Proteomic Parsimony through Bipartite Graph Analysis Improves Accuracy and Transparency

Assembling peptides identified from LC–MS/MS spectra into a list of proteins is a critical step in analyzing shotgun proteomics data. As one peptide sequence can be mapped to multiple proteins in a database, naïve protein assembly can substantially overstate the number of proteins found in samples....

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Main Authors: Zhang, Bing, Chambers, Matthew C., Tabb, David L.
Formato: Artigo
Idioma:Inglês
Publicado em: 2007
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Article
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC2810678/
https://ncbi.nlm.nih.gov/pubmed/17676885
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1021/pr070230d
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spelling pubmed-28106782010-01-25 Proteomic Parsimony through Bipartite Graph Analysis Improves Accuracy and Transparency Zhang, Bing Chambers, Matthew C. Tabb, David L. J Proteome Res Article Assembling peptides identified from LC–MS/MS spectra into a list of proteins is a critical step in analyzing shotgun proteomics data. As one peptide sequence can be mapped to multiple proteins in a database, naïve protein assembly can substantially overstate the number of proteins found in samples. We model the peptide–protein relationships in a bipartite graph and use efficient graph algorithms to identify protein clusters with shared peptides and to derive the minimal list of proteins. We test the effects of this parsimony analysis approach using MS/MS data sets generated from a defined human protein mixture, a yeast whole cell extract, and a human serum proteome after MARS column depletion. The results demonstrate that the bipartite parsimony technique not only simplifies protein lists but also improves the accuracy of protein identification. We use bipartite graphs for the visualization of the protein assembly results to render the parsimony analysis process transparent to users. Our approach also groups functionally related proteins together and improves the comprehensibility of the results. We have implemented the tool in the IDPicker package. The source code and binaries for this protein assembly pipeline are available under Mozilla Public License at the following URL: http://www.mc.vanderbilt.edu/msrc/bioinformatics/. 2007-08-04 2007-09 /pmc/articles/PMC2810678/ /pubmed/17676885 http://dx.doi.org/10.1021/pr070230d Text en
institution US NLM
collection PubMed Central
language Inglês
format Artigo
topic Article
spellingShingle Article
Zhang, Bing
Chambers, Matthew C.
Tabb, David L.
Proteomic Parsimony through Bipartite Graph Analysis Improves Accuracy and Transparency
description Assembling peptides identified from LC–MS/MS spectra into a list of proteins is a critical step in analyzing shotgun proteomics data. As one peptide sequence can be mapped to multiple proteins in a database, naïve protein assembly can substantially overstate the number of proteins found in samples. We model the peptide–protein relationships in a bipartite graph and use efficient graph algorithms to identify protein clusters with shared peptides and to derive the minimal list of proteins. We test the effects of this parsimony analysis approach using MS/MS data sets generated from a defined human protein mixture, a yeast whole cell extract, and a human serum proteome after MARS column depletion. The results demonstrate that the bipartite parsimony technique not only simplifies protein lists but also improves the accuracy of protein identification. We use bipartite graphs for the visualization of the protein assembly results to render the parsimony analysis process transparent to users. Our approach also groups functionally related proteins together and improves the comprehensibility of the results. We have implemented the tool in the IDPicker package. The source code and binaries for this protein assembly pipeline are available under Mozilla Public License at the following URL: http://www.mc.vanderbilt.edu/msrc/bioinformatics/.
author Zhang, Bing
Chambers, Matthew C.
Tabb, David L.
author_facet Zhang, Bing
Chambers, Matthew C.
Tabb, David L.
author_sort Zhang, Bing
title Proteomic Parsimony through Bipartite Graph Analysis Improves Accuracy and Transparency
title_short Proteomic Parsimony through Bipartite Graph Analysis Improves Accuracy and Transparency
title_full Proteomic Parsimony through Bipartite Graph Analysis Improves Accuracy and Transparency
title_fullStr Proteomic Parsimony through Bipartite Graph Analysis Improves Accuracy and Transparency
title_full_unstemmed Proteomic Parsimony through Bipartite Graph Analysis Improves Accuracy and Transparency
title_sort proteomic parsimony through bipartite graph analysis improves accuracy and transparency
publishDate 2007
url https://ncbi.nlm.nih.gov/pmc/articles/PMC2810678/
https://ncbi.nlm.nih.gov/pubmed/17676885
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1021/pr070230d
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