What's an under represented PTM? Trust me, you probably don't want to know, but...ugh...there are a lot of PTMs out there....
This is actually a cool chart that is the number of publications about some of these under-considered PTMs per year:
Just because we aren't searching for them doesn't mean they aren't there. But how would you get to them? Enter TeaProt and the urPTMdb!
First off, urPTMdb and TeaProt are very different things housed at the same location (https://tea.coffeeprot.com/)
TeaProt is a really clever Shiny App for downstream analysis of quantitative proteomics data.
urPTMdb is a set of databases of proteins with these weird PTMs.
The more I mess with TeaProt (and work out the acronyms) the more I liked how clever the output is. There are a lot of downstream proteomic analysis Shiny applications and probably a lot more on the way. What sets this one apart?
The big differentiator here is probably the number of databases you can compare your protein lists against. And if you go all the way to the bottom to the functional gene set enrichment analysis -- you can pull up the urPTMdb and compare your data to that as well.
Imagine this scenario:
What if you've done everything right and you've completed a fantastic proteomic analysis of some disease conditions and you get to the end and everything is the same? I've got a couple of these on backup hard drives that haunt me years later. I didn't skip a step. I had the luxury of time and the right access to resources to do it all right -- and I couldn't find anything to explain those phenotypes.
What if you take that output report and dump it in here.
What if TeaProt and urPTMdb says -- hey, those a bunch of those proteins have an F-U-mylation (actually an option on the list). Did you ever think of an F-U-mylation? What if a shift in F-U-mylation actually drives this disease state?
Name another way to get to that data. I'll wait.
In the meantime, the rest of the app is well written and crisp and the graphs are solid and smart.
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