Aligning LC peaks by converting gradient retention times to retention index of peptides in proteomic experiments

Kosaku Shinoda, Masaru Tomita, Yasushi Ishihama

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Motivation: 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. Results: We have developed a new alignment method for peptide retention times based on linear solvent strength (LSS) theory. We found that log k0 (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 (tg) to log k0. Using the optimized function, tg 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.

Original languageEnglish (US)
Pages (from-to)1590-1595
Number of pages6
Issue number14
StatePublished - Jul 2008
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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