In proteomics, many methods for the identification of proteins have been developed. However, because of limited known genome sequences, noisy data, incomplete ion sequences, and the accuracy of protein identification,...In proteomics, many methods for the identification of proteins have been developed. However, because of limited known genome sequences, noisy data, incomplete ion sequences, and the accuracy of protein identification,it is challenging to identify peptides using tandem mass spectral data. Noise filtering and removing thus play a key role in accurate peptide identification from tandem mass spectra. In this paper, we employ a Bayesian model to identify proteins based on the prior information of bond cleavages. A Markov Chain Monte Carlo（MCMC）algorithm is used to simulate candidate peptides from the posterior distribution and to estimate the parameters for the Bayesian model. Our simulation and computational experimental results show that the model can identify peptide with a higher accuracy.展开更多
基金supported by an NSF Science and Technology Center,under Grant Agreement CCF-0939370 and 2 G12 RR003048 from the RCMI program,Division of Research Infrastructure,National Center for Research Resources,NIH
文摘In proteomics, many methods for the identification of proteins have been developed. However, because of limited known genome sequences, noisy data, incomplete ion sequences, and the accuracy of protein identification,it is challenging to identify peptides using tandem mass spectral data. Noise filtering and removing thus play a key role in accurate peptide identification from tandem mass spectra. In this paper, we employ a Bayesian model to identify proteins based on the prior information of bond cleavages. A Markov Chain Monