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Reposted by Daniel Haas

TRR333 BATEnergy @batenergy.bsky.social · 06/12/2025

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

rdcu.be

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...

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Daniel Haas @daniel-haas.bsky.social · 16/12/2025

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...

nature.com

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.

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Reposted by Daniel Haas

Natalie Krahmer @nataliekrahmer.bsky.social · 04/12/2025

🚀 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...

nature.com

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.

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