Abstract
In recent years, liquid chromatography coupled to mass spectrometry (LC-MS) has emerged as the main technology to measure the whole of small molecules (the metabolome) in a diversity of matrices. Within the field of computational metabolomics, significant efforts have been made in the development of tools to (pre)process untargeted LC-MS data. However, tools that circumvent the time-consuming, manual preprocessing of targeted LC-MS data with vendor-specific software remain sparse. We therefore present TARDIS, an open-source R package for the analysis of targeted LC-MS metabolomics and lipidomics data. Both established (area under the curve, maximum intensity and points over the peak) and recently developed (custom signal-to-noise ratio and bell-curve similarity) quality metrics were included to offer increased efficiency of peak quality evaluation. The robustness of TARDIS' peak integration was demonstrated through a quantitative comparison to state-of-the-art vendor software. To this end, applicability at a large scale (n = 1786) was validated across three distinct biofluids (stool, saliva and urine) and two LC-MS instruments, using data from the FAME, ENVIRONAGE, and FGFP cohort studies. In conclusion, TARDIS offers a robust and scalable open-source solution for the targeted analysis of LC-MS metabolomics and lipidomics data. TARDIS and its source code are freely available at https://github.com/UGent-LIMET/TARDIS.