Spectral counting in Proteome Discoverer

The image above is stolen directly by/from Google Images.  First thing that pops up if you look up "spectral counting".  Anyway, I often get questions about using Proteome Discoverer for spectral counting.  I have some slides that I cut from an iORBI I attended several years ago showing spectral counting in PD and this is what I send people.

I'm not a big fan of spectral counting, but it does have its place sometimes.  The slides show you a comparison of spectral counting vs. quan of peaks at the MS1 intensity level as the limitations in dynamic range from spectral counting (with a very nice reference).

EDIT (2/1/17) New DropBox link!

Peaks 7

My first Xmas present arrived while I was on vacation.  Peaks 7 came out and I got a nice long trial license to check it out.  I installed it on my plane ride home.

The list of new features from Peaks 6 to Peaks 7 is kind of mind blowing.  This software is very very sophisticated.  It is supposed to be faster, it has online collaboration and sharing modules embedded within it and it now does label free quan (with really pretty heat maps).  There are a full list of the new features available here.

However, the features I'm most interested in checking out are described as "improved de novo localization scores" and "statistical charts for accurate filtration of de novo data".  I love to hear about improvements in scoring accuracy and FDR, and de novo is where FDR needs the most improvement.  Anybody going out of their way to improve that can send me their software in an easy to install way with a nice free trial and I'm going to do a fair job of checking it out in my free time.

Bonus?  You can directly import MSF reports from Proteome Discoverer 1.3 and 1.4.  I don't know if this will simplify my workflow for de novo studies (link to the video I made for working de novo into PD), but it just might.  I'll be back later with first and final impressions!

MaConDa -- A nice resource for identifying contaminants

This is a cool resource I recently stumbled across!  The MaConDa is a really easy and simple site that has exact mass information on previously identified contaminants in MS/MS runs.  I know most of us have the supplemental information Excel sheet somewhere from that cool paper from a few years ago lying around somewhere, but this is like that with a couple of neat twists.

1) You can filter by instrument type (ion trap, QQQ, or TOF [probably what you'd use for Orbi])
2) You can set a custom PPM error, such as that for your instrument
3) You can filter by contaminant type
4) You can output a list that contains adducts.  Icing on the cake!

Check MaConDa out at this link.  I bet you'll end up using it sometime.  I used it today!

GOrilla -- Gene ontology tool with a great name

Quick note that I found while reading the new MCP on vacation...I know....I have a problem.  In my defense, there are some great papers this month....

Anyway, a new to me tool for gene ontology is GOrilla, which appears to be hosted by the Weizmann institute.  I know there are a lot of GO tools out there, but this one has a couple of nice features beyond its great name.  The first is a customizable p value threshold for your gene enrichment analysis.  The second is the easy control of your output format.  You can simply checkmark the box to output your data in Excel format and/or you can export it directly into the Revigo visualization tool.

You can check out GOrilla here.

Ohaiyou gozaimasu!

Ive got one backpack full of stuff, my IPad, and I'm on a bus to the mountains of northern Japan.  My goals include seeing a wild snow monkey, snowboarding, and making a dent in the global supply of Sapporo (which is crazy cheap here!).  As a consequence, the blog may not see many updates until I return.  It should be an exciting winter, however, as many many good things are happening and I can't wait to share them with y'all!

PRTC. I swear I wrote this entry once before!

Are you running some sort of quality control when you do proteomics?  If your answer is "of course" then I like you.  Heck, I'm a friendly guy.  If your answer was "never, and I hate pugs!" I'd probably still like you, but I might like you better if you are running some sort of QC.

We need to have some sort of metric of how our instruments are running.  I'm often asked what my favorite is.  This question commonly comes after people find out that I don't know what a BSA digest should look like...

The answer, and I swear I wrote all of this a long time ago, is PRTC.

PRTC?  Now, I'll admit, I didn't know about this thing until I joined Thermo.  But I like it so much that I keep aliquots in the ziplock baggy that I keep my toothpaste in for when I travel.

