PatternLab for proteomics is an integrated computational environment that unifies several previously published modules for analyzing shotgun proteomic data. PatternLab contains modules for formatting sequence databases, performing peptide spectrum matching, statistically filtering and organizing shotgun proteomic data, extracting quantitative information from label-free and chemically labeled data, performing statistics for differential proteomics, displaying results in a variety of graphical formats, performing similarity-driven studies with de novo sequencing data, analyzing time-course experiments, and helping with the understanding of the biological significance of data in the light of the Gene Ontology. Here we describe PatternLab for proteomics 4.0, which closely knits together all of these modules in a self-contained environment, covering the principal aspects of proteomic data analysis as a freely available and easily installable software package. All updates to PatternLab, as well as all new features added to it, have been tested over the years on millions of mass spectra.

The fragmentation of alumina and glass plates due to lateral impact is studied. A few hundred plates have been fragmented at different impact velocities and the produced fragments are analyzed. The method employed in this work allows one to investigate some geometrical properties of the fragments, besides the traditional size distribution usually studied in former experiments. We found that, although both materials exhibit qualitative similar fragment size distribution function, their geometrical properties appear to be quite different. A schematic model for two-dimensional fragmentation is also presented and its predictions are compared to our experimental results. The comparison suggests that the analysis of the fragments' geometrical properties constitutes a more stringent test of the theoretical models' assumptions than the size distribution.

Background: A goal of proteomics is to distinguish between states of a biological system by identifying protein expression differences. Liu et al. demonstrated a method to perform semirelative protein quantitation in shotgun proteomics data by correlating the number of tandem mass spectra obtained for each protein, or "spectral count", with its abundance in a mixture; however, two issues have remained open: how to normalize spectral counting data and how to efficiently pinpoint differences between profiles. Moreover, Chen et al. recently showed how to increase the number of identified proteins in shotgun proteomics by analyzing samples with different MScompatible detergents while performing proteolytic digestion. The latter introduced new challenges as seen from the data analysis perspective, since replicate readings are not acquired.

The Search Engine Processor (SEPro) is a tool for filtering, organizing, sharing, and displaying peptide spectrum matches. It employs a novel three-tier Bayesian approach that uses layers of spectrum, peptide, and protein logic to lead the data to converge to a single list of reliable protein identifications. SEPro is integrated into the PatternLab for proteomics environment, where an arsenal of tools for analyzing shotgun proteomic data is provided. By using the semi-labeled decoy approach for benchmarking, we show that SEPro significantly outperforms a commercially available competitor.

Supplementary data are available at Bioinformatics online.