Everything you ever wanted to know about the Proteomics Standards Initiative

This is an interesting and thorough breakdown of the Proteomics Standards Initiative, one of many groups that is trying to demystify this awesome field of ours to outsiders (Open Access).

Tonight's plan! Learn Compound Discoverer!

Okay, that's a pretty cool icon, right?

What do Proteome Discoverer and Compound Discoverer have in common (besides the obvious overuse of the letter "r") a ton of stuff!

Compound Discoverer looks just like the Proteome Discoverer interface. The goal is to make it easier to move from one data processing workflow to another without having to learn a whole new set of interfaces!

See? It looks just like it. You set up studies the same way, you import data files the same way, and then you just have to figure out what all those funny nodes do!

I'm about to put metabolite ID and metabolism via mass spec on my resume. Even better, when they get around to launching Compound Discoverer 2.0, then this interface can easily do straight up metabolomics.

By the way. If you have used this program (or bought it and would like to learn how to use it) this is the place to find info: WWW.myCompoundDiscoverer.com

Honestly, I wrote this whole post so that the next time I Google: MyCompoundDiscoverer, it'll take me here and then I can direct link to the page. There are instructional videos and overviews!

PRIDE Inspector -- Meta-analyze all the things!

Apparently, I've downloaded several versions of the PRIDE inspector, because I have many .ZIP files in my Downloads folder. For some reason, though, I don't guess I've ever Unblocked the Zip file, unzipped it, and then ran it...or I forgot that I did...

A brand new paper in MCP reminded me of it, and I'm assuming that this means that the newest version I just downloaded is the best one anyway (available here!)

Part of the title of the new paper, btw, is:...moving toward a universal visualization tool for proteomics data standard formats...cool, right?

Some people save their data for archiving in some format or the other like: mzml, mxml, mzxml and xzmlzxmllxmzmlllllllzzMxxmz (just to name a few). These have all been very valiant attempts (except that last one. that guy was just being a jerk) to keep our data in a format that would standardize things, so we have all the important data in one place and we can compare instrument -to - instrument.

Problem is, there have been several of these. And it can be daunting. Say, I'm dosing these cells with a super cool drug and I want to do proteomics on it and I see that another lab previously dosed mice with that drug it might not be easy at all to compare that data.

See where I'm heading?

The PRIDE Inspector reads basically all of these data formats (except that last one. the inventor has been shunned by the field this morning). You get a nice graphical output of the spectra and if the data is processed you can check the results at every level.  Perhaps even as nice, if the study is saved in the PRIDE database you can link directly to the dataset.  Just want to rapidly meta-analyze everything in the PRIDE repository? This might be your window!

Now, notice the fact that the title includes "moving toward a universal". I'm sure that means there is more to be done here. But it looks like a albert heck of a nice start.

Article in The Scientist: Cracking the Complex

The Scientist has a really good article this edition on top down proteomics for protein complexes. If you don't get the free magazine delivered to your house, you can read the article here.

Highlights? Overviews of a bunch of different researchers current work...oh...and the suggestion that Northwestern has hacked a QE HF to have extended mass range along the way of the Exactive EMR!?!?!  and it has the capability to do pseudo-MS3s!!!!!

Want to do proteomics with epidemiologists? This paper aims to create common language!

Image Source: 4designersart/Fotolia.com (lifted from this article)

Epidemiology has been one of those big things for a while. In my mind it seems like it kinda blew up the same time all this -omics stuff did. Schools have been putting lots of money into both the last few years. On the outside, it seems like they're opposite things. They are looking at trends in human beings to find disease patterns, while we're looking extremely deep into the disease, or people with the disease.  However, now that we can get deep proteomic coverage in single runs, does this open us up to working together?

More and more, it looks like the answer is a resounding yes!  For an overview of the topic you should check out this review: Epidemiologic Design and Analysis for Proteomic Studies: A Primer on -OmicTechnologies.  It is open access and tries to bridge the gap.

I think it is very nicely written. While it is geared more toward the epidemiologists, in telling them what we do, it highlights some studies where the two were combined well. If I wanted to do a big study of some disease popping around in a human population, I wouldn't know how to sample people in a statistically valid sense. "Hey group A, you have the disease, right? You're TMT channels 126-129!" 

Their job is to assess the factors that are important and design the experiment in a significant way. And then pull the right data out of the final protein list to show what is important. Turns out half the people here also suffer from a second disease? That's a nice data point to have so we don't draw a spurious conclusion, right?  And there is something useful to be gained from that knowledge post-data processing? Even better!

This way we can focus on getting good quantitative protein IDs. And...if someone wants to explain what we do in terminology geared toward my collaborators' specific fields? Well! then I can send them this open source PDF to clear up some misconceptions before we sit down at the table and start designing this killer study!

Global glycopeptide quantification!

I just stole this right off a Twitter feed. Left the Tweet intact, even! (Thanks, Julian!)

