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In vitro and in silico processes to identify differentially expressed proteins

2004; Wiley; Volume: 4; Issue: 8 Linguagem: Inglês

10.1002/pmic.200300840

1615-9861

Nadia Allet, Nicolas Barrillat, Thierry Baussant, Celia Boiteau, Paolo Botti, Lydie Bougueleret, Nicolas Budin, Denis Canet, Stéphanie Carraud, Diego Chiappe, Nicolas Christmann, Jacques Colinge, Isabelle Cusin, Nicolas Dafflon, Benoît Depresle, Irène Fasso, Pascal Frauchiger, Hubert Gaertner, Anne Gleizes, E. Gonzalez‐Couto, Catherine Jeandenans, Abderrahim Karmime, Thomas Kowall, Sophie Braga Lagache, Eve Mahé, Alexandre Masselot, Hassan Mattou, Marc Moniatte, Anne Niknejad, Moreno Paolini, Florent Perret, Nicolas Pinaud, Frédéric Ranno, Sylvain Raimondi, Samia Reffas, Pierre‐Olivier Regamey, Pierre‐Antoine Rey, Patricia Rodriguez‐Tomé, Keith Rose, Gérald Rossellat, Cédric Saudrais, Camille Schmidt, Matteo Villain, Catherine Zwahlen,

Identification and Quantification in Food

We present an integrated proteomics platform designed for performing differential analyses. Since reproducible results are essential for comparative studies, we explain how we improved reproducibility at every step of our laboratory processes, e.g. by taking advantage of the powerful laboratory information management system we developed. The differential capacity of our platform is validated by detecting known markers in a real sample and by a spiking experiment. We introduce an innovative two-dimensional (2-D) plot for displaying identification results combined with chromatographic data. This 2-D plot is very convenient for detecting differential proteins. We also adapt standard multivariate statistical techniques to show that peptide identification scores can be used for reliable and sensitive differential studies. The interest of the protein separation approach we generally apply is justified by numerous statistics, complemented by a comparison with a simple shotgun analysis performed on a small volume sample. By introducing an automatic integration step after mass spectrometry data identification, we are able to search numerous databases systematically, including the human genome and expressed sequence tags. Finally, we explain how rigorous data processing can be combined with the work of human experts to set high quality standards, and hence obtain reliable (false positive < 0.35%) and nonredundant protein identifications.