opt-TMT -scale down everything so you aren't wasting so much reagent!

 

There is another optTMT, but that one doesn't have a dash and it's for designing smart multi-batch mutiplexed experiments. You can read about that one here. 

This new one is about how a lot of TMT labs are labeling 400 pounds of peptide (181 kg) and then injecting 200 micrograms per injection on their Orbitraps and 1000 micrograms on their Astrals. 

If you wanted to just label 10x more peptide than you'd possibly use instead of 10 million times more peptide, how would you do it? That's what the dash is for! 

While this might seem just a little silly since there are protocols out there that have been replicated dozens of times for labeling single human cells, they are actually a lot more convenient than you'd think. We know how much reagent in our lab to use for 1 cell or 25 cells and it's a drag when we have to break out the peptide quan kits and borrow someone's plate reader. This study gives you that in-between concentration fully optimized. 

Another funny solvent is better than formic acid for proteomics?

First off -- 

CHECK WITH YOUR HPLC MANUAL OR MANUFACTURER!!

Is the resolution of GIFs getting worse all the time? If so, it's the only change I've personally seen from this whole "AI revolution", except people saying "I asked ChatGPT" when they would have said "I did a Google search" back before Google reorganized and put their search algorithm teams under the control of their marketing teams. True story, that's why Google really doesn't work well anymore and AskJeeves is back, but now it needs more electricity than all of Spain will sue this year to look up stuff on Wikipedia for you. 

Okay, so someone at some time decided formic acid was a pretty good compromise. Pretty sure it was people in the John Yates lab. TFA gave you the best possible HPLC peaks for peptides, but it lowered your ionization efficiency. Acetic acid gave you the best ionization efficiency but if you were doing MuDPiT (which was a 2D chromatography system for proteomics best left forgotten today but it provided unprecedented proteomic coverage with the awful HPLCs we had at the time), acetic acid messed up your peaks too bad. So...formic acid it is.

Worth noting, formic acid has some drawbacks like poor stability in light, particularly when diluted. So when a lab dropped a paper showing acetic acid should be revisited, we jumped on it. My lab doesn't use formic acid in our HPLCs at all. We do have vendor permission and we have several thousand runs to demonstrate it hasn't been a bad idea at all. 

So when I was contacted by a researcher who was like - "yo, we have something better!"  we borrowed someone else's HPLC and tested it out. In our hands on (nanoflow) it's only marginally better than acetic acid, and possibly so marginal that on the sub-nanogram loads it wasn't significant by student's t-test. I forget, and Cameron actually did the work while I was visiting collaborators. But when you crank up the flow rates? 

Okay, so someone at some time decided formic acid was a pretty good compromise. Pretty sure it was people in the John Yates lab. TFA gave you the best possible HPLC peaks for peptides, but it lowered your ionization efficiency. Acetic acid gave you the best ionization efficiency but if you were doing MuDPiT (which was a 2D chromatography system for proteomics best left forgotten today but it provided unprecedented proteomic coverage with the awful HPLCs we had at the time), acetic acid messed up your peaks too bad. So...formic acid it is.

New Nature Genetics study comparing pQTLs is....worth reading....

 

Ummm.....so...Imma just leave this here and not talk about it any more, maybe. Wait. Maybe just this - if your technology is producing results that can be validated 30% of the time then you could save a lot of time and just pick a gene or protein and flip a coin and go read up on other technologies....

DIA Multiplexed proteomics with off-the-shelf TMTPro reagents!

 

This is obviously interesting - and surprisingly easy to pull off. The data is processed in FragPipe and one of the output sheets is put into these python tools to identify the complementary fragment ions. 

I like the figure above because they use 2 very similar peptides labeled with TMT and demonstrate that they can clearly find clean complementary fragment ion pairs. Oh yeah, here is the paper. 

They really really don't want to do any spectral deconvolution so they only used 3 of the TMTProC tags that give them clusters of complementary ions 4 Da apart. The open suggestion is here the whole time that if you aren't afraid of deconvoluting your complementary ion clusters - you can obviously do more than a 3-plex DIA experiment. 

This is a really nice read with the appropriate controls included as well as a way to dramatically increase the throughput of some DIA proteomics workflows on basically any mass analyzer. Worth a read for sure. 

If you type "TMTc" into the blog search bar you'll find a lot of stuff over the years. This is one old post that goes more into what this is and why it can be valuable. 

