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Talking About ICON

ICON went limit up for the wrong reasons. Some are saying its because the terms were so convoluted and that not many know how to calculate the capital reduction. PLEASE, this is not your first rodeo, this is not the first capital reduction exercise. You are all supposed to be professional investors or advisors. The limit up was obviously caused by the 50 into one capital reduction. A simple calculation showed that ICON had 2.377bn shares. Divide that by 50 = 47.5 million shares. That must be close to the fewest number of shares listed by a company on Bursa. The complaining parties ARE those who SOLD SHORT unwittingly. Imagine you had 100,000 prior to the ex date. It was trading at 4 sen, you value was RM4,000, and as with 99.9999% of ICON investors, you had lost money already big time. Then you woke up and saw the price going limit up. (0.035 x 50) + (100 × 0.105) ÷ 101 = 0.12 Add the 30 sen limit up, you have 42 sen, today add another 30 sen you have 72 sen. As you can see even at 72..

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BioPlex Update Preprint — 5,500 New Protein Interactomes — in a new cell line!

Ummm....so on a scale of 1 to BioPlex -- how big is your big proteomics data? Holy cow. You know, sometimes when you don't hear about these huge proteomics undertakings its easy to think "maybe they thought the first 10,000 human proteome interactomes was enough..." NOPE. BioPlex is alive and well and providing human protein protein interaction data at a pace that doesn't quite seem possible. Proof? Check out this new preprint! Not familiar with BioPlex? It is a bulldozer type approach to human protein interactions. Instead of doing something complicated and elegant -- why not just synthesize every open reading frame in humans and do an expert level immunoprecipitation -- mass spectrometry experiment on them. Yeah -- every one! BioPlex 3.0 showed about half the theoretical human proteome. For real. It is a project so big and ambitious that is is easy to forget about. How do you take this another step forward? Well -- you throw in some different cell types. And instead of lo..

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CyTOF data on single cells for 281 cancer patients with long term clinical data!

Somewhere around you, possibly within walking distance, depending on the relative funding level and decision making skills of your administrators is probably a big grey and orange box like the thing above. I'd be comfortable betting you $1.14 that it probably isn't doing anything right this second. This box is called a CyTOF and -- I swear -- it has all sorts of promise, but it's a bit of technology that is seeking a real application. And I'm going to jump on every paper I see that suggests we may have finally found it. Imagine that you're doing flow cytometry. You've tagged your cells with a couple of proteins and these proteins have a dye on them. The instrument measures the intensity of these dyes as the cells are sorted and you basically get single cell data on the intensity of these two proteins in each single cell. Now upgrade that idea and replace the dyes with protein tags that you can see with a mass spec. The cells get sorted, go through, get ion..

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Announcing the First Ever News In Proteomics MineAthon (Challenge)!

I have been working on yet another crazy idea off and on for a month or two and it's now almost (like 18%) fully organized. I'll stand by these words all day. Proteomics hardware is about mature. Yeah, we'll get some cooler stuff down the road, but until we figure out how to fix our informatics problem -- who cares if you get 3% more peptide IDs or 10% more spectra? Most of the tools people are using are only converting a tiny percentage of spectra into biological findings. There is much more to be gained with smarter data processing than even applying phase constraint over a wider mass range. In the most popular data processing pipelines people aren't even looking for PTMs, because it's still really hard to do it. SO....Let's see where we are right now! Do you think your data processing pipeline is the best for finding important biological changes and PTMs? Want to prove it, participate in some cool human research, be on a cool paper, a wold-wide webcast ..

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Announcing the First Ever News In Proteomics MineAthon!

I have been working on yet another crazy idea off and on for a month or two and it's now almost (like 18%) fully organized. I'll stand by these words all day. Proteomics hardware is about mature. Yeah, we'll get some cooler stuff down the road, but until we figure out how to fix our informatics problem -- who cares if you get 3% more peptide IDs or 10% more spectra? Most of the tools people are using are only converting a tiny percentage of spectra into biological findings. There is much more to be gained with smarter data processing than even applying phase constraint over a wider mass range. In the most popular data processing pipelines people aren't even looking for PTMs, because it's still really hard to do it. SO....Let's see where we are right now! Do you think your data processing pipeline is the best for finding important biological changes and PTMs? Want to prove it, participate in some cool human research, be on a cool paper, a wold-wide webcast ..

