Need insanely deep coverage? Try fractional diagonal chromatography!

Told you guys I was going to get caught up on the literature again!  My day job hasn't been getting less nutso and I've been falling behind on the fun stuff I want to do.  Sometimes you have to pull a fun all-nighter backdating blog posts!

Anyway, this new paper in Open Proteomics shows us how to get EXTREMELY deep coverage of a sample.  Did you know there are over 450 known protein PTMs?!?!  Holy cow.  We need to stop thinking about just phosphorylations!  The problem is, these other 449 PTMs are also low abundance, but we don't yet have enrichment methodologies for all of them. What we need is extreme chromatography.

Our normal approaches for separation probably aren't going to cut it if we want true -omics coverage.  We're going to need combination approaches.  Separation at the top down level, followed by separation at the middle-down or bottom-up levels, as well as particular PTM enrichments.  If we are really going to be stepping into the domains of trying to track every PTM, we're going to need some extreme measures.  Definitely check out this paper here for a nice perspective on what we're dealing with and what strategies we currently have in place for going after weird PTMs.

Next gen sequencing traces the Ebola outbreak back to its source

This one isn't a proteomics story.  I really need to catch up on the literature.  I've got some long plane rides coming up so that should help.

Anyway, this is next gen sequencing.  And Ebola.  And a tragic story.  A study released in Science this week shows the use of next gen sequencing at 2000x coverage to trace the Ebola outbreak to a single person who picked it up from the natural reservoir.  The detail here is astounding.  I stole this pic from the article (it's open access, so I doubt I'll get sued.

It is a pretty conclusive story.  And it makes sense from an epidemiology standpoint, right?  From the news, we all know how tragic this outbreak has been and how scary it really might become.

To put the severity of this one in perspective, I think this speaks volumes:  5 of the contributing authors on this paper have died from the disease...

You can read more about this all here.

Prime XS -- Advance proteomics...and the rest of science

This is SO smart.  And we need an equivalent here in the U.S.  If you want to start it, I volunteer to head it.

Prime-XS is a program ran by the EU.  It ensures 2 things:  1) That proteomics is used for scientific studies of extreme merit.  and 2) That labs participating in XS are exposed to high quality, high impact biological problems.  It is a win-win.  Top notch labs get top notch collaborators and the EU pays for it!

How it works:  Researchers in the EU countries can apply for days of access to proteomics facilities that the EU has reserved for this program.  This is how it is currently divided:

Ummm.... 621 days are available in Utrecht?  That couldn't be the Heck lab, right?  Pretty sure it is!

What a win for everybody!  How many top notch proteomics facilities can you think of that have trouble finding high impact biological questions?  Tons, right?  Let's face it.  Cool problems don't always come from the same places where our best proteomics facilities are.  This fixes it.  In one fell swoop.  Top biological problems -- top proteomics capabilities -- and we all win.

The downside is that this program is going to push the power balance in impact factor toward Europe.  Meaning we need something like this over here.

You can read more about Prime-XS here.

Rough week around here

Last thing you want to see on your primary processing PC?  3600 sector errors.  Needless to say, it is slowing things down around here.

Fill a library with what I don't know about in shotgun proteomics

Today I got a super interesting question about Percolator and Spectral libraries.  While investigating, I figured I'd better go to the Percolator Google Group and see (btw, I think this is going to be very interesting to everybody!!!)

Anywho, while searching, I came across a whole lot of names I recognize who belong to a Crux-users Google group.  What the heck is Crux?  Umm....maybe an incredibly awesome and apparently free software package for proteomics?

On further investigation, I found Scholar references back to 2008(?!?!?!).  I feel less out of touch because it appears to be buried within the 1,000 programs and features that make up the Trans Proteomic Pipeline.

You can find out more about Crux here.  Feel free to tell me about it.  Back to Percolator....

Biomath calculator App

What is more annoying than doing that calculation above me?  Seriously!  I have x amount of protein in ug what is my molar concentration?  You are probably smarter than me, but I have a tendency to lose a decimal place or two.  This week I said "wow, there should be an app for that".  And there is.  Of course.

Promega has a free BioMath calculator that will do this one for you, among other things.  (Lots of DNA calculations and dilutions, but this is what I downloaded it for!)  It is available for both Android and Apple.

