Thanks (especially as I was so vague). It feels like a lot of scripting for sure. After chatting with Magnus, even working through some tutorials like those collabs by @robbinbouwmeester.bsky.social on ProteomicsML wouldn't be a bad idea.

And you get the cutest mascotte octopus. His name is Mark! He'll guide you Clippy-wise through your analyses 🙌

Looks amazing 😍

What excites me most is that it introduces the first ML-based solution for peptide multiconformers. But that’s not all! We also demonstrate a substantial performance boost for uniconforming peptides.

Our findings are clear: multiconformer peptides cannot be overlooked when predicting CCS!

Or even better, use the transfer learning ability of DeepLC with a good base model (e.g., the one above)

Cool! Small comment, indeed the hela_hf model can predict from TMT-labelled peptides, it needs to extrapolate a lot. Best is probably to use this model: github.com/RobbinBouwme...

Looks 3D printed, are you sure this is food-safe 😬. There is a lot of discussion around this, especially because of the grooves and edges that easily catch food and are hard to clean. Be sure to clean it very well, and maybe run some swabs of the cutter on your timsTOF ;)

Although I might be biased, in my opinion it is the best stream-lined experience for rescoring. Even going to quant with tools such as FlashLFQ, simply works phenomenally.