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dc.contributor.authorDicker, Lee
dc.contributor.authorLin, Xihong
dc.contributor.authorIvanov, Alexander R.
dc.date.accessioned2019-09-30T11:55:45Z
dc.date.issued2010
dc.identifier.citationDicker, Lee, Xihong Lin, and Alexander R. Ivanov. 2010. “Increased Power for the Analysis of Label-Free LC-MS/MS Proteomics Data by Combining Spectral Counts and Peptide Peak Attributes.” Molecular & Cellular Proteomics 9 (12): 2704–18. https://doi.org/10.1074/mcp.m110.002774.
dc.identifier.issn1535-9476
dc.identifier.issn1535-9484
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:41426758*
dc.description.abstractLiquid chromatography-tandem mass spectrometry (LC-MS/MS)-based proteomics provides a wealth of information about proteins present in biological samples. In bottom-up LC-MS/MS-based proteomics, proteins are enzymatically digested into peptides prior to query by LC-MS/MS. Thus, the information directly available from the LC-MS/MS data is at the peptide level. If a protein-level analysis is desired, the peptide-level information must be rolled up into protein-level information. We propose a principal component analysis-based statistical method, ProPCA, for efficiently estimating relative protein abundance from bottom-up label-free LC-MS/MS data that incorporates both spectral count information and LC-MS peptide ion peak attributes, such as peak area, volume, or height. ProPCA may be used effectively with a variety of quantification platforms and is easily implemented. We show that ProPCA outperformed existing quantitative methods for peptide-protein roll-up, including spectral counting methods and other methods for combining LC-MS peptide peak attributes. The performance of ProPCA was validated using a data set derived from the LC-MS/MS analysis of a mixture of protein standards (the UPS2 proteomic dynamic range standard introduced by The Association of Biomolecular Resource Facilities Proteomics Standards Research Group in 2006). Finally, we applied ProPCA to a comparative LC-MS/MS analysis of digested total cell lysates prepared for LC-MS/MS analysis by alternative lysis methods and show that ProPCA identified more differentially abundant proteins than competing methods. Molecular & Cellular Proteomics 9:2704-2718, 2010.
dc.language.isoen_US
dc.publisherAmerican Society for Biochemistry and Molecular Biology
dash.licenseLAA
dc.titleIncreased Power for the Analysis of Label-free LC-MS/MS Proteomics Data by Combining Spectral Counts and Peptide Peak Attributes
dc.typeJournal Article
dc.description.versionVersion of Record
dc.relation.journalMolecular & Cellular Proteomics - MCP
dash.depositing.authorLin, Xihong::d37120f433d98141714935d45b7669e9::600
dc.date.available2019-09-30T11:55:45Z
dash.workflow.comments1Science Serial ID 63544
dc.identifier.doi10.1074/mcp.M110.002774
dash.source.volume9;12
dash.source.page2704


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