Y-MRT - a new prototype TOF with 1 million resolution and 300 Hz?!?

 

Ummmmm......okay so....these specs are amazing....

How do you increase mass resolution? Generally just increase the flight path, right? But you can only go so far before there isn't enough electricity on earth to generate the appropriate vacuum. Reflectrons double the path and the W-TOFs from Pegasus that a big vendor acquired recently can really push those numbers up by multiple reflectrons.

The Y-TOF takes that concept to 11. It's one thing to say "I can make my instrument do 1 million resolution". Give me 45 minutes with your Q-Exactive and I can make it do 1 million resolution. Each scan will take about 8 minutes. (more like 4 seconds, I forget) but it's completely impractical. 

AND you can tune a Time of Flight to get really good mass resolving power at one particular m/z. My Q-TOF gets incredible resolving power in a mass range that isn't exactly where I need it.

The Y-TOF did a 30 minute proteomics run and averaged 600,000 to 800,000 resolution across the usable peptide range!!!  

AND sub-PPM mass accuracy. Parts per BILLION mass accuracy. ON A TOF. 

Obviously a prototype, but more obviously something we should keep our eyes on. Worth noting, they do have to use Astral level loads for bulk proteomics (1 microgram of peptides for the best data) and that this prototype isn't going to smoke your recently purchased $1M instrument, but it's starting in a very nice spot. 

Prosit-PTM! Deep learn modified peptides???

 

We all know other great protein informatics teams are working on the holy grail for DIA proteomics - deep learning and prediction of modified peptides.

Am I extra excited because the team that gave us Prosit is working on it? Yes. Yes, I unfairly am, when I should be evaluating this preprint purely on it's own merits and not the historic success of one of our field's most historically reliable teams.  And not just because of their informatics skills. What makes me excited the most is their long history of making tools that anyone can use. 

Check out this preprint here! 

Breaking through barriers with an Orbitrap-TOF instrument!

Thanks to all the journals allowing Open Peer Review and allowing me to sign about half of the 30 or so papers I've reviewed recently, it's pretty clear to people how unproductive I think things like this title are. Even if, as in here, I really do like the paper. 

I think I'm just old, for real, but I do think that if you've got cool biology in your paper but you've got the instrument front and center you're doing yourself a disservice. 10 years from now that instrument is going to be $100k from second party vendors or $50k on Ebay without an ionization source or accompanying PC and no one is going to look at the biology in your paper. 

However - this is some pretty amazing crosslinking data. And that's my point, I guess. It's a nice study. FAIMS helps a lot with crosslinking on both an Eclipse and Astral, but stepped collision energy - while helpful on the Tribrid, has minimum effects on the Astral. Higher CE helps a lot. There is also a neat toolkit I heard mentioned at iHUPO called "Raw Vegetable" which I assumed someone said but actually meant "Raw Beans" (which I love). You also get a cool step-by-step breakdown on how to optimize crosslink data analysis in Proteome Discoverer. They do some filtering inside the software and break it down at every level. Super helpful for anyone using that toolkit in their lab (I think they use 3.1).

Worth noting, they used the freely available MSAnnika node for the crosslinking, which is pretty cool to see it in use - and optimized through. 

Plasma fractionation increases proteomic coverage!

 

Y'all aren't going to believe this one. For real. 

Everyone out there complaining about the number of proteins you can identify in plasma proteomics and no one has ever tried fractionating it??? What is wrong with us? 

Whoops. Not everyone got that this is a joke. Okay...so...literally for all of time everyone has fractionated plasma in some way to get higher coverage. That makes the title funny. 15 years ago I was running SDS-PAGE gels and cutting fractions and running them separately on an LCMS. 13 years ago I was using something called an OFF-GEL to first fractionate the proteins at the intact level by isoelectric focusing and then digesting the proteins in those fractions and fractionating them at the peptide level to get proteomic depth. The problem with fractionating is that mass spec time is expensive and if each sample takes 144 hours to analyze (example) you only complete 60 samples per year if you never run a QC, a blank, your instrument never needs maintenance and you work every single day of the year. The UK Biobank study 1 would take 833 years. Most people aren't that patient and we're all sort of looking for ways to get a lot of samples analyzed before we retire. 

Hypothetical multiplex tag works in single cells?

