We have now released 𝐃𝐈𝐀-𝐍𝐍 𝟐.𝟎! DIA-NN 2.0 Academia: https://t.co/hxgTs5kYau. We also now offer DIA-NN Enterprise for Industry, please contact Aptila Biotech https://t.co/QFYuxu7QCl.

Identification numbers and FDR control of Spectronaut 18 vs DIA-NN 1.9 as benchmarked by Jesper Olsen and colleagues (Nature Methods) https://t.co/YjH2KVtrGh

DIA-NN 2.0 release is almost ready, will come with some big news. One is scanning methods support, at the moment doing some tuning of the algorithm using Synchro-PASEF data by @labs_mann :) Turns out, 'Q1 information' is really helpful for gaining peptidoform confidence. Likely

Deeply honored to receive the MCP Lectureship award at @SSP2024. Thank you for this incredible opportunity to share our work at @broadinstitute. Very grateful for all the support from my amazing @LeibnizFMP lab and collaborators! #interactomics #3Dproteome

DIA-NN 1.9.2 is out! There are some major improvements, please check the release notes

Incredibly honoured to receive the EuPA award. Tremendous thanks to @lilley_ks and all amazing colleagues at the @CamCProteomics, the birthplace of DIA-NN, as well as my fantastic lab members, our collaborators and the proteomics community for all the support!

Major reduction of memory usage by future DIA-NN versions. Current development version searching 20 million phosphopeptides:

News in Proteomics Research Blog Post | Did y'all know DIA-NN has an integrated viewer now?! https://t.co/XGSuZ8yORe --- #proteomics #prot-other

Are you using mass spec based proteomics to study endogenous proteolytic activity? This work describes an R package TermineR for downstream processing (annotation, visualization, differential analysis etc.) of semi-specific search results from #FragPipe.

I made a few more tutorial videos on the newest versions of DIA-NN & Fragpipe for those new to the software. Because why pay us when you can do it yourself right?

DIA-NN now supports visualisation. View the DIA-NN analysis directly in Skyline (many thanks to @SkylineUW and @nesvilab teams for implementing and testing the required interface!) as well as in the integrated DIA-NN Viewer.

DDA used to be considered 'more reliable', as one could be confident in correct peptidoform assignment. Now also works for any DIA data, even from 10 years ago :)

Excited to announce DIA-NN version 1.9, our software suite for proteomics data processing! A range of cool new features and general performance improvements. We consider it the most significant update in the history of DIA-NN. https://t.co/RKzniysMLg

Finally published! Delighted to introduce CLASP: a novel framework for spatial proteomics. Using XLMS we unveiled intricate protein localizations & topologies in mitochondria. CLASP offers higher resolution and throughput than BioID/APEX. https://t.co/sYLv4CEust

Read this technote by @SCIEXOmics where they demonstrate their Mag-Net workflow using @ReSyn_Bio bead enrichment and #ZenoSWATH #DIA. These methods pave the way for enabling robust #plasma analysis that can be automated for large cohort analyses.

I am thrilled that our team @KasperFugger and @pedrobeltrao received a @HFSP Research Grant. We will have loads of fun exploring the chemical space of bioactive modified nucleotides and their modifying/detoxifying enzymes! We will soon look for a Postdoc (Chemical Biology)!

Now, I kind of understand what algorithms likely lead to this kind of output. Although DIA-NN (dev version at least) will likely instead report just a single localisation instead of three. The question is, how people interpret this for biology inference? Is only T814 considered

Took a look at a Spectronaut phosphoproteomics report (PXD034128). How to interpret this? Three precursors with different phosphate localisations matched to the same RT, first one has T828 annotated but 98.4% confidence in phosphate being on S818. The second actually has S818

I can confirm that both DIA-NN 1.8.1 and latest beta versions have correctly controlled FDR on Orbitrap Astral data - checked by searching plasma EV data https://t.co/WZ3oipx5aG against human + E.coli database, real FDR is in the range 1%-2% when 1% filter is applied

DIA-NN at present is the best tool for „free“ to get most out of DIA data - but doesn‘t come with data viz features. This is a great tutorial to import DIA-NN results into skyline to vizualize results https://t.co/L4qIwnK1UC