MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis.

Abstract

MGVB is a collection of tools for proteomics data analysis. It covers data processing from in silico digestion of protein sequences to comprehensive identification of post-translational modifications and solving the protein inference problem. The toolset is developed with efficiency in mind. It enables analysis at a fraction of the resources cost typically required by existing commercial and free tools. MGVB, as it is a native application, is faster than existing proteomics tools such as MaxQuant and, at the same time, finds very similar, in some cases even larger, numbers of peptides at a chosen level of statistical significance. It implements a probabilistic scoring function to match spectra to sequences, a novel combinatorial search strategy for finding post-translational modifications, and a Bayesian approach to locate modification sites. This report describes the algorithms behind the tools, presents benchmarking data sets analysis comparing MGVB performance to MaxQuant/Andromeda, and provides step by step instructions for using it in typical analytical scenarios.