 

Did you know there are other tags out there for multiplexing proteomics? Younger people probably don't and I can't tell you where they to get them because I actually truly can't. Let's change the subject entirely.

Did you know lawyers are seriously expensive? Like, for real expensive. If you're struggling with science salaries maybe you should check it out. Okay, let's go back to this paper.

If hypothetical multiplex tags did exist in some places where I couldn't tell you about could those ficitious tags be used for single cell proteomics?  Are you thinking....ummm...yes...? why wouldn't they be? These people found a team of peer reviewers who thought it was useful to check - at Analytical Chemistry! 

And they compared it directly to the commercial reagents that we all know and love. They used the same intrument Orbitrap Fusion Eclipse using 120kDa MS1 and 30kDa MS/MS. The only difference is that the fictitious tags that don't exist and if they did I couldn't tell you where to get them used a slightly lower m/z cutoff. They also optimized at 5:1 tag to peptide. 

For the experimental design here, they sorta mailed it in. 3 tags were used for cells and other tags were used for different controls. They also optimized the carrier to single cells by basically not saying "...why would it be different for one multiplexed reagent when 15 different papers already optimized this on the same Orbitrap hardware...? and said you could go about 100x - 200x to one? 

Then they actually did some interesting stuff by labeling mouse spleen cells with 13 of their available 16 channels. The most interesting part is where they find that if they don't use FDR at all ("set to 100%") they can get 12,543 proteins in mouse spleen cells!!! Someone said "ummm....wtf....you need to use FDR..." and they get 3,991 peptides and 3,602 proteins. So....1.1 peptides/protein on average. Ouch. The FDR calculation scheme is ...nonconventional.... and I almost want to download their data and reprocess it in FragPipe and see if the data is good but the data analysis is unnecessarily strange. Oh. The fun I had when I had free time....

Interestingly, however, the authors get those 3,602 one-hit-wonder proteins to clearly separate the different cells in the mouse spleen into their originating cells, and generate a beautiful T-SNE or U-Map plot, and that's what we came here for anyway, right? The authors suggest some follow-up experiments where they plan to combine both their tagging solution with the amazing commercial one....

GlyCounter - find all those glycopeptides whether you fully sequenced them or not!

 

If you've ever tried to look for a glycopeptide in any type of MS/MS spectra you know how very very rare it is that you get all of the information that you're looking for.

If you want to get full sequence coverage of everything it's probably going to take ETD and 2 different energies of collision dissociation of some kind. The clever combinations of energies certainly help get you more fragments, but they also increase the background complexity. "Is that 8ppm away from the b5 ion or could that actually be the NeuNaC is the third sugar in which case that's the 3 ppm off of the z4 after the loss of a less likely HexNaC at the end? (I possibly made that up because it's equally funny to me if that is a chemical impossibility or if it isn't).

Do you need ALL the info, though? Sometimes I just want to know things like "did this drug increase the number of spectra with glycan related oxonium ions".  I definitely do want to know more than that, but that's what I know how to do with some clever R scripts Conor Jenkins wrote me almost 10 years ago. 

How do you get to real information - and spectra - for glycopeptides in your data in an easy way? 

You don't.

Until now! Hello GlyCounter! 

You're probably assuming "cool, now I just need someone to download some crappy python scripts, fix them and then make me a dummies guide on how to run them." 

NOPE! Check this out! I wouldn't write about it if it was the python thingy, probably.

It's a slick little GUI that takes straight RAW files or mzMLs! Click your options (including whether you used UVPD or ETD(!!!!!!!) and it does the rest, including kick out handy IPSA annotated spectra! 

Important - if you are using a non-Thermo format and convert your data to mzML you don't want to compress them. In MSConvert, turn off that thing. Honestly, that thing messes up a lot of other workflows. If you're converting through FragPipe, it might convert them by default depending on what version of FragPipe you're using. 

Gotta run, but if you need a solid new and approachable toolkit for glycan modifications, you should absolutely check this out. 

Broadly inhibiting PFEMP1 antibodies sequenced from a single child!!

 Sneaky HUGE PAPER ALERT! 

Direct PNAS link here. 

This is so so so cool. Here is the thing, PFEMP1 is this molecule that covers the surface of malaria parasites and it does something very similar to what our antibodies do. It switches domains around to that it is incredibly variable. For a while it was thought that because it's so huge (I forget but I think it's 600kDa or more) it might be able to switch around more than our antibodies.

