The lipidomics field faces unique challenges in standardizing its nomenclature and measurement precision, unlike genomics, transcriptomics, and proteomics, which have relatively consistent units of measurement (genes, transcripts, proteins). In lipidomics, measurement limitations frequently prevent analysts from identifying lipids at precise structural or isomeric subspecies levels. Consequently, lipid identification often relies on generalized representations, such as abstract class or species names aligned with established ontologies. This, along with variations in database standards, creates a particularly fragmented and complex landscape for prior knowledge in lipidomics.
LipiNet is designed to address these challenges by integrating information across disparate lipidomics databases, each with different identifiers and varying levels of lipid resolution. By unifying these resources and accounting for the inherent ambiguity in lipid identification, LipiNet enables more cohesive and comprehensive network analyses across lipidomics databases.
- Multi-layered network construction and analysis
- Cross-database lipid identifier integration
- Tools for filtering, analysing and visualising by layers
LipiNet relies on functions in OnionNet, which in turn requires graph-tool to be installed. Because graph-tool is built around C++ for efficiency, unfortunately there is no straightforward pip installation. Nonetheless, there are a number of ways to install graph-tool besides pip, see here for more details. The easiest way for most users is probably to create a new env via conda:
conda create --name gt -c conda-forge graph-tool ipython jupyter
conda activate gt
Then you can install OnionNet within the conda env with:
pip install git+https://github.com/saezlab/onionnet.git
Finally you can install LipiNet in a similar fashion:
pip install git+https://github.com/saezlab/lipinet.git
Now you should be ready to go!
In the near future we intend to include both LipiNet and OnionNet on PyPI.
The general framework for LipiNet is:
- Dataset parsing
- Dataset exploration
- Integration (in progress)
You can currently find tutorials for the first two steps with SwissLipids in the notebooks folder, describing the network creation process and initial findings from exploration.
Most of the datasets in LipiNet are openly available for use provided you cite them accordingly. We encourage users to check the terms of the resource themselves.
Both LipiNet and OnionNet are in active development and subject to change. Some functions may be modified or deprecated in future releases. If you find LipiNet helpful or have ideas for improvement, we'd love to hear more!