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Including shared peptides for estimating protein abundances: A significant improvement for quantitative proteomics
Authors:Mélisande Blein-Nicolas  Hao Xu  Dominique de Vienne  Christophe Giraud  Sylvie Huet  Michel Zivy
Institution:INRA, UMR 0320/UMR 8120 Génétique Végétale, Gif-sur-Yvette, France.
Abstract: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/.
Keywords:Mass spectrometry  Protein quantification  Proteome  Shared peptides  Statistical modeling
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