PatternLab for proteomics: a tool for differential shotgun proteomics

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 Chen et 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.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 at http://pcarvalho.com/patternlab webcite.A goal of proteomics is to distinguish between states of a biological system by identifying protein expression differences [1]. Shotgun proteomics is a large-scale strategy for protein identification in complex mixtures that involves pre-digestion of intact proteins followed by peptide separation, fragmentation in a mass spectrometer, and database search. Its name is derived from DNA shotgun sequencing, which in turn follows the analogy of a shotgun's quasi-random firing pattern and dispersion to ensure the target is hit.Multi-dimensional Protein Identification Technology (Mud