More statistical analysis of PSM FDR from the Qu lab!

We have an awful lot of search engines these days and we have almost as many (more?) ways of working out our automatic false discovery rates.  The Qu lab seems to have stepped back and said, let's try to sort it out, meaning, which FDR is more appropriate for large datasets -- and when?

This is a heavy analysis of three different search engines available for running in or through Proteome Discoverer as well as an analysis of what false discovery rate algorithm/method or filter will leave you with the best possible results.  Interestingly, the answers appear to be very analyzer and fragmentation-type dependent.

I'll leave this here for you guys who took more maths!  The answer appears to be...there is no easy answer...these are things we're definitely need to spend more time working on as proteomics moves further and further into the BIG DATA world.

You can find the abstract for this (paywall) paper here.

Histone post-translational modifications in monocyte-derived macrophages

(Picture credit: Laxmi Iyer, original url [unrelated to this article] here.)

It turns out that most of the histone work that has been done out there has been done on mouse cell lines or immortalized human lines. While this is undoubtedly useful information, immortalized cell lines tend to be kind of messed up and we all know about the plus/minuses of studying mice.

The Ciborowski lab has a plan of long term, in-depth studying of histones and their post-translational modifications of normal human macrophages. In this first study (available here, open access) they work on establishing their normal, baseline conditions for resting macrophages. Once they get that, they can go on to further studies.

For this analysis they are primarily using an LTQ-Orbitrap XL with ETD and employing both CID and ETD fractionation. Surprisingly, the majority of the information being obtained for PTM matches is not coming from the ETD. This is likely due to the lower speed/efficiency of the earliest ETD system compared to the ones I normally get to mess around with. It does, however, contribute meaningfully to the study. This is a nice clear study but I mostly highlight it here because I'm very interested in what they are going to do next AND how this data is going to line up with other well established histone PTM datasets we have from other models.  So...this post is kind of to remind myself to check back on these guys later...sorry...

EuPA 2016 --- Turkey?

The theme of the conference is "Challenge Accepted. Standardization and Interpretation of Proteomics." Ummm.....YES!!!!  And the lineup of speakers is already AWESOME!  You can check it out here.

New voice narration Surprise Egg Opening Kinder Surprise İron Man Home M...

New free label free node for Proteome Discoverer 2.0!

2 hours in and complete brain overload here at HUPO!  So much good science out there in the field!

One important side note that I think you guys will like, though. The first free OpenMS nodes for Proteome Discoverer 2.0 are now available for download. The first is LFQ for label free quan!  The second is a workflow in OpenMS I'm unfamiliar with, but will read up on ASAP.

You can download these here.  (Let me know how you like them! I can't wait to give 'em a shot!)

Quantitative thermal proteome profiling!

About a year ago a paper came out that introduced me to the concept of Thermal Proteome Profiling.  While this concept will likely have several different applications, it is definitely really good at figuring out what proteins a drug is interacting!

This month, a brand new paper out of the Savitski lab takes this idea another step further. In this work they use TMT10plex reagents AND thermal proteome profiling to determine what proteins are being directly affected by certain drugs.

Being a Nature Protocols paper, the methodology is set out completely so that any of us can go right out and replicate it. This team also developed software for Python and R that can be used to process the data and they make this all available.

Are you stuck on what that stupid drug's mechanism of action is? You should probably check out this paper!

The long-awaited PD 2.0 (2.0) NIH Training Workshop!

This summer one of my biggest workshops was a PD 2.0 training workshop Alison Wiedergreen set up at the NIH Bethesda campus. It was really well attended and there were tons of great questions.

The crowd requested a follow-up workshop after they got going with the software and we finally made the schedule work!  I'm happy to announce PD 2.0 workshop 2.0!

To register you can follow this link!

In the AM the brilliant and charismatic Dr. Talamantes will be going over the basic functions of PD 2.0, including getting you going if you are new to PD completely or if you have used PD 1.x in the past.

In the afternoon I'll stop watching and we'll go over some more specific workflows. I think we'll look at some real big datasets and how to organize them as well as maybe how to combine quantitative analysis of global proteins with phosphoproteomics or something similar.  If you have a suggestion for what you specifically want to take a look at, shoot me a suggestion and/or a dataset and we'll try to make time for it!  We'll also look at what is coming in Proteome Discoverer 2.1 (which is just a bunch of improvements to the PD 2.0 interface. It works and looks just about the same, I promise!)

Is October 29 too early for halloween costumes?