MCP is going full open access!!!

As another great step forward -- MCP just announced, along with whatever ASBMB is (for some reason sounding it out reminds me of a Denzel Washington movie, not sure why....) that in January 2021 they're going full open access! 

Will the editors and reviewers still be some of the hardest people to impress in all of science? 

Of course!  (It's still MCP)  

What it does is opens a very important door. 

1) For the journal to get more real life exposure for the nitpicky, professionals-only, crazy rigorous, stuff that it will accept. 

2) And for it to be easier for the outside world to see what we really can do in proteomics when we absolutely have to stick to rigorous standards for study design, data formatting, and interpretation, etc., etc.,. 

Look, I know impact factors are stupid, but open access always improves how much your study is read and cited (this study found an 18% increase) and I suspect this will be a far larger increase for MCP.  How many of us really write when we're at work? I think the number is lower than other sciences. When we're in lab there is a nanoLC that requires....something....they always require something.... or there is a biologist that needs to talk about data interpretation of a disease they're supposedly an expert in, plus the vacuum pumps are SO LOUD. 

I think most of us write in the early mornings or late at night and if I've got the choice of logging in through VPN to my library or using an open access reference that will just open through Google Scholar? I dunno about you, but that's almost always an easy choice. 

London Proteomics Discussion Group (More SARS-CoV-2) Friday 5/15!

This Friday (May 15th) there is another of the biweekly (bimonthly?) -- every two weeks -- SARS-CoV-2 proteomics talks. This Friday's will feature --

You can register here!

The Clinical Knowledge Graph -- Is this the missing downstream piece!??!?

This deserves FAR more time than I have right now to give any one single thing right this second, but if it is 1/10 what I think I'm looking at, it's amazing and you should check it out.

How bummed out do you get when you give someone a perfect dataset -- you know the one, the one where the QA runs before and after were perfect. The one where you didn't need to impute anything. The one where you didn't even need to apply normalization because your loading was spot on in every injection. You hand that over knowing that at a technical level you've done EVERYTHING as well as anyone in the world could do it.....

....and you get the blank stare from the collaborator.....and that "what do I do with this?"

And what do you do?
Your first thought is to say something like, "Wait. What? Aren't you the f****** biologist? Isn't this the disease you study? Go do f****** biology with it... don't you dare tell me you brought this here with no plan of what to do next!?! What do you actually do here?!? F*** right off and get out of my office, F***!"

But that isn't what you can say, because you weren't raised in West Virginia and you were taught better manners than that. Instead you point them to the 4 resources that probably make the most sense for their study of the 50 or so that you know probably could be used to help them, because -- well -- we really don't have centralized proteomics knowledge or even protein level data. It's spread everywhere.

Maybe the Clinical Knowledge Graph is a step toward fixing this. Maybe? I can hope until I can put some data into it!

The Metabolomics Spectrum Resolver -- Visualize multiple matches!

A super fun exercise if you're new to metabolomics is to go ahead and search your experimental data against the wrong database. Working on human metabolomics? Try using a library for pesticides or archaea or from the fossil fuel industry or something. If you're using MS1 for identifications, your number of IDs may not even decrease, particularly if you're using resolution less than 30,000! And due to the similarity of many small molecules you'll find a lot of positive hits with MS/MS as well.

In a more realistic experiment, there are MS/MS libraries for small molecules all over the place. If you get multiple conflicting hits for the same molecule from different databases, what do you do? (It's common to search your data against multiple libraries -- and multiple conflicting matches is extremely likely). Interpreting which one is correct is often a nightmare because there is no format unifier.

BOOM!

This online interface can directly import spectra from 6 different metabolomics databases and compare them. The format is fantastic -- it looks similar like the IPSA program from Coon lab for annotating peptides -- it is intuitive and the graphs are pretty and can be directly exported in a several different formats, from Support Vector Graphics through JSONs and less weird stuff in-between.

FDA/NIH Glycoscience Day is Full Remote!

Every year I think -- I should put in another abstract for the NIH/FDA Glycoscience research day.

And then I remember how much fun it is to drive to the NIH Bethesda campus during rush hours.

I'd sign up for this instead EVERY. SINGLE. TIME.  

Honestly, where do you guys use the bathroom when you're it takes you 45 minutes to move forward 3 exits?  Is "bladder of steel" listed in the job requirements down there? 

As something positive to come out of this virus thing -- the whole thing is remote! 

And it isn't a bunch of local government shmucks talking. They got Rebekah Gundry who will talk SurfaceGenie and some other heavy hitters from the real world in the massively growing field of glycomics and glycoproteomics!  

You can register here!

A new crisis -- A shortage of peer reviewers!

Amidst the crises of this virus thing, we've got another one going on. No one can seem to find peer reviewers. As much as I make fun of the speed and other weaknesses in the peer review process, it is obviously still critical to scientific progress.

