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An evaluation, comparison, and accurate benchmarking of several publicly available MS/MS search algorithms: Sensitivity and specificity analysis
Author(s) -
Kapp Eugene A.,
Schütz Frédéric,
Connolly Lisa M.,
Chakel John A.,
Meza Jose E.,
Miller Christine A.,
Fenyo David,
Eng Jimmy K.,
Adkins Joshua N.,
Omenn Gilbert S.,
Simpson Richard J.
Publication year - 2005
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.200500126
Subject(s) - mascot , algorithm , computer science , search algorithm , benchmarking , sensitivity (control systems) , probabilistic logic , database search engine , data mining , set (abstract data type) , false discovery rate , machine learning , search engine , artificial intelligence , information retrieval , biology , marketing , electronic engineering , political science , law , business , programming language , biochemistry , gene , engineering
MS/MS and associated database search algorithms are essential proteomic tools for identifying peptides. Due to their widespread use, it is now time to perform a systematic analysis of the various algorithms currently in use. Using blood specimens used in the HUPO Plasma Proteome Project, we have evaluated five search algorithms with respect to their sensitivity and specificity, and have also accurately benchmarked them based on specified false‐positive (FP) rates. Spectrum Mill and SEQUEST performed well in terms of sensitivity, but were inferior to MASCOT, X!Tandem, and Sonar in terms of specificity. Overall, MASCOT, a probabilistic search algorithm, correctly identified most peptides based on a specified FP rate. The rescoring algorithm, PeptideProphet, enhanced the overall performance of the SEQUEST algorithm, as well as provided predictable FP error rates. Ideally, score thresholds should be calculated for each peptide spectrum or minimally, derived from a reversed‐sequence search as demonstrated in this study based on a validated data set. The availability of open‐source search algorithms, such as X!Tandem, makes it feasible to further improve the validation process (manual or automatic) on the basis of “consensus scoring”, i.e. , the use of multiple (at least two) search algorithms to reduce the number of FPs.∁

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