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Estimating relative abundances of proteins from shotgun proteomics data

Sean McIlwain1, Michael Mathews1, Michael S Bereman1, Edwin W Rubel23, Michael J MacCoss1 and William Stafford Noble14*

Author Affiliations

1 Department of Genome Sciences, University of Washington, Seattle, WA, USA

2 Department of Otalaryngology–HNS, Seattle, WA, USA

3 Department of Physiology & Biophysics, University of Washington, Seattle, WA, USA

4 Department of Computer Science and Engineering, Seattle, WA, USA

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BMC Bioinformatics 2012, 13:308  doi:10.1186/1471-2105-13-308

Published: 19 November 2012

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 (SIN), 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 SIN 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 webcite.