Trackable and scalable LC-MS metabolomics data processing using asari

Abstract

Significant challenges still exist in the computational processing of data from LC-MS metabolomic experiments into metabolite features. In this study, we examine the issues of provenance and reproducibility in the current software tools. The inconsistency among these tools is attributed to the deficiencies of mass alignment and controls of feature quality. To address these issues, we have developed a new open-source software tool, asari, for LC-MS metabolomics data processing. Asari is designed with a set of new 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 of computational performance over current tools, and is highly scalable.