A robust linear regression based algorithm for automated evaluation of peptide identifications from shotgun proteomics by use of reversed-phase liquid chromatography retention time.
BACKGROUND
Rejection of false positive peptide matches in database searches of shotgun proteomic experimental data is highly desirable. Several methods have been developed to use the peptide retention time as to refine and improve peptide identifications from database search algorithms. This report describes the implementation of an automated approach to reduce false positives and validate peptide matches.


RESULTS
A robust linear regression based algorithm was developed to automate the evaluation of peptide identifications obtained from shotgun proteomic experiments. The algorithm scores peptides based on their predicted and observed reversed-phase liquid chromatography retention times. The robust algorithm does not require internal or external peptide standards to train or calibrate the linear regression model used for peptide retention time prediction. The algorithm is generic and can be incorporated into any database search program to perform automated evaluation of the candidate peptide matches based on their retention times. It provides a statistical score for each peptide match based on its retention time.


CONCLUSIONS
Analysis of peptide matches where the retention time score was included resulted in a significant reduction of false positive matches with little effect on the number of true positives. Overall higher sensitivities and specificities were achieved for database searches carried out with MassMatrix, Mascot and X!Tandem after implementation of the retention time based score algorithm.
Xu
Hua
H
Department of Molecular Virology Immunology and Medical Genetics, Comprehensive Cancer Center, the Ohio State University Medical Center, Columbus, OH 43210, USA. xu.171@osu.edu
Yang
Lanhao
L
Freitas
Michael A
MA
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D002853:Chromatography, Liquid / Q000379:methods*; D003198:Computer Simulation; D016014:Linear Models; D008956:Models, Chemical; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D010455:Peptides / Q000737:chemistry*; D020543:Proteome / Q000737:chemistry*; D012044:Regression Analysis; D020539:Sequence Analysis, Protein / Q000379:methods*; D013997:Time Factors
D010455:Peptides; D020543:Proteome