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Development and validation of a spectral library searching method for peptide identification from MS/MS
Author(s) -
Lam Henry,
Deutsch Eric W.,
Eddes James S.,
Eng Jimmy K.,
King Nichole,
Stein Stephen E.,
Aebersold Ruedi
Publication year - 2007
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.200600625
Subject(s) - computer science , database search engine , pipeline (software) , search engine , software , sequence database , shotgun proteomics , matching (statistics) , sequence (biology) , data mining , identification (biology) , computational biology , proteomics , information retrieval , chemistry , biology , programming language , mathematics , biochemistry , statistics , botany , gene
A notable inefficiency of shotgun proteomics experiments is the repeated rediscovery of the same identifiable peptides by sequence database searching methods, which often are time‐consuming and error‐prone. A more precise and efficient method, in which previously observed and identified peptide MS/MS spectra are catalogued and condensed into searchable spectral libraries to allow new identifications by spectral matching, is seen as a promising alternative. To that end, an open‐source, functionally complete, high‐throughput and readily extensible MS/MS spectral searching tool, SpectraST, was developed. A high‐quality spectral library was constructed by combining the high‐confidence identifications of millions of spectra taken from various data repositories and searched using four sequence search engines. The resulting library consists of over 30 000 spectra for Saccharomyces cerevisiae . Using this library, SpectraST vastly outperforms the sequence search engine SEQUEST in terms of speed and the ability to discriminate good and bad hits. A unique advantage of SpectraST is its full integration into the popular Trans Proteomic Pipeline suite of software, which facilitates user adoption and provides important functionalities such as peptide and protein probability assignment, quantification, and data visualization. This method of spectral library searching is especially suited for targeted proteomics applications, offering superior performance to traditional sequence searching.