KOINA - Proteomics machine learning for everyone!

 

There have been some bioinformatics initiatives where the goal has been "any dumbass can do this, even you!" and it has turned out that 

(pronounced doom basses, the latter word sounds like the instruments) 

Is KOINA really something we could all use, regardless of what languages we do/don't know and how very long ago we had any formal training in informatics? I'm not sure, but the authors seems to have tried really really hard to make it so. 

The first problem a lot of people run into when integrating tools into other tools is what language is supported? Machine learning is almost always in Python and proteomics people like Java and various versions  of C. 

30 different tools are integrated into KOINA in an attempt to both make this all more open and to show off that they can take data from and to different languages! 

The next thing is that even within the same programs peptide annotations can be completely different. Ever try to make a Venny from a peptide list from 2 different software packages? Chances are zero overlap because someone's like, "let's absolutely for no reason whatsoever provide the c-terminal amino acid with a period after it AND THEN let's make up a non-standard abbreviation for this PTM and put it in italics while underlining a random letter in this non-standard abbreviation" one of those is absolutely real, the other one probably is, I don't know everything.

So the high school sized list of KOINA authors decided to do something no proteomic informatics person in all of history has ever tried. 

THEY CONSULTED THE PROTEOMICS STANDARDS INITIATIVE (we have a proteomics standards initiative????) (PSI)

I just got back from International Human Proteomics and rumor was that this year's secret meeting of our field's least popular group of people was held in the nosebleed section of the Sabrina Carpenter concert, ensuring that they actually annoyed more people this year with their suggestions of how making up new ways of doing the same things is stupid and we should stop, and that no one who could possibly need this lesson heard them. 

KOINA includes steps to convert peptide level annotations to a standardized format that PSI suggested that we all use at some point and we all pointedly ignored. 

I need to get going, but if you read this far (sorry) you should check out KOINA here! 

SCPLine - SCP downstream analysis tool that looks easy to operate!

 

No time to check this, but I want to come back to it later, and this is the easiest place for me to find it again! 

SPIN - Super impressive/fast low contact/low volume single cell proteomics I bet you can buy some day soon!

 

This was a fantastic read this weekend.  Both the technology and the fact it is a cool story overall! 

I should, however, start with - no, you can't buy one yet. This is some advanced prototype of what was a student project a few years ago. You can read and watch a video about the Isolatrix here. 

If it was for sale right this second, I wouldn't jump in line to buy it. The reason it is so amazingly super fast is that it makes a decent number of mistakes, but it's smart enough to identify those wells with zero cells or more than 1 cell, or one funny cell itself. While this sounds simple to fix, it's easier for us to run every "single cell" well rather than skip the ones that are funny. That works when we're getting >85% single cells in our current workflow, but I currently wouldn't trade 100x faster for a whole lot more errors till some other issues were sorted out.

Now, when they do compare it to our current single cell isolation solution in our lab - Isolatrix preps in lower volumes and a ton faster AND gets visual data, so it's neither close nor really fair. Higher coverage per cell and the ability to go back and evaluate the cells that you were working with visually? That's a lot. Now...my solution was $25k when I bought it ($42k list now) and the chips were like $12 a piece (about $50 each now) and so we expect better newer tech to crush it in head-to-head analyses.

If you are a mass spec nerd and want to see what a TIMSTOF SCP vs Ultra2 look like on the same cells. This is another good comparison of the early and newer hardware.

Ignore the EvoSep instructions and load peptide tips however you want!!

The whole point of the EvoSep HPLC is reproducibility, I'm pretty sure. That's what it says on all their things. The tips come with handy little visual instruction cards that tell you exactly how to load them. My team actually made stickers for the boxes where you check off each step you've completed because we can only spin 2 boxes at a time in our centrifuge and we often prep far more tips at a time. 

And...I swear about half of the papers I read (particularly from one group I'm going to point out right now) seem to just load the tips however they happen to feel like doing it that day. 

This is by far the funniest one. In this paper on AlphaDIA - a group that has strong ties to EvoSep - ignores the instructions entirely - in 2 separate ways, in a single paper. 

Look, this is funny because it's the same study, but if the goal is reproducible peptide binding why introduce a potentially confounding variable at all?? 

First EvoTip prep

Propanol-1 then water then 99% acetonitrile then re-equilibrate (you never learn how much volume here).

And then a totally different way of loading the same tips! This time with a robot, so maybe it has to do it this way? 

Propanol-1 then 99% acetonitrile twice then water twice with seemingly random volumes? 

In case you aren't familiar this is what the card says in the box. 

It was tough choosing a gif here, but it's tough to pass this one up.

AlphaDIA - I don't understand why it is different but it IS open source!

