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In vitro and in silico processes to identify differentially expressed proteins
Author(s) -
Allet Nadia,
Barrillat Nicolas,
Baussant Thierry,
Boiteau Celia,
Botti Paolo,
Bougueleret Lydie,
Budin Nicolas,
Canet Denis,
Carraud Stéphanie,
Chiappe Diego,
Christmann Nicolas,
Colinge Jacques,
Cusin Isabelle,
Dafflon Nicolas,
Depresle Benoît,
Fasso Irène,
Frauchiger Pascal,
Gaertner Hubert,
Gleizes Anne,
GonzalezCouto Eduardo,
Jeandenans Catherine,
Karmime Abderrahim,
Kowall Thomas,
Lagache Sophie,
Mahé Eve,
Masselot Alexandre,
Mattou Hassan,
Moniatte Marc,
Niknejad Anne,
Paolini Marianne,
Perret Frédéric,
Pinaud Nicolas,
Ranno Frédéric,
Raimondi Sylvain,
Reffas Samia,
Regamey PierreOlivier,
Rey PierreAntoine,
RodriguezTomé Patricia,
Rose Keith,
Rossellat Gérald,
Saudrais Cédric,
Schmidt Camille,
Villain Matteo,
Zwahlen Catherine
Publication year - 2004
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.200300840
Subject(s) - in silico , identification (biology) , shotgun proteomics , computational biology , computer science , proteomics , data mining , plot (graphics) , sample (material) , multivariate statistics , shotgun , differential (mechanical device) , biology , chromatography , mathematics , chemistry , statistics , genetics , machine learning , gene , botany , engineering , aerospace engineering
Abstract 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.