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

ABSTRACT:

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, Delta Cn, 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

SUBMITTER: Jiang X 

PROVIDER: S-EPMC2040164 | biostudies-literature | 2007 Aug

REPOSITORIES: biostudies-literature