Trackable and scalable LC-MS metabolomics data processing using asari. Nat Commun. 2023;14(1):4113.
JGM, Chromatography, Liquid, Reproducibility of Results, Tandem Mass Spectrometry, Metabolomics
Nat Commun. 2023;14(1):4113.
This work was in parted funded by NIH grants (to SL) U01 CA235493 (NCI) and R01 AI149746 (NIAID).
Significant challenges remain in the computational processing of data from liquid chomratography-mass spectrometry (LC-MS)-based metabolomic experiments into metabolite features. In this study, we examine the issues of provenance and reproducibility using the current software tools. Inconsistency among the tools examined is attributed to the deficiencies of mass alignment and controls of feature quality. To address these issues, we develop the open-source software tool asari for LC-MS metabolomics data processing. Asari is designed with a set of specific algorithmic framework and data structures, and all steps are explicitly trackable. Asari compares favorably to other tools in feature detection and quantification. It offers substantial improvement in computational performance over current tools, and it is highly scalable.