PRTC stands for "Pugs Rock The Cazbar!"

or Peptide retention time calibration....

What it is:  A clean, equimolar, mixture of 15 isotopically labeled peptides for varying hydrophobicity.  Running these can give you a picture of the performance of your LC gradient and your signal intensity over time.  Since they are isotopically labeled you don't have to worry about them being mistaken for something else if you happen to have a small fraction carry over into your next run.

They are also well aliquoted.  So you can use them as spiked in standards at low concentration to normalize label free peptide quan from sample to sample. (I should have some nice data on this in the next couple of weeks.)  And in Pinpoint, you can simply add in QC peptides and it throws them in.

Oh.  And it's cheap.

Downside?  None.  Period.  Exclamation point.

Want to know more?  Check out the product page at Pierce, or this sweet application note written by some of my favorite people!

Comparison of peptide and protein fractionation methods in proteomics

This is a nice analysis that comes from a pretty simple set of experiments that were just done nicely.  The article from Mostovenko et. al., (open access!) compares multiple methods of fractionating both an E.coli digest and a single digest of human plasma.

The methods compared are:  SDS-PAGE vs. SCX vs. IEF.  The output is unique peptides and overlap between methods.  Interestingly, in the bacterial digest, SDS-PAGE and SCX run kind of neck and neck, while IEF lags behind.  However, in the plasma digest, SCX fractionation is the clear winner.

Protein Expression Control Analysis (PECA)

This is a paper for all you bioinformatics people out there.  Partially because you need to have a stronger background in computer stuff that I do to even install and use PECA.

It appears to be a nice tool for the analysis of RNA expression data and it has the capabilities for also inputting quantitative proteomics data from different formats.

PECA differentiates itself from other software by specifically targeting transcripts or proteins that fall within a steep range but limited range of almost logarithmic increase or decrease.

For more information, check out the abstract at JPR (not open access) here.  You can also download the software for compiling and install directly from sourceforge here.

Arginine phosphorylation in bacterial stress response

In press at MCP is a great new paper showing how arginine phosphorylation is used by Bacillus subtilis in the regulation of response to stress.

And it isn't a little involved.  It's a lot involved.  This study shows that it can be linked to heat shock response, response to oxidative stress and in the resistance to drugs.  Pretty impressive findings out of well-characterized model organism.

Beyond the fact that we have yet another PTM to worry about, this paper is valuable for the clear (and pretty simple!) methodology for harvesting, enriching, and analyzing arginine phosphopeptides.

A good read, if only for putting in perspective how much we don't know about the physiology of even the most well-studied organisms!  Definitely check it out while it is still open access.  Direct access to the PDF is here.

How far is the human proteome project at this point?

The human proteome project has been rocking for a while now.  How far has all this work gotten so far.

Well, here is an update (not open access), compliments of JPR and Terry Farrah et. al., and the number is around 62%.

62% what?

Oh, 62% of the coding sequences of DNA that we think code for proteins have strong supporting evidence of their existence in MS/MS spectra. That's pretty cool right  Over half way!

Let's take a moment and think about how great this is, and how far we've come so far!

While doing so, let's forget the fact that one post translational modification can have dramatic ramifications on the function of a protein.  Let's also forget the fact that in 2011, we knew of about 80,000 specific PTMs. Also, let's forget about conformational changes that can have effects every bit as impressive as PTMs.

Please don't get me wrong, I'm not trying to put down the work of the participants of the human proteome project or the good people at ISB who are running the peptide atlas.  I'm simply concerned about our tendency to underestimate the complexity of biological systems.  We did that with the human genome project.  First of all, getting MS/MS spectra for all of the proteins predicted from the HGP data is the tip of the iceberg.  Secondly, let's not declare big ongoing projects completed for a while.  Grant dollars are pretty scarce out there, and we don't need ignorant politicans reading headlines and cutting all the money to our friends because they think the job is done.

Ran into this one thanks to Twitterer @PastelBio