1 LipidMS 3.0: an R-package and a web-based tool for LC-MS/MS data processing and lipid annotation María Isabel Alcoriza-Balaguer 1 , Juan Carlos García-Cañaveras 1,* , Francisco Javier Ripoll- Esteve 2 , and Agustín Lahoz 1,3,* 1 Biomarkers and Precision Medicine Unit, Medical Research Institute-Hospital La Fe, Av. Fernando Abril Martorell 106, Valencia, 46026, Spain. 2 Department of Informatics, Medical Research Institute-Hospital La Fe, Av. Fernando Abril Martorell 106, Valencia, 46026, Spain. 3 Analytical Unit, Medical Research Institute-Hospital La Fe, Av. Fernando Abril Martorell 106, Valencia, 46026, Spain. *email: juancarlos_garcia@iislafe.es, agustin.lahoz@uv.es Abstract Summary: LipidMS was initially envisioned to use fragmentation rules and data-independent acquisition (DIA) for lipid annotation. However, data-dependent acquisition (DDA) remains the most widespread acquisition mode for untargeted LC-MS/MS-based lipidomics. Here we present LipidMS 3.0, an R package that not only adds DDA and new lipid classes to its pipeline, but also the required functionalities to cover the whole data analysis workflow from pre-processing (i.e., peak-peaking, alignment and grouping) to lipid annotation. We applied the new workflow in the analysis of a serum dataset acquired in MS, DDA and DIA modes. Our results show that LipidMS 3.0 data pre-processing outperforms XCMS and complements those lipids annotated using MS- DIAL, one of the most widely used tools in lipidomics. To extend and facilitate LipidMS 3.0 usage among less experienced R-programming users the workflow has been also implemented as a web- based application. Availability and Implementation: LipidMS R-package is freely available at https://CRAN.R- project.org/package=LipidMS and as a website at http://www.lipidms.com. . CC-BY-NC-ND 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted February 28, 2022. ; https://doi.org/10.1101/2022.02.25.476005 doi: bioRxiv preprint