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. 2020 Apr 28;11:2057. doi: 10.1038/s41467-020-15960-z

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 2 — LipidCreator uses an internal knowledge base that stores information about lipids and their fragments. This information is assembled based on the user’s selection of lipid classes and additional parameters from the two main levels, the precursor and fragment level. Then, LipidCreator combines the user’s selection with information about lipid classes, precursors, backbones, fatty acyl chains, lipid fragments and information on isotope labels and forwards it to the transition layer for targeted assay generation. The downstream filter layer then applies collision-energy models to the generated transitions, if either automatic CE optimization or manual CE mode are enabled. The final export layer generates the final transition lists and spectral libraries and either stores them locally or transmits them directly to Skyline.