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Development and Validation of a Spectral Library Searching Method for Peptide Identification from Tandem Mass Spectrometry



Henry Lam, Eric Deutsch, James E. Eddes, Jimmy K. Eng, Nichole King, Stephen Stein, H. Ueda


A notable inefficiency of shotgun proteomics experiments lie in 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, especially for targeted proteomics applications. To that end, an open-source, functionally complete, high-throughput and readily extensible search tool, SpectraST, is developed. A high-quality spectral library is constructed by combining the high-confidence identifications (by 4 sequence search engines) of millions of spectra taken from various data repositories, and carefully filtered to remove incorrectly identified and low-quality spectra. Using this library, SpectraST is shown to vastly outperform 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 adaptation and provides important functionalities such as raw data import, peptide and protein probability assignment, quantification, and data visualization.
Journal of Proteome Research


bioinformatics, mass spectra, peptides, proteomics


Lam, H. , Deutsch, E. , Eddes, J. , Eng, J. , King, N. , Stein, S. and Ueda, H. (2021), Development and Validation of a Spectral Library Searching Method for Peptide Identification from Tandem Mass Spectrometry, Journal of Proteome Research (Accessed April 19, 2024)
Created October 12, 2021