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A high-confidence human plasma proteome reference set with estimated concentrations in PeptideAtlas

Farrah, Terry; Deutsch, Eric W; Omenn, Gilbert S; Campbell, David S; Sun, Zhi; Bletz, Julie A; Mallick, Parag; Katz, Jonathan E; Malmström, Johan LU and Ossola, Reto, et al. (2011) In Molecular & Cellular Proteomics 10(9). p.110-006353
Abstract

Human blood plasma can be obtained relatively noninvasively and contains proteins from most, if not all, tissues of the body. Therefore, an extensive, quantitative catalog of plasma proteins is an important starting point for the discovery of disease biomarkers. In 2005, we showed that different proteomics measurements using different sample preparation and analysis techniques identify significantly different sets of proteins, and that a comprehensive plasma proteome can be compiled only by combining data from many different experiments. Applying advanced computational methods developed for the analysis and integration of very large and diverse data sets generated by tandem MS measurements of tryptic peptides, we have now compiled a... (More)

Human blood plasma can be obtained relatively noninvasively and contains proteins from most, if not all, tissues of the body. Therefore, an extensive, quantitative catalog of plasma proteins is an important starting point for the discovery of disease biomarkers. In 2005, we showed that different proteomics measurements using different sample preparation and analysis techniques identify significantly different sets of proteins, and that a comprehensive plasma proteome can be compiled only by combining data from many different experiments. Applying advanced computational methods developed for the analysis and integration of very large and diverse data sets generated by tandem MS measurements of tryptic peptides, we have now compiled a high-confidence human plasma proteome reference set with well over twice the identified proteins of previous high-confidence sets. It includes a hierarchy of protein identifications at different levels of redundancy following a clearly defined scheme, which we propose as a standard that can be applied to any proteomics data set to facilitate cross-proteome analyses. Further, to aid in development of blood-based diagnostics using techniques such as selected reaction monitoring, we provide a rough estimate of protein concentrations using spectral counting. We identified 20,433 distinct peptides, from which we inferred a highly nonredundant set of 1929 protein sequences at a false discovery rate of 1%. We have made this resource available via PeptideAtlas, a large, multiorganism, publicly accessible compendium of peptides identified in tandem MS experiments conducted by laboratories around the world.

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publication status
published
keywords
Algorithms, Biomarkers, Blood Proteins, Chromatography, Liquid, Databases, Protein, Humans, Mass Spectrometry, Peptides, Plasma, Proteome, Proteomics, Reference Standards, Software, Trypsin, Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't
in
Molecular & Cellular Proteomics
volume
10
issue
9
pages
110 - 006353
publisher
American Society for Biochemistry and Molecular Biology
external identifiers
  • scopus:79959499115
ISSN
1535-9484
DOI
10.1074/mcp.M110.006353
language
English
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no
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c20d9ee9-0ccd-48ce-9e7c-d095ad40f06b
date added to LUP
2016-11-16 20:31:27
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2017-10-08 04:54:43
@article{c20d9ee9-0ccd-48ce-9e7c-d095ad40f06b,
  abstract     = {<p>Human blood plasma can be obtained relatively noninvasively and contains proteins from most, if not all, tissues of the body. Therefore, an extensive, quantitative catalog of plasma proteins is an important starting point for the discovery of disease biomarkers. In 2005, we showed that different proteomics measurements using different sample preparation and analysis techniques identify significantly different sets of proteins, and that a comprehensive plasma proteome can be compiled only by combining data from many different experiments. Applying advanced computational methods developed for the analysis and integration of very large and diverse data sets generated by tandem MS measurements of tryptic peptides, we have now compiled a high-confidence human plasma proteome reference set with well over twice the identified proteins of previous high-confidence sets. It includes a hierarchy of protein identifications at different levels of redundancy following a clearly defined scheme, which we propose as a standard that can be applied to any proteomics data set to facilitate cross-proteome analyses. Further, to aid in development of blood-based diagnostics using techniques such as selected reaction monitoring, we provide a rough estimate of protein concentrations using spectral counting. We identified 20,433 distinct peptides, from which we inferred a highly nonredundant set of 1929 protein sequences at a false discovery rate of 1%. We have made this resource available via PeptideAtlas, a large, multiorganism, publicly accessible compendium of peptides identified in tandem MS experiments conducted by laboratories around the world.</p>},
  author       = {Farrah, Terry and Deutsch, Eric W and Omenn, Gilbert S and Campbell, David S and Sun, Zhi and Bletz, Julie A and Mallick, Parag and Katz, Jonathan E and Malmström, Johan and Ossola, Reto and Watts, Julian D and Lin, Biaoyang and Zhang, Hui and Moritz, Robert L and Aebersold, Ruedi},
  issn         = {1535-9484},
  keyword      = {Algorithms,Biomarkers,Blood Proteins,Chromatography, Liquid,Databases, Protein,Humans,Mass Spectrometry,Peptides,Plasma,Proteome,Proteomics,Reference Standards,Software,Trypsin,Journal Article,Research Support, N.I.H., Extramural,Research Support, Non-U.S. Gov't},
  language     = {eng},
  number       = {9},
  pages        = {110--006353},
  publisher    = {American Society for Biochemistry and Molecular Biology},
  series       = {Molecular & Cellular Proteomics},
  title        = {A high-confidence human plasma proteome reference set with estimated concentrations in PeptideAtlas},
  url          = {http://dx.doi.org/10.1074/mcp.M110.006353},
  volume       = {10},
  year         = {2011},
}