Vaitheeswari,·Cazenave-Gassiot, A.·Venkataraman, K.·Tai, E. S.·Torta, F.·Wenk, M. R.·Chan, M. Y. Y.·Choi, H.

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Abstract

Data processing and quality control are essential for complex targeted mass spectrometry (MS) assays in large-scale metabolomics studies. However, existing software solutions have significant gaps in robustness and scalability. We report MRMhub, a one-stop solution for streamlined processing of large-scale targeted MS data. MRMhub consists of a novel peak integration engine with unique algorithmic design to address the scalability challenge and a comprehensive collection of post-acquisition data processing and analytical quality control tools. The ensemble facilitates rapid and consistent processing of complex chromatograms, quantification, drift and batch correction, quality assessment and control, feature filtering, and data/workflow sharing and reporting. MRMhub can process data from highly complex assays in population-scale studies within minutes, with full digital footprints warranting reproducibility and traceability. We demonstrate its performance using two large-scale lipidomics data sets. We distribute the source code and data sets freely for community development.

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Burla, B., Teo, G., Benke, P. I., Lu, Z., Tan, S. H., Ji, S., Oh, J., Lim, P. Y., Vaitheeswari,, Cazenave-Gassiot, A., Venkataraman, K., Tai, E. S., Torta, F., Wenk, M. R., Chan, M. Y. Y., Choi, H.. 2025-12-23. MRMhub: one-stop solution for automated processing of large-scale targeted metabolomics data. https://doi.org/10.64898/2025.12.20.695370

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