Sooooooo....umm...I'm relatively sure I just learned a few things from this new paper ASAP at JPR and I think you should read it, particularly if you want to learn/unlearn/overlearn what you thought you understood about how XIC (eXtracted Ion Chromatogram) based clustering (a step most commonly employed as a part of label free quantification workflows by data dependent LC-MS/MS) works.
Now. I'm on a fence between here, because I don't understand this very well, but I'm really excited about this study and how they did it. Stop typing? Type faster? Screenshots? ....Screenshots!
Okay -- so WTFTICR is any of these here words?
Either I was in the sun too long or all of these are new programs to me. Presumably they exist behind the scenes as steps in processing pipelines I know about? I don't know. This paper is worth reading just to look up new software! But it's not done. It is about a new way to do XIC clustering based on Bayesian thingamathings, which of course are:
...I totally knew that....
You can get XNET at this GitHub and it requires an Apache License, which is something I'd seen written and...I give up. I had no idea what that was either, but it is an open source agreement that you can read about here.
And my favorite part about this paper might be how brazenly just honest and just good the whole thing seems. My interpretation is this:
1) Bayesian network things might be a smart way to do XIC clustering quan
2) This is what this is and how we set it up.
3) Here is the potential it might have
4) Here we stacked it up against stuff that you already know, like MaxQuant and OpenMS
5) Sure -- we don't actually win this comparison, but here is all our code and you can use it as long as you get this cool license that says you guarantee it stays open source forever.
The only thing might possibly improve this intimidatingly smart and positive example of how science should work might be ending it like this.
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