Background: A goal of proteomics is to distinguish between states of a biological system by identifying protein expression differences. Liuet al. demonstrated a method to perform semi-relative 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, Chenet al. recently showed how to increase the number of identified proteins in shotgun proteomics by analyzing samples with different MS-compatible detergents while performing proteolytic digestion. The latter introduced new challenges as seen from the data analysis perspective, since replicate readings are not acquired.Results: To address the open issues above, we present a program termed PatternLab for proteomics. This program implements existing strategies and adds two new methods to pinpoint differences in protein profiles. The first method, ACFold, addresses experiments with less than three replicates from each state or having assays acquired by different protocols as described by Chenet al. ACFold uses a combined criterion based on expression fold changes, the AC test, and the false-discovery rate, and can supply a "bird's-eye view" of differentially expressed proteins. The other method addresses experimental designs having multiple readings from each state and is referred to as nSVM (natural support vector machine) because of its roots in evolutionary computing and in statistical learning theory. Our observations suggest that nSVM's niche comprises projects that select a minimum set of proteins for classification purposes; for example, the development of an early detection kit for a given pathology. We demonstrate the effectiveness of each method on experimental data and confront them with existing strategies.Conclusion: PatternLab offers an easy and unified access to a variety of feature selection and normalization strategies, each having its own niche. Additionally, graphing tools are available to aid in the analysis of high throughput experimental data. PatternLab is available athttp://pcarvalho.com/patternlab.

Summary: We present an updated version of the TFold software for pinpointing differentially expressed proteins in shotgun proteomics experiments. Given an FDR bound, the updated approach uses a theoretical FDR estimator to maximize the number of identifications that satisfy both a fold-change cutoff that varies with the t-test P-value as a power law and a stringency criterion that aims to detect lowly abundant proteins. The new version has yielded significant improvements in sensitivity over the previous one. Availability: Freely available for academic use at http://pcarvalho.com/patternlab. Contact:paulo@pcarvalho.com

Summary: XDIA is a computational strategy for analyzing multiplexed spectra acquired using electron transfer dissociation and collision-activated dissociation; it significantly increases identified spectra (∼250%) and unique peptides (∼30%) when compared with the data-dependent ETCaD analysis on middle-down, single-phase shotgun proteomic analysis. Increasing identified spectra and peptides improves quantitation statistics confidence and protein coverage, respectively. Availability: The software and data produced in this work are freely available for academic use at http://fields.scripps.edu/XDIA Contact: paulo@pcarvalho.com Supplementary information: Supplementary data are available at Bioinformatics online.