We've got two papers out where the Editors have told us -- sorry -- no one will review this. Now, since my name is on it, you could just assume that they're both really really bad, or I've successfully annoyed every scientist in the world to the point that no one wants to read another word I've written, but this appears to be systematic.

I suspect that the grown-ups who have been doing the heavy lifting on this are at home where they have other responsibilities like children and methodically redoing their bathrooms with 1 cm square tiles (what I assume tenured PIs do in their spare time)

Editors -- the starter's are out -- time to go to your bench!

Seriously, there are people out there who may not be as buried in pressing responsibilities -- or haven't reviewed 10,000 papers in their lifetimes already. Proof? Someone set up a Google Document of people volunteering to review! (...and it's not just for postdocs...)

If you have ever wondered things like "hey -- why don't I ever get asked to review anything" because you'd love to. Now's your shot!  You can add yourself to the list here!

Shiny + MetaProteomics + gut microbiome = pepFunk!

Did I spend more time looking at "Funky" gifs than using this great online ShinyApp?

(Funky BigFoot seems to...imply...yes...?...)

Inspirational paper? This great new tool!

Wow -- the the Gifs are making me a little dizzy, to be honest.

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(.....toooo pepFunkyTown....)

Despite my clear focus in this blog post, the "pep" in "pepFunk" is the central story in the paper. These authors find that when doing gut microbiome metaproteomics (i.e. digesting poo and trying to figure out what is there) that the peptide centric approach is a much better indicator than assembling to the protein level and then trying to interpret.

That makes a ton of sense because the whole technique of rolling up incomplete data at the protein coverage level to incomplete data at the FASTA level (since we don't have a true database of every organism that is present or their distribution) makes even less sense in a population this diverse than usual.

This isn't a suprise, but how do you utilize peptide centric data? Not easy -- until you've got a simple web app to dump data into!

You can check out the online version of the pepFunk Shiny App here.

Mixed Data Acquisition -- DIA + DDA in the same run!

Which is better?

Data Dependent Acquisition (DDA)
or
Data INdependent Acquisition (DIA)

Presenting -- MIXED Data Acquisition!

To help clarify this idea, I outsourced the design of a schematic of a "tribrid" orbital mass spectrometer device to some professional illustrators.

Tricked you! I did...part of this...myself! 

When you're doing DIA in your fancy instrument, the ion trap is doing a lot of nothing. Why not acquire DDA MS/MS spectra there while your Orbitrap is busy getting MS1 and DIA data? It's like spectra for free! 

Obviously, you'll need to worry about cycle time -- which is tough to calculate on your own on the Tribrids -- and consider the matrix and chromatography time, etc., etc., but there are key advantages. 

You know those coeluting peaks that kind of smear together in the XIC, particularly in the high mass range where your resolution is lower? These authors demonstrate that they can use the DIA windows to separate those out and improve their quan. The paper is pretty short -- proof of concept, but it doesn't take much imagination to see the potential advantages. Use your MS/MS to generate your libraries that you use for DIA quan? 

If there is a downside, I sure don't see it. You paid extra to get that ion trap back there. You might as well make it do stuff!  

HLA Peptide Sample Prep Completely Changes Peptide Populations.

This is a thorough analysis of HLA peptide enrichment and purification strategies that finds -- wait for it -- that how you prep these awful terrible poorly fragmenting (highly deamidated?) and often modified peptides changes the ones you're able to see.

I think they did a great job for this work, so the following should not -- in any way -- be considered a reflection of what I think of the work this team did.

From the abstract --

Again -- our biggest weakness in any head to head with any moron with a Hi-SeQ (or who has $1k to spend on a NanoPore -- which -- is coming fast) is that proteomics, as a field, can not ever once follow the same sample prep procedure, is completely and utterly, in every way, our fault. The moron with the Hi-SeQ just has to follow the directions that came in the box.

Proteomics Data Mining Challenge (#ALSMinePTMs) Data Coming in!

Good morning/afternoon/something crazy world out there!

Tons of data is coming in for the 2020 Proteomics Data Mining Challenge, as well as questions/clarifications (RIP my Inbox)....

If you've been expecting an email from me, it might possibly be in the 4...2...8...?...drafts...?

I'll investigate that...later....or just delete them. That seems unconquerable.

Just a reminder that I am not a judge, so if you attempt something like the submission at the very top -- I'll 100% appreciate it, but it won't help you out. I'm just the hype and disorganization guy.

This is a lot of data and if you're running a little behind, just let us know. I'll try to get to the questions that have come in today (...sorry...)

We're getting data via WeTransfer, DropBox, BOX, GoogleDrive, and something that started with a V or a Z? Meh. Seems to have worked, as well as data in CSV form that has been small enough to email.

WOOO!! Let's go!