The Null-Test for peptide identification algorithm in Shotgun proteomics

Journal of Proteomics
Shu-Rong ZhangYu-Kui Zhang

Abstract

The present research proposed general evaluation strategy named Null-Test for peptide identification algorithm in Shotgun proteomics. The Null-Test method based on random matching can be utilized to check whether the algorithm has a tendency to make a mistake or has potential bugs, faultiness, errors etc., and to validate the reliability of the identification algorithm. Unfortunately, none of the five famous identification software could pass the most stringent Null-Test. PatternLab had good performance in both Null-Test and routine search by making a good control on the overfitting with sound design. The fuzzy logics based method presented as another candidate strategy could pass the Null-Test and has competitive efficiency in peptide identification. Filtering the results by appropriate FDR would increase the number of discoveries in an experiment, at the cost of losing control of Type I errors. Thus, it is necessary to utilize some more stringent criteria when someone wants to design or analyze an algorithm/software. The more stringent criteria will facilitate the discovery of latent bugs, faultiness, errors etc. in the algorithm/software. It would be recommended to utilize independent search combining random database with st...Continue Reading

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Citations

Apr 16, 2019·Journal of Proteome Research·Yulia DanilovaAttila Kertész-Farkas

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