Optimization of filtering criterion for SEQUEST database searching to improve proteome coverage in shotgun proteomics

Background: In proteomic analysis, MS/MS spectra acquired by mass spectrometer are assigned to peptides by database searching algorithms such as SEQUEST. The assignations of peptides to MS/ MS spectra by SEQUEST searching algorithm are defined by several scores including Xcorr, ΔCn, Sp, Rsp, matched ion count and so on. Filtering criterion using several above scores is used to isolate correct identifications from random assignments. However, the filtering criterion was not favorably optimized up to now. Results: In this study, we implemented a machine learning approach known as predictive genetic algorithm (GA) for the optimization of filtering criteria to maximize the number of identified peptides at fixed false-discovery rate (FDR) for SEQUEST database searching. As the FDR was directly determined by decoy database search scheme, the GA based optimization approach did not require any pre-knowledge on the characteristics of the data set, which represented significant advantages over...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Xinning Jiang, Xiaogang Jiang, Guanghui Han, Mingliang Ye, Hanfa Zou
• Keywords: Algorithm, BMCBI 2007, GA Based Approach, SEQUEST