A regularized method for peptide quantification.
Peptide abundance estimation is generally the first step in protein quantification. In peptide abundance estimation, peptide overlapping and peak intensity variation are two challenges. The main objective of this paper is to estimate peptide abundance by taking advantage of peptide isotopic distribution and smoothness of peptide elution profile. Our method proposes to solve the peptide overlapping problem and provides a way to control the variance of estimation. We compare our method with a commonly used method on simulated data sets and two real data sets of standard protein mixtures. The results show that our method achieves more accurate estimation of peptide abundance on different samples. In our method, there is a variance-related parameter. Considering the well-known trade-off between the variance and the bias of estimation, we should not only focus on reducing the variance in real applications. A suggestion about parameter selection is given based on the discussion of variance and bias. Matlab source codes and detailed experimental results are available at http://bioinformatics.ust.hk/PeptideQuant/peptidequant.htm.
Yang
Chao
C
Laboratory for Bioinformatics and Computational Biology, Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
Yang
Can
C
Yu
Weichuan
W
D000465:Algorithms; D003198:Computer Simulation; D016018:Least-Squares Analysis; D016011:Normal Distribution; D010446:Peptide Fragments / Q000032:analysis* / Q000737:chemistry; D011506:Proteins / Q000032:analysis* / Q000737:chemistry; D040901:Proteomics / Q000379:methods*; D021241:Spectrometry, Mass, Electrospray Ionization / Q000379:methods*
D010446:Peptide Fragments; D011506:Proteins