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Including shared peptides for estimating protein abundances: A significant improvement for quantitative proteomics
Author(s) -
BleinNicolas Mélisande,
Xu Hao,
de Vienne Dominique,
Giraud Christophe,
Huet Sylvie,
Zivy Michel
Publication year - 2012
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201100660
Subject(s) - proteomics , computer science , inference , quantitative proteomics , computational biology , key (lock) , peptide , data mining , biology , artificial intelligence , biochemistry , computer security , gene
Inferring protein abundances from peptide intensities is the key step in quantitative proteomics. The inference is necessarily more accurate when many peptides are taken into account for a given protein. Yet, the information brought by the peptides shared by different proteins is commonly discarded. We propose a statistical framework based on a hierarchical modeling to include that information. Our methodology, based on a simultaneous analysis of all the quantified peptides, handles the biological and technical errors as well as the peptide effect. In addition, we propose a practical implementation suitable for analyzing large data sets. Compared to a method based on the analysis of one protein at a time (that does not include shared peptides), our methodology proved to be far more reliable for estimating protein abundances and testing abundance changes. The source codes are available at http://pappso.inra.fr/bioinfo/all_p/.