You can read more about it here.  A big thanks to the talented team at Protea for pointing this one out to me!

I've seriously considered this.

A friend forwarded me this picture.  I'm not the only person that loves this field!!!

Then I got this one forwarded to me as well (thanks Da Jules):

Cool targeted peptide quan article in The Scientist

I had to go to the doctor today for routine checkup stuff and what magazine was at the top of the stack? This month's issue of The Scientist!  That's just how Baltimore is.  There is probably almost as many scientists here as drug addicts.  My physician is probably married to a researcher at Hopkins or UMD or something.

Anyway, there is a great article this month describing how to move from discovery to targeted proteomics, as well as a description of each open source platform.  This'll come to no surprise if you've used it at all, but Skyline made the top of their list.

A couple of these platforms were new to me and it might be worth it to check out this nice little review.  Or even to forward it to collaborators.  It is concise and nicely written.  You can find it here.

Proteome Discoverer 2.0 teaser!

Look what I got this morning!  It is looking really really good too.  I got it to do some PC benchmarking today while hanging out with a sick dog.

I'll show the benchmarking stuff later.  I need to sort out some variables.  While I was doing it, I noticed a file was taking a whole lot longer than it did on PD 1.4 (or on earlier PD 2.0 alpha copies).

Check out what our friends in Bremen got working!!!  That one file did take longer than normal, but it was because PD was doing a bunch of other things:

If you've spent any time on the job queue on any version of Proteome Discoverer, chances are I just blew your mind a little.  PD is running multiple files at once!!!!  Now, it remains to be determined if those two matching 81% are because PD detected that I was reprocessing the same Fusion files just with different names, but the fact that it is intelligently allocating time is bound to make more than just me happy. I need to do some digging around.  I don't have all that many Fusion files and I'm running them cause they are the hardest to work with.  If we can do them fast, I'm not worried about the QE or Elite files.  Easy.  This RAW file has 16k unique peptides in it!

I'm running on a crazy fast PC (more details on that later, too!) but it knocked out 4 Fusion runs in an hour and 17 minutes.  I was experimenting with different peptide and protein FDRs and it just tore right through them.  By comparison, I just saw a big fancy dual CPU Xeon choke on HeLa files in PD 1.4 for hours.  Better hardware.  Better software.  And all the sudden these huge datasets everyone is generating don't seem all that scary!

BTW, wait till you see how PD 2.0 handles complex experiments! Thermo is about to release the best proteomics software we've ever seen.

Computing exact p-values to improve your shotgun proteomics

You know what I love?  When people start applying nice statistics to proteomics data.  A lot of these datasets are geting far too large for us to say "x is twice y".  But we all have a lot on our plates.  We can't just take a bunch of stats classes (believe me, I'm trying and I've already had to drop on that I paid for this summer...) in order to get caught up.  We need good, trustworthy, time tested stats built into our processing schemes.

Why not go for simple p-values?
Because, obviously, it isn't that simple, dummy!
HAHA!  But it turns out that it is!

JJ Howbert and Bill Noble think it is and they have some really good evidence.  Check out this paper (it appears to be open access) in press at MCP.

In this study, they went to the original Xcorr values assigned by Sequest and looked at the total score distribution across all the peptide-spectral matches.  At this level, they were able to determine the probability that their test hypothesis (in this case, the Xcorr value) was true, cause that's what p-values do.

When they went back and ranked their peptides by p-value, rather than Xcorr, they found they had a much more accurate measurement of PSM validity than merely saying "anything above an Xcorr of 2.0 is trustworthy" (which is what most of us have been doing all along, be honest, and we've all secretly known it was silly.  It's like saying a TMT fold regulation of 1.25 is significant.  It's just us being lazy....)

Awesome, right?  As proof of principle, they compared the same data set to a bunch of different engines and, predictably, this worked better than the other engines tested.

What about Percolator?!?!

This is where I don't know quite enough Greek letters..or at least when you're adding and dividing them it does a funny thing to my brain.  What I know this morning?  They were able to work this pipeline into Percolator and I fell asleep.  They come from the same place.  Of course it works with Percolator!