title: Including shared peptides for estimating protein abundances: a significant improvement for quantitative proteomics.
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/.
authors: INRA, UMR 0320/UMR 8120 Génétique Végétale, Gif-sur-Yvette, France.
authors: Mélisande
authors: M
authors: Blein-Nicolas
authors: Hao
authors: H
authors: Xu
authors: Dominique
authors: D
authors: de Vienne
authors: Christophe
authors: C
authors: Giraud
authors: Sylvie
authors: S
authors: Huet
authors: Michel
authors: M
authors: Zivy
mesh_terms: D013058:Mass Spectrometry / Q000379:methods; D015233:Models, Statistical; D010455:Peptides / Q000032:analysis*; D011506:Proteins / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*
chemical_list: D010455:Peptides; D011506:Proteins