LipidMS 3.0: an R-package and a web-based tool for LC-MS/MS data processing and lipid annotation

Abstract

Motivation 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. Results We applied the new workflow in the data analysis of a commercial human serum pool spiked with 68 representative lipid standards acquired in full scan, DDA and DIA modes. When focusing on the detected lipid standard features and total identified lipids, LipidMS 3.0 data pre-processing performance is similar to XCMS, whereas it complements the annotations returned by MS-DIAL, providing a higher level of structural information and a lower number of incorrect annotations. To extend and facilitate LipidMS 3.0 usage among less experienced R-programming users, the workflow was also implemented as a web-based application. Availability The LipidMS R-package is freely available at https://CRAN.R-project.org/package=LipidMS and as a website at http://www.lipidms.com. Supplementary information Supplementary data are available at Bioinformatics online.

Brain areas lipidomics in female transgenic mouse model of Alzheimer's disease

“…In general, the intra-batch coefficients of variation of the internal standards were between 10 and 21%. Finally, data pre-processing and lipid species were annotated with the LipidMSv3 package 42 .…”

FAMetA: a mass isotopologue-based tool for the comprehensive analysis of fatty acid metabolism

“…msConvert from ProteoWizard [ 42 ], and a csv file containing the required metadata (sample name, acquisition mode, sample group or class, and any additional information like external measures for normalization) ( Supplementary Figure 2 , steps 1–2). Data preprocessing can be performed in the R environment/web-based application using our proposed workflow, which combines functions from FAMetA and our previously described R-package LipidMS [ 43 , 44 ] (available via CRAN ( https://CRAN.R-project.org/package=LipidMS )) ( Supplementary Figure 2 , steps 2–5). LipidMS is called for the first preprocessing step, which runs peak-picking, alignment and grouping through functions batchdataProcessing , alignmsbatch and groupmsbatch ( Supplementary Figure 2 , step 2).…”

FAMetA: a mass isotopologue-based tool for the comprehensive analysis of fatty acid metabolism

“…msConvert from ProteoWizard 43 , and a csv file containing the required metadata (sample name, acquisition mode, sample group, or class, and any additional information like external measures for normalisation) ( Supplementary Figure 2, steps 1-2 ). Data preprocessing can be performed in the R environment/web-based application using our proposed workflow, which combines functions from FAMetA and our previously described R-package LipidMS 44,45 (available via CRAN (https://CRAN.R-project.org/package=LipidMS)) ( Supplementary Figure 2, steps 2-5 ).…”