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Estimating relative abundances of proteins from shotgun proteomics data

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 count...

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Autores principales: McIlwain, Sean, Mathews, Michael, Bereman, Michael S, Rubel, Edwin W, MacCoss, Michael J, Noble, William Stafford
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3599300/
https://www.ncbi.nlm.nih.gov/pubmed/23164367
http://dx.doi.org/10.1186/1471-2105-13-308
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author McIlwain, Sean
Mathews, Michael
Bereman, Michael S
Rubel, Edwin W
MacCoss, Michael J
Noble, William Stafford
author_facet McIlwain, Sean
Mathews, Michael
Bereman, Michael S
Rubel, Edwin W
MacCoss, Michael J
Noble, William Stafford
author_sort McIlwain, Sean
collection PubMed
description 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.
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spelling pubmed-35993002013-03-25 Estimating relative abundances of proteins from shotgun proteomics data McIlwain, Sean Mathews, Michael Bereman, Michael S Rubel, Edwin W MacCoss, Michael J Noble, William Stafford BMC Bioinformatics Software 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. BioMed Central 2012-11-19 /pmc/articles/PMC3599300/ /pubmed/23164367 http://dx.doi.org/10.1186/1471-2105-13-308 Text en Copyright ©2012 McIlwain et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software
McIlwain, Sean
Mathews, Michael
Bereman, Michael S
Rubel, Edwin W
MacCoss, Michael J
Noble, William Stafford
Estimating relative abundances of proteins from shotgun proteomics data
title Estimating relative abundances of proteins from shotgun proteomics data
title_full Estimating relative abundances of proteins from shotgun proteomics data
title_fullStr Estimating relative abundances of proteins from shotgun proteomics data
title_full_unstemmed Estimating relative abundances of proteins from shotgun proteomics data
title_short Estimating relative abundances of proteins from shotgun proteomics data
title_sort estimating relative abundances of proteins from shotgun proteomics data
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3599300/
https://www.ncbi.nlm.nih.gov/pubmed/23164367
http://dx.doi.org/10.1186/1471-2105-13-308
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