It was a great pleasure to teach #FragPipe at the Biological Proteomics for Beginners workshop at #UCSD, sponsored by Thermo Fisher Scientific. We had a fantastic group of grad students, postdocs, and professors. Yes, I even got to teach UCSD professors how to analyze bottom-up proteomics data 😁

We didn’t cherry-pick the dataset for the benchmark—FragPipe also delivers excellent performance on another dataset (PXD028735).

Questions are always welcome! We're always happy to hear your feedback. (3/3)

Peptide-level LFQbench-style plot using the PXD003881 (IonStar) dataset. (2/3)

We also benchmarked LFQ precision and accuracy against the latest versions of other popular tools. Here's an protein-level LFQbench-style plot using the PXD003881 (IonStar) dataset. (1/3)

In this release, one of the major improvements is LFQ using IonQuant. Thanks to this excellent preprint (www.biorxiv.org/content/10.1...), we identified and fixed a suboptimal step in the XIC. We're always eager to listen to feedback from the community!

pubs.acs.org/doi/10.1007/... and pubs.acs.org/doi/10.1021/... discussed it in depth. Basically, there is a 1.0005 period in the mass axis. The decoy's shift better to be N*1.0005 so that it has false peptide but similar property as the target.

Ah, sorry, I pasted the wrong figures in a post I deleted. Let me redo it.

There are two types of the works discussing about 1.0005 Th shift. One is from The and Kall 2020 (www.nature.com/articles/s41...). See the figure.

Another one is from the lab in PNNL and SEQUEST family. Let me find a paper