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ShinyGO! A beautiful, simple and powerful online data interpretation tool.

I didn't want to write about this one until I got these stupid manuscript edits out the door, because I needed ShinyGO and I didn't want anyone else slowing it down. "Minor" edits done! Tool sharing time! You can read about ShinyGO here. Don't feel like reading? You can play with ShinyGO online here! Are there lots of tools like this out there hiding on the web? Yup! There sure are, but this honestly might be the easiest way to dig through a bunch of different resources all at once. You will need to get your protein list to universal gene identifiers or something similar (it'll translate several different types) and then you can start doing all sorts of analyses. For the figure above, I let ShinyGO select the closest related organism (ended up being Scumbag Arabidopsis) and I flipped through different databases until I got some visualizations that made my data make some sense (turned out being a nice visualization of KEGG resources with pathway representation scale..

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At long last — A Guidance Document for HLA Peptides!

Twenty somethig years of analyzing HLA Class I/II antigens with mass spectrometry and we need to face facts -- mass spectrometry of these things still sucks. Important? Yes. Super incredibly important. But mobile protons are NOT a fun thing for us to work with as our exclusive charge acceptor. Our technologies work best with doubly charged peptides that end in lysines or arginines. But LCMS is the only thing that has ever worked at all for these molecules, so we're stuck with them. What we need is a set of guidelines to at least reference --- and here is the first one I've seen! Honestly, I expected this to have maybe 50 different authors on it as some sort of an over arching consensus of a big meeting on the topic to sort it out. And, that might make me a little more comfortable, but I tell you what, this document is not bad at all. You know why? This group has actually validated some HLA peptides and successfully utilized them! This isn't a big hypothetical piece. Th..

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Making publication ready annotated spectra with IPSA and PD (or any other tool)!

IGNORE MY WRITING. Make beautiful MS/MS spectra easily online by pushing this hideous big button. Lookin' to make beautiful spectra for your poster or publication? Just push that big button!! This might, yet again, be a post mostly for me, because I can't seem to remember the name of this tool and I keep going to Google Scholar and looking through 2019 papers from the Coon lab. And that isn't exactly a one-paper-a-year sort of lab. And since I'm already typing I'm going to show you how to use this awesome tool. You can read about in in MCP here. If you need to do something fancy, beyond what the online IPSA tool can do, you can download the whole thing on Github here and manipulate it (or run it locally in your own web browser on your offline computer. I'm going to go through this from a Proteome Discoverer centric application using IMP-PD 2.1 (the free PD version that you can get here.). Sweet! Here is a great tutorial for installing it, I'd not ..

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Context specific FDR for top down proteomics!

On the bridge of the Starship Northwestern, Captain Kelleher and his crew are exploring the farthest reaches of proteomics, going where no lab has gone before. I just had the worst idea ever -- and -- of course there is an internet tool where you can take anyone'es picture and "Trek" it. (...sorry...) What started this ramble? Well, while we're here on earth still struggling with accurate estimation of FDR for linear and slightly modified peptides, on the Starship Northwestern they're beaming down tools for accurate estimation of FDR for freaking intact proteoforms! How are you currently assessing FDR for proteoforms? I'll tell you how I am. I'm not. I'm so pumped that I've identified a few dozen proteins from fragmenting their intact forms that I'm just popping them into my list. And -- I'd wager that is what basically everyone is doing outside the 4 labs that do top down proteomics each and every day. And if you've got an exact mass..

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Quantitative live cell imaging + proteomics shows real time influenza progression!

This brand new study at Nature something or other is timely, interesting and shows a combination of techniques complementing each other I'd never have thought of! Live cell imaging? That can't help with proteomics...I mean...how does looking at the surface of a cell help? It turns out that, live cell imaging (light microscopy!) has gotten massively sophisticated! Those images at the top are Rab11 foci! So... live cell imaging of protein complexes inside a normal human cell. That's pretty awesome all on it's own, right? What could make it even better? Involving proteomics, obviously, but -- you know -- let's leave that part out of the title. THEN let's do something that is right at the front of everyone's mind right now -- influenza!! If that doesn't make you want to read this, we probably can't be friends. Rab11 is a protein that maintains other proteins at the cell surface and helps recycle the vesicles. This group shows how the influenza ..

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