Okay, this paper is obviously awesome. It goes after some biological question and it comes up with some great insight. Unfortunately, it contains a lot of words I don't know and on this lovely Saturday afternoon I don't have the motivation to do the research necessary for me to fully appreciate what they are going after.

Why should you check out this paper? Cause its pure spectralporn! I can say that, right? They say "foodporn" on network TV all the time! I mean, its like foodporn for LC-MS/MS spectra!

Seriously, though, check this figure out (click to expand)!  This is some nice looking data!  Benjamin Parker et al., out of the University of Sydney know what the heck they are doing.

A pan-cancer proteomic perspective on The Cancer Genome Atlas

Okay. (Ben slowly gathers thoughts...)...

Now I'm going to tell you about a paper that is so cool that even though I have no idea how they did it, I still think its worth sharing.  I'm hoping I'll figure it out as I write this.

First of all, its Open Access (yay!) and available here!  Second of all, its cool enough that 2 people sent it to me since it came out and this morning I thought I'd get it on the second read through.

What I do get:  The Cancer Genome Atlas is not a leather bound book that sits in a room that smells of rich mahogany....

...instead, it is a huge cohort of clinical cancer samples that have have been or are in the process of being studied with a ton of different genomics techniques. The homepage of the project is here.

Browsing through the papers that have been done on this Atlas (to construct this Atlas? that makes more sense...) shows that there is a lot of bioinformatics firepower at work here.

So...in this study this group took these samples and did an interesting protein array analysis of them. This is where I get foggy. The array they used is called an RPPA. This is a Reversed Phase Protein Lysate Microarray (wikipedia link) (and if are a Jove user, or care enough to register for a free trial, here is a video that shows how an RPPA works.)

Okay. So they are using fancy antibody arrays to show the presence/absence/abundance of proteins.  Got it. What do the arrays detect? Well, they went for a whopping 181 antibody probes! Wait? What? Just 181 targets? And the targets were selected based on what we know of current cancer pathways and stuff. My assumption is that the arrays are very fast and/or very cheap...or we would have done this with a mass spec and looked at hundreds of targets with PRM (people are routinely doing 700+ per assay these days on Q Exactives) or more with SRM, right?

But this is where it gets impressive -- monitoring all 181 targets on these arrays they looked at over 3,000 different samples...which is a lot...   And these samples have been previously clustered by neat things like disease type and primary driving mutation.  So, you can see how different genes interact with hundreds of samples of the same disease that follow the same -- or different cancer driving pathways.

Take home point for me is: For you guys out there generating insane amounts of clinical data, we need to steal more genomics tools! Cause these guys seem (at least...to an outsider...) to be able to do stuff with the data!

Discoverer International User's Meeting!

Hey! I meant to put this up a bit ago. This was one of my favorite events all last year. My attendance this year isn't all that likely....though not out of the question yet! I still have vacation days. We'll see.

You can register here. Warning, if fills up fast!  Oh, and this is what Bremen looks like in December...

...yeah, it totally sucks...

Downstream analysis of proteomics data!

Alright!  This painfully thought-out and beautifully executed experiment yielded a big list of differentially regulated proteins! Woooo!  So...now what....?

This review from Karimpour-Fard et al., is a great place to start. This concise little piece in Human Genomics walks you through some tools and approaches that can help you figure out:

1) What is significant in your list (and all the stuff that isn't)

2) What those words mean that that stats and bioinformatics people are always using (Anova?)

3) How to extract some biologically meaningful data out of all that stuff.

A nice short review (and Open Access!) that might help you make that next step forward!

Shoutout to my aunt Beth who took this cool picture downstream off a bridge near home!

SIM-XL -- Lets identify those crosslinked peptides!

Mapping protein-protein interactions in complexes is a tough job. We can go one of two ways with it:
1) The relative way: When I pull down proteins under condition A and under condition B,  I see relative upregulation of this protein, so it must be associated
and
2) The crosslinking way: Under these conditions I throw in a crosslinking compound, then pull everything down, digest and identify the crosslinked peptides.

Both are hard, but the relative way is a good bit simpler from the data processing perspective.  Analysis of crosslinked MS/MS spectra? Thats hard. There are some nice approaches like XComb and StavroX/MeroX.  SIM-XL is a new one. If you wonder why you might want to try a different piece of software, look at this result output (click to Zoom!)

Um....how frickin' cool is that?!?!?

Its a GUI driven interface with all sorts of cool graphical maps to help make sense of your crosslinked data. It'll accept all sorts of MS/MS converted data files (and if you've got the MSFileReader installed, it'll directly read Thermo .RAW files!)  and its even possible to map your data against spatial constraint data obtained from 3D protein structures to see if what you are seeing is possible at the biological level!

You can check out the SIM-XL website here.

And you can find the original paper by Lima et al., here.