Something else for me to worry about. Thank up for that 😅

Decided to take Amtrak down to Portland for US HUPO. I give myself 50/50 odds of being on time lol

I have always personally thought larger the better (eg 1:100).

The dynamic programming part of MS-GF+ would probably be a fun (re headache inducing) challenge for undergrads

I should note that one needs to remember the existence of "neighbor peptides" if you just search on the subset. Neighbor peptides are irrelevant peptides that look like relevant peptides.
pubs.acs.org/doi/10.1021/...

I agree that different solutions are required for different use cases. I would even argue that for certain cases (really small database) that FDR is the wrong thing to do.

I think it was 2017 @neely.bsky.social

Maybe I am missing something but I'm not aware of any DIA analysis that only looks for a fre peptides. Am I reading your ppst wrong?

Are you referring to the talk where he (and Uri) showed that different shuffling of the decoy databases can yield different estimates? This effect becomes larger as the db becomes smaller.

pubmed.ncbi.nlm.nih.gov/30560673/

I really like the wording of this approach. Going to have to remember it for the future.

Is there a beef with mstdn? I've haven't seen anyone articulate that yet. I think we as a community are still trying to figure out the next step so to me it seems natural to have some chaos.

It's kind of interesting how much FDR has been talked so far. It's day 2 and there have been like 5 talks.

On the other hand, I don't remember the last time I saw a FDR focused talk at ASMS. Not sure what this means, if anything, but an interesting observation.

Which paper was that? I think I missed the thread mastadon.

Thanks! And I wil admit I typically don't do metabolomics either. Bit of a new experience for me.

We then looked at how MHNs may provide utility for annotation of metabolomics data and may aid in interpretability of metabolomics by applying this representation to several previously published datasets. (4/4)

In a MHN edges connect an arbitrary number of nodes. These edges allow for more complex visualizations of relationships that may be hidden in a graph representation. An example can be shown in Fig1, which describes how 3 different coauthor relationships (1B/1C/1D) would yield the same graph. (3/4)

A molecular hypernetwork (MHN) is an extension to a molecular network (MN) that has been popularized by platforms such as GNPS. A MN uses a graph representation where nodes are spectra and edges connect two nodes that have high spectral similarity. (2/4)