Estimating relative abundances of proteins from shotgun proteomics data

Sean Mcilwain,Michael Mathews,William Stafford Noble,Edwin W Rubel,Michael J Maccoss,Michael S Bereman

doi:10.1186/1471-2105-13-308

Sean Mcilwain, Michael Mathews + Show 4 more

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BackgroundSpectral 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).ResultsWe 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.ConclusionsWith 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.

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Spectral counting methods provide an easy means of identifying proteins with differing abundances between complex mixtures using shotgun proteomics data
Existing methods for differential proteomics fall into two categories: spectral counting methods that rely on counting the number of spectra that map to a given protein across multiple experiments, and peptide chromatographic peak intensity methods that use the area under the peptide precursor ion peak as a measure of peptide abundance
The contributions of this paper are two-fold: we describe a performance comparison of the reproducibility and linearity of the spectral index (SIN), exponentially modified protein abundance index (emPAI), normalized spectral abundance factor (NSAF), and distributed normalized spectral abundance factor (dNSAF) protein quantification methods, and we provide to the proteomics community a flexible, open source spectral counting software tool

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Spectral counting methods provide an easy means of identifying proteins with differing abundances between complex mixtures using shotgun proteomics data. Existing methods for differential proteomics (reviewed by [1]) fall into two categories: spectral counting methods that rely on counting the number of spectra that map to a given protein across multiple experiments, and peptide chromatographic peak intensity methods that use the area under the peptide precursor ion peak as a measure of peptide abundance. ProteoIQ (http://www.bioinquire.com) and Scaffold [9] are commercial software products that post-process results from a variety of database search programs. Available tools such as APEX [10], emPAI calc [11], and PepC [12] each offer a single spectral counting method.

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