PepC: proteomics software for identifying differentially expressed proteins based on spectral counting

Abstract

Identifying biologically significant changes in protein abundance between two conditions is a key issue when analyzing proteomic data. One widely used approach centers on spectral counting, a label-free method that sums all the tandem mass spectra for a protein observed in an analysis. To assess the significance of the results, we recently combined the t-test and G-test, with random permutation analysis, and we validated this approach biochemically. To automate the statistical method, we developed PepC, a software program that balances the trade-off between the number of differentially expressed proteins identified and the false discovery rate. This tool can be applied to a wide range of proteomic datasets, making data analysis rapid, reproducible and easily interpretable by proteomics specialists and non-specialists alike.

Availability and implementation: The software is implemented in Java. It has been added to the Trans Proteomic Pipeline project's 'Petunia' web interface, but can also be run as a command line program. The source code is GNU Lesser General Public License and the program is freely available on the web. http://sashimi.svn.sourceforge.net/viewvc/sashimi/trunk/trans_proteomic_pipeline/src/Quantitation/Pepc.

Fig. 1.

(A) Screen shot from PepC illustrating differentially expressed proteins that were isolated from media of macrophages of low density lipoprotein receptor-deficient (Ldlr–/–) mice on a low-fat (chow) or high-fat (Western) diet (Becker et al., 2010). The slider control (right edge of panel) is set to yield a maximum FDR of 10%. The heatmap displays combinations of t-test (y-axis, P-value) and G-test (x-axis, G-statistic) confidence intervals that meet this condition (FDR≤10%). Its color scale (yellow = most, black = least) indicates the number of proteins whose abundances differ significantly between the two groups. When a box is selected (in this case, G-statistic ≥0.75 and P-value ≤0.04), the number of proteins identified and the expected false positives are shown in the dialog directly beneath the heatmap. The individual proteins and their respective statistical parameters are displayed in the window at the bottom of the screen.