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Quality assessments of peptide–spectrum matches in shotgun proteomics
Author(s) -
Granholm Viktor,
Käll Lukas
Publication year - 2011
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.201000432
Subject(s) - shotgun proteomics , shotgun , database search engine , computer science , identification (biology) , fragmentation (computing) , statistical model , set (abstract data type) , data mining , proteomics , artificial intelligence , search engine , information retrieval , biology , botany , gene , programming language , operating system , biochemistry
The peptide identification process in shotgun proteomics is most frequently solved with search engines. Such search engines assign scores that reflect similarity between the measured fragmentation spectrum and the theoretical spectra of the peptides of a given database. However, the scores from most search engines do not have a direct statistical interpretation. To understand and make use of the significance of peptide identifications, one must thus be familiar with some statistical concepts. Here, we discuss different statistical scores used to show the confidence of an identification and a set of methods to estimate these scores. We also describe the variance of statistical scores and imperfections of scoring functions of peptide–spectrum matches.