Estimating relative abundances of proteins from shotgun proteomics data

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 SINand 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 athttp://noble.gs.washington.edu/proj/crux-spectral-counts.

[1]  William Stafford Noble,et al.  Rapid and accurate peptide identification from tandem mass spectra. , 2008, Journal of proteome research.

[2]  William Stafford Noble,et al.  Direct Maximization of Protein Identifications from Tandem Mass Spectra* , 2011, Molecular & Cellular Proteomics.

[3]  William Stafford Noble,et al.  Detecting cross-linked peptides by searching against a database of cross-linked peptide pairs. , 2010, Journal of proteome research.

[4]  J. Koziol,et al.  Label-free, normalized quantification of complex mass spectrometry data for proteomics analysis , 2009, Nature Biotechnology.

[5]  M. Mann,et al.  Exponentially Modified Protein Abundance Index (emPAI) for Estimation of Absolute Protein Amount in Proteomics by the Number of Sequenced Peptides per Protein*S , 2005, Molecular & Cellular Proteomics.

[6]  Kerry G Bemis,et al.  Label-free mass spectrometry-based protein quantification technologies in proteomic analysis. , 2008, Briefings in functional genomics & proteomics.

[7]  William Stafford Noble,et al.  Assigning significance to peptides identified by tandem mass spectrometry using decoy databases. , 2008, Journal of proteome research.

[8]  William Stafford Noble,et al.  Statistical calibration of the SEQUEST XCorr function. , 2009, Journal of proteome research.

[9]  M. Washburn,et al.  Quantitative proteomic analysis of distinct mammalian Mediator complexes using normalized spectral abundance factors , 2006, Proceedings of the National Academy of Sciences.

[10]  Masaru Tomita,et al.  emPAI Calc - for the estimation of protein abundance from large-scale identification data by liquid chromatography-tandem mass spectrometry , 2010, Bioinform..

[11]  Michael J MacCoss,et al.  Comparison of database search strategies for high precursor mass accuracy MS/MS data. , 2010, Journal of proteome research.

[12]  B. Searle Scaffold: A bioinformatic tool for validating MS/MS‐based proteomic studies , 2010, Proteomics.

[13]  Martin Eisenacher,et al.  The mzIdentML Data Standard for Mass Spectrometry-Based Proteomics Results , 2012, Molecular & Cellular Proteomics.

[14]  M. Washburn,et al.  Refinements to label free proteome quantitation: how to deal with peptides shared by multiple proteins. , 2010, Analytical chemistry.

[15]  Lennart Martens,et al.  A comparison of MS2‐based label‐free quantitative proteomic techniques with regards to accuracy and precision , 2011, Proteomics.

[16]  R. Aebersold,et al.  A uniform proteomics MS/MS analysis platform utilizing open XML file formats , 2005, Molecular systems biology.

[17]  D. Tabb,et al.  Proteomic parsimony through bipartite graph analysis improves accuracy and transparency. , 2007, Journal of proteome research.

[18]  Rong Wang,et al.  The APEX Quantitative Proteomics Tool: Generating protein quantitation estimates from LC-MS/MS proteomics results , 2008, BMC Bioinformatics.

[19]  N. L. Heinecke,et al.  PepC: proteomics software for identifying differentially expressed proteins based on spectral counting , 2010, Bioinform..

[20]  Mehdi Mirzaei,et al.  Less label, more free: Approaches in label‐free quantitative mass spectrometry , 2011, Proteomics.