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

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.