This is a very heavy and extremely interesting paper from a search algorithm optimization perspective. Oh, the paper in question is from Sonja Holl et al., and is available here.
You should probably read it yourself (its open access!) but I'm going to stumble through my layman-level interpretation of what I just read over Holiday Inn coffee that I think was some sort of homeopathy caffeine experiment...
The paper is essentially a meta-analysis of 6 data sets from three different types of instruments. Some come from ion traps, some come from Orbitraps, and some come from something called a Q-TOOF ;)
The goal of the study was to see how much changing the search parameters in a guided way would improve or hurt the results. And its kind of drastic. What they came up with is something that is a new optimization platform for a big and super interesting project called Taverna (will investigate!). The optimization plaform in Taverna looks at your data and determines what search parameters that you should be using for ideal levels of high quality peptide spectral matches (PSMs).
The taverna optimization platform looks at a number of variables including mass accuracy, isotopic distributions and more peptide-centric parameters like missed cleavages and enzyme fidelity. Up to this point, I was wondering why someone would re-write Preview....but then they make a sharp right turn and incorporate retention time prediction into the algorithm! Interesting, right?!?
Another interesting plus? It appears to be a designed for server level applications! A nice read even if your neurons aren't firing all the way! Now its time to figure out what this Taverna thing is all about!
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