Back in the 1990s or something the amazing Michal Fried was getting malaria samples from women in Africa who had gotten malaria while pregnant - multiple times. The first time was generally really bad. Like as bad as you could imagine, but if the adult survived the next time she was pregnant she and her baby were basically immune to all forms of Plasmodium falciparum malaria (maybe the others, I'm not sure). Those data basically proved that a malaria vaccine could one day be engineered and today we do have one. It's insanely absurdly difficult and expensive to produce, but it exists. 

It also shows that the human mAB can outcompete PFEMP1...somehow.... and if we could just exploit that gap in flexibility between our immune system and the protective systems of the parasite - we could have much easier to produce vaccines. 

But you can't sequence polyclonal human antibodies....right...? This team seems to have!

If you're interested in the story I mentioned above -- 

https://www.nature.com/articles/27570

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! 

The first credible quantitative analysis of 7,000 proteins in human plasma!

 

Over the last few years I think I've seen 5 different company presentations that have been in some sort of an arms race - purely with one another - to deep dive in human plasma. This one now says they can get 3k proteins in human plasma, so the other one says 3,500 and the next one has to say 4,000, and then 5,000. I've visited a couple of them personally and the scientists running the samples seem to spending most of their time trying to find new jobs because their executives and their sales teams seem incapable of shutting the fuck up for even 10 seconds and are pulling these protein coverage numbers out of their own lower body cavities. Yo, if you're ever in a company as a scientist and you answer to the sales team - get out. 12 years hanging around mass spec companies and I've never once seen that setup work. 

Back to the paper! 

Among all this noise there is have been credible developments in hardware and chromatography and nanoparticles. And what if you really sat back and had people who are experts at all of these things work together to see what they could really do? 

The best case scenario is that you might end up with something like this. 

This is the newest (that I'm aware of) generation of Seer Proteograph linked to some fancy uPAC 50cm chromatography running at 1uL/min on the Orbitrap Astral with about 30 minutes/sample and a similar but slightly longer method on an Orbitrap Exploris 480. 400ng of peptide (wow! in my mind now, that's a lot, but people put a lot) on the Astral and something in that range on the Exploris. Feels like they tuned in the chromatography on the Exploris and then the Astral arrived and they were good to go on the chromatography side.

Of course, the nanoparticle corona stuff had to be done up front and that's where the magic is to get past those 22 pesky proteins that make up 99% of the blood plasma.

Now - this isn't a SomaScam /Illumina Protein Crap experiment where you can detect all sorts of stuff but you can't quantify any of it because your aptamers have a dynamic range of 2 (not orders, 2). This group spiked in bovine proteins at different levels in different samples and determined how well they could quantitative recapitulate the expected ratios.

Turns out it works really really well, with or without depletion. For real, there were legit analytical chemists doing legit analytical chemistry here and it makes this whole workflow seem very very smart. 

Sure - this isn't an inexpensive workflow. We all know everyone complains about the Seer kits. I hear rumors of $250-$350/sample all the time. And an Astral isn't cheap, but we're talking about 48 samples/day so 336 samples/week before controls? That's more than Illumina's solution can do. And - sure - an Astral isn't cheap but neither is an Illumina sequencer (which you also need for O-Link)

It's been a pretty good week for plasma proteomics for mass spectrometry.... and sure, I'm biased, but if we're competitive from a price, thoughput and coverage perspective, the other technologies seem more than a bit silly. 

Are data streams the answer to proteomics processing bottlenecks?

 

I don't know if this is the answer to some of my most pressing current problems, but it does seem silly dumping all the data at once into something that can't handle it.

Maybe if we treat the data as a stream(?) we can allow it to be progressively processed(?)

In their models they get up at a 3-order(!!!) of magnitude(!!!) increase in speed when processing large (and largely simulated) datasets up of tens to hundreds of thousands of proteomics samples.

Worth at least thinking about, IMHO.

It's official! The Proteomics Show will be live at International HUPO!

 

Since no one asked for it (and ASMS ignored me)! 

THE Proteomics Show will record live at International HUPO on Monday, November 10th. Main poster/exhibit hall at 2pm (I think!) 

It'll be me for sure and one super secret special host - and audience participation! Woooo! Toronto!