Sifting through the tandem MS trash for proteins?

Review: Dynamic Spectrum Quality Assessment and Iterative Computational Analysis of Shotgun Proteomic Data: Toward More Efficient Identification of Post-translational Modifications, Sequence Polymorphisms, and Novel Peptides. By Alexey I. Nesvizhskii et al.
Molecular & Cellular Proteomics 5:652-670, 2006

The Aebersold group continues to produce great new tools for proteomic data analysis. Presented is a method to comb MS/MS data for good spectra which were not matched with a peptide on your first search. Unique to this technique is the notion that what constitutes a high quality spectrum can be learned from the analyzed data itself, i.e., without relying on a training data. The database search assignments that are made in the first pass at data analysis are generally based on high quality MS/MS spectra, these are used to identify high quality spectra that were not matched. The method is based on a Linear Discriminant Analysis of a set of spectra quality measurements.

A nice aspect of this paper is that the authors did a good job of illustrating how their nifty new data analysis tool could result in biological insight. One drawback is that a lot of computation is required to squeeze a little more information out of your data.

This software is named QualScore and can be found at: http://cvs.sourceforge.net/viewcvs.py/sashimi/

Review of Review: Guidelines for the next 10 years of Proteomics

Guidelines for the next 10 years of Proteomics
M. R. Wilkins et al.
Proteomics Vol. 6 Issue 1
http://www3.interscience.wiley.com/cgi-bin/jissue/112225239

The first couple of pages of this article left me thinking, tell me something that I don’t know. The first pages are filled with profound ideas like “The rapid expansion of proteomics, whilst exciting, has brought with it many technical issues.” But at the same time the review is short and these type of ideas could be useful to those from other disciplines that are dabbling in proteomics.

One idea that that I found very interesting was the term Analytical Incompleteness. “Analytical incompleteness refers to a phenomenon where a technique used for the analysis of complex mixtures of peptides may only yield information for a fraction of relevant peptides in any single analytical run." This term apperar common on google, but it is new to me and is a good description of the current state of Proteomics.

The real value of this paper is the Amendum: Editorial and AuthorGuidelines for Publication in PROTEOMICS. Meetings in July and August, which were initiated by the journal of Molecular and Cellular Proteomics (MCP) resulted in standards for preparing, reviewing and publishing of data from MS/MS experiments. Guidelines like these are vital for Proteomics to mature and solve medical problems.