What do you need to do with your lipid data?

Our mission is to provide the bioinformatic framework to understand lipids in context and create an integrative systems biology view for lipid research.

Check out the tools we develop and provide below!

LIFS workflow in four steps. 1 Data: vendor files from the mass spectrometer, converted to the open mzML format with MSConvert. 2 Identify and quantify: shotgun data go through LipidXplorer and then lxPostman, targeted data through Skyline, which works with the vendor files and its LipidCreator plugin; both give results plus the methods behind them. 3 Explore and enrich: the results go to LipidSpace, Lipidome Projector and MAPtoGO, which also takes features and counts from other omics. 4 Report: the methods go into the Lipidomics Checklist, whose mzTab-M metadata and the results go into LipidCompass. Badges mark the tools with embedded lipid name normalization by Goslin: lxPostman, LipidCreator, LipidSpace, MAPtoGO and LipidCompass; Lipidome Projector built its reference space with Goslin. LipidSpace and LipidCompass read mzTab-M. Standards and validation: mzTab-M, mzQC and the mzTab Validator.
All LIFS tools are open source and free to use; Skyline (MacCoss Lab) and MSConvert (ProteoWizard) are external tools.

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All tools & standards

The full LIFS suite, in workflow order.

Step 1

Data

LipidXplorer reads the open mzML format; Skyline opens vendor files directly.

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MSConvert

Desktop External tool

Converts vendor raw files to the open mzML format that LipidXplorer reads. Part of ProteoWizard.

  • Chambers, M.C., Maclean, B., Burke, R. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nature Biotechnology 30(10), 918–920 (2012). doi:10.1038/nbt.2377 (external site)
  • Adusumilli, R., Mallick, P. Data conversion with ProteoWizard msConvert. In: Comai, L., Katz, J.E., Mallick, P. (eds) Proteomics. Methods in Molecular Biology, 339–368 (2017). doi:10.1007/978-1-4939-6747-6_23 (external site)
Download (external site) ProteoWizard

Step 2

Identify & quantify

Shotgun data: LipidXplorer, then lxPostman. Targeted data: LipidCreator, then Skyline.

Skyline screenshot with the LipidCreator plugin

Skyline

Desktop Win External tool

Opens vendor raw files, integrates peaks and quantifies the transitions designed in LipidCreator. Developed by the MacCoss Lab, University of Washington.

Download (external site) MacCoss Lab

Step 3

Explore & enrich

Compare whole lipidomes, find the lipids that differ, and test lipid and other omics features for GO-term enrichment.

LipidSpace screenshot

LipidSpace

Desktop REST API Win macOS Linux

Compares whole lipidomes through a structural-space model, with machine-learning feature selection, QC and an interactive GUI.

  • Kopczynski, D., Hoffmann, N., Troppmair, N. et al. LipidSpace: simple exploration, reanalysis, and quality control of large-scale lipidomics studies. Analytical Chemistry 95(41), 15236–15244 (2023). doi:10.1021/acs.analchem.3c02449 (external site)
More Info Download (opens github.com) v1.2.2

Step 4

Report

Report the methods behind your results, validate your files and deposit results and metadata together.

Lipidomics Checklist screenshot

Lipidomics Checklist

Web app

Reports the analytical, identification and quantification methods behind your results, step by step. The basis for mzTab-M metadata; PDF export and an optional Zenodo DOI.

  • McDonald, J.G., Ejsing, C.S., Kopczynski, D. et al. Introducing the lipidomics minimal reporting checklist. Nature Metabolism 4(9), 1086–1088 (2022). doi:10.1038/s42255-022-00628-3 (external site)
  • Kopczynski, D., Ejsing, C.S., McDonald, J.G. et al. The lipidomics reporting checklist: a framework for transparency of lipidomic experiments and repurposing resource data. Journal of Lipid Research 65(9), 100621 (2024). doi:10.1016/j.jlr.2024.100621 (external site)
Start checklist (external site) PDF / DOI
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mzTab-M

Library

A standard format for reporting quantitative mass spectrometry metabolomics results in a tab-separated file.

Read the spec (external site) HUPO-PSI
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mzQC

Library

A standard for reporting and exchanging quality control metrics for mass spectrometry runs and datasets.

  • Bielow, C., Hoffmann, N., Jimenez-Morales, D. et al. Communicating mass spectrometry quality information in mzQC with Python, R, and Java. Journal of the American Society for Mass Spectrometry 35(8), 1875–1882 (2024). doi:10.1021/jasms.4c00174 (external site)
Read the spec (external site) HUPO-PSI

Every step

Lipid names

One naming standard, built into lxPostman, LipidCreator, LipidSpace, MAPtoGO and LipidCompass.

Goslin screenshot

Goslin

Web app Library

Parses the many lipid nomenclatures and serialises them back to standardised LIPID MAPS shorthand. Libraries in C++, Python, Java and R.

More Info Open web app (opens apps.lifs-tools.org) C++ / Py / Java / R
Not sure which tool fits? Please reach out, we are happy to help you! Ask for support

Skyline screenshot cropped from Fig. 1c of Peng, B., Kopczynski, D., Pratt, B.S. et al. LipidCreator workbench to probe the lipidomic landscape. Nat Commun 11, 2057 (2020), licensed under CC BY 4.0.

LIFS Training Workshop at Lipidomics Forum 2026

Written by Nils Hoffmann on May 03, 2026. Posted in Training

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Welcome to the web portal for Lipidomics Informatics for Life Sciences (LIFS)

Even with lipid content of over 10% in the human body, lipids were not the central scientific focus during the last decade. However, more and more evidence proves that non-genetically determined molecules such as metabolites and lipids play a critical role in cellular regulation. Today, it is evident that lipids are not only crucial for energy homeostasis and as an environmental-cellular barrier but also that they represent a central part of our signal transduction machinery.
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