Estimating relative abundances of proteins from shotgun proteomics data

Sean McIlwain, Michael Mathews, Michael S Bereman, Edwin W Rubel, Michael J MacCoss, William Stafford Noble, Sean McIlwain, Michael Mathews, Michael S Bereman, Edwin W Rubel, Michael J MacCoss, William Stafford Noble

Abstract

Background: Spectral counting methods provide an easy means of identifying proteins with differing abundances between complex mixtures using shotgun proteomics data. The crux spectral-counts command, implemented as part of the Crux software toolkit, implements four previously reported spectral counting methods, the spectral index (SI(N)), the exponentially modified protein abundance index (emPAI), the normalized spectral abundance factor (NSAF), and the distributed normalized spectral abundance factor (dNSAF).

Results: We compared the reproducibility and the linearity relative to each protein's abundance of the four spectral counting metrics. Our analysis suggests that NSAF yields the most reproducible counts across technical and biological replicates, and both SI(N) and NSAF achieve the best linearity.

Conclusions: With the crux spectral-counts command, Crux provides open-source modular methods to analyze mass spectrometry data for identifying and now quantifying peptides and proteins. The C++ source code, compiled binaries, spectra and sequence databases are available at http://noble.gs.washington.edu/proj/crux-spectral-counts.

Figures

Figure 1
Figure 1
Reproducibility of spectral counts across biological and technical replicate experiments. Each plot compares either the SIN, emPAI, NSAF or dNSAF measure for proteins that were reproducibly identified across two replicate experiments. For visualization purposes, the counts are plotted on a logarithmic scale. The number in the lower right corner of each panel is the corresponding Spearman correlation and the number in the upper left is the number of datapoints compared.
Figure 2
Figure 2
Comparison of spectral counts across replicates. This graph summarizes the statistical analysis of the reproducibility measurements. An edge leading out from node A to node B indicates a statistically significant improvement in reproducibility for method A relative to method B.
Figure 3
Figure 3
Comparison of spectral counts across UPS1 dilution curve. This graph summarizes the statistical analysis of the linearity measurements. Two types of analysis were performed, using the linear regression correlation, R2 and mean percent error (MPE) for the C. elegans + UPS1 dilution curve dataset. An edge leading out from node A to node B indicates a statistically significant improvement in linearity for method A relative to method B.

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