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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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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. |
format | Online Article Text |
id | pubmed-3599300 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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