 

I really did read the full body of the text for the published manuscript on AlphaDIA. I kept looking for the "why is this different than the DIA tools that I currently use" and I never had that "OH! That's why this is different!" moment. For real, WTF is a learning transfer? I'm either a poor reader or it is never fully explained in the text anywhere. 

However it 

1) Processes Orbitrap data

2) Processes TIMSTOF data

3) Processes SCIEX ZenoTOF data

4) Processes Astral TOF data

And it is fully open source! YEAH!  Which doesn't matter a lot to academics cause 2 of the main tools you probably use are free to you and it doesn't make a difference. But it matters to a lot of people! 

Looking at just the baseline numbers for what we get in DIA-NN and SpectroNaut on our cancer cell line digests on the TIMSTOF Ultra and the numbers I get when I reanalyze other people's Astral data, the AlphaDIA numbers seem on the lower end, but within reasonable expectations. Also, it's worth noting the Github went live 4 years ago and the preprint is at least 18 months old, so these might be older files. Proteomics hardware/methods and informatics improvements have been nuts the last couple of years, so it's tough to tell how much all that factors in here. 

If you haven't looked at it in a while, I'm happy to report the Github has some neat little animated walkthroughs and things. 

OH YEAH! And it runs on MacIntoshes. So if you're just done with Windows 11 enough that you're going to buy hardware from the US POTUS's very close friend Mr. Apple himself.

You can read about it here. 

Does anyone know why there is a cockroach peptidomics paper every couple of years? New one!

 

I won't lie, I'm not even going to go past the first page on this one. I was at Johns Hopkins off and on for like 20 years. I have some cat sized cockroach related psychological trauma I've submerged that I'm not about to bring to the surface because someone decided to put an actual picture of how they get the neuropeptides out of the cockroach. 

However, every couple of years someone does peptidomics on cockroaches and I do not know why. I probably won't ever know. Hopefully it's some important model or something. I looked and it doesn't appear to be the same group. 

I'm sure they did a great job on this or it wouldn't get published in JPR. If you want to read it, here is the link. Have fun. 

Oh yeah, and I didn't hallucinate this. I just searched "cockroach peptidomics" in JPR's search bar. 2 there in the last 5 years alone! 

Gross.

Pathogenic demo drops on Steam on Halloween!

 

Listen up gamer nerds. You can keep playing whatever dumb thing you are currently playing - or - hear me out - you can get a free demo for Pathogenic where you play as a pathogen trying to infect a host!! 

Demo goes live on halloween!  More info here.

US HUPO ABSTRACT SUBMISSION DEADLINE IS TOMORROW! 10/24/2025!!!

 

For all of you excited to leave a decent pile of what you might consider your human rights behind for a few days of fun in the Middle West you better get on it! 

Abstract deadlines are TOMORROW! 

Get 'em submitted so you can go to Missouri!! 

MaxQuant + SDRF enables great reproducibility with no downsides whatsoever!

 

As everyone in proteomics already knows there is absolutely no downside whatsoever to using MaxQuant for every proteomics experiment. It's super fast, visually stunning and modern, incredibly stable and gives you all sorts of insight into your experiments when they succeed and those exceptionally rare times when it just stops running 18 days into analyzing those 4 files.

How could you make our field's very favorite toolkit even better? No way, right? Oh. Do I have one for you. What if you could also get your metadata out in the soon-to-be-mandatory (those are single dashes, no generative AI here - every word on this blog is typed by this one weird dyslexic guy) SDRF format? 

BOOM. Direct SDRF! 

Nature Comms?!? Whoa. What a demonstration of how great it is to work with MaxQuant that you can score a high impact publication by getting it to export a .JSON table in the format you want!  Heads up editors - one of y'all is about to see our SDRF exporter for Proteome Discoverer as soon as I get an hour to figure out what computer I made it on! Heck, I'll throw in one for Metamorpheus too! Supplemental methods.

Disclaimers: Proteomics metadata is something we should be uploading properly. We arent. Hell, I'd say 75% of proteomics experiments aren't even having their data put on public repositories. My job is to draw attention to things by generally being a nuisance about it. Anything that makes getting data deposited with appropriate metadata is a very very good thing. Thank you to these authors for their work and effort. 

There's a whole book on immunoproteomics / immunopeptidomics!

 

Okay, this one slipped by me. I had a weird 2024...and 2025 isn't going to set any records for normalcy.

It all seems brand new to me. There is some smart immunopeptidomics using TOMAHAQ derived methods as well as the clearest description of the ThunderPASEF technique I've seen. The lead in for why you'd want to do immunopeptidomics in the first place might be the star, though. Totally worth checking out!