Title
Aligning LC peaks by converting gradient retention times to retention index of peptides in proteomic experiments.
Abstract
Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a powerful tool in proteomics studies, but when peptide retention information is used for identification purposes, it remains challenging to compare multiple LC-MS/MS runs or to match observed and predicted retention times, because small changes of LC conditions unavoidably lead to variability in retention times. In addition, non-contiguous retention data obtained with different LC-MS instruments or in different laboratories must be aligned to confirm and utilize rapidly accumulating published proteomics data.We have developed a new alignment method for peptide retention times based on linear solvent strength (LSS) theory. We found that log k(0) (logarithm of retention factor for a given organic solvent) in the LSS theory can be utilized as a 'universal' retention index of peptides (RIP) that is independent of LC gradients, and depends solely on the constituents of the mobile phase and the stationary phases. We introduced a machine learning-based scheme to optimize the conversion function of gradient retention times (t(g)) to log k(0). Using the optimized function, t(g) values obtained with different LC-MS systems can be directly compared with each other on the RIP scale. In an examination of Arabidopsis proteomic data, the vast majority of retention time variability was removed, and five datasets obtained with various LC-MS systems were successfully aligned on the RIP scale.
Year
DOI
Venue
2008
10.1093/bioinformatics/btn240
Bioinformatics
Keywords
Field
DocType
retention factor,peptide retention time,proteomic experiment,different lc-ms instrument,aligning lc peak,gradient retention time,retention time,peptide retention information,non-contiguous retention data,retention time variability,rip scale,retention index,liquid chromatography,machine learning,tandem mass spectrometry
Proteomics,Computer science,Kovats retention index,Mass spectrometry,Logarithm,Bioinformatics
Journal
Volume
Issue
ISSN
24
14
1367-4811
Citations 
PageRank 
References 
0
0.34
3
Authors
3
Name
Order
Citations
PageRank
Kosaku Shinoda1271.92
Masaru Tomita21009180.20
Yasushi Ishihama3374.71