Daniel Haas

C-COMPASS: a user-friendly neural network tool profiles cell compartments at protein and lipid levels Nature Methods - C-COMPASS is an open-source software designed to predict the spatial cellular distribution of proteins and lipids from cellular organelle profiling using a neural network-based...

New TRR333 work from Natalie Krahmer's (Helmholtz Munich) and Jan Hasenauer's (Uni Bonn) teams: C-COMPASS, developed by Daniel Haas, makes subcellular proteomics and lipidomics accessible. The AI-based software maps proteins and lipids within cells and was applied to human adipocytes.
rdcu.be/eTpEs

C-COMPASS: a user-friendly neural network tool profiles cell compartments at protein and lipid levels - Nature Methods C-COMPASS is an open-source software designed to predict the spatial cellular distribution of proteins and lipids from cellular organelle profiling using a neural network-based regression model.

I‘m excited to announce that our paper has just been published in 𝑁𝑎𝑡𝑢𝑟𝑒 𝑀𝑒𝑡ℎ𝑜𝑑𝑠.

Here, we provide a solution for analyzing subcellular proteomics datasets and extend it to predict lipid localizations as well.

#proteomics #lipidomics #bioinformatics #systemsbiology

www.nature.com/articles/s41...

C-COMPASS: a user-friendly neural network tool profiles cell compartments at protein and lipid levels - Nature Methods C-COMPASS is an open-source software designed to predict the spatial cellular distribution of proteins and lipids from cellular organelle profiling using a neural network-based regression model.

🚀 Excited to share new work from Daniel Haas. In collaboration with Jan Hasenauer and Daniel Weindl within
@batenergy.bsky.social we've developed a software tool to map protein and lipid localization in cells, making spatial biology more accessible
www.nature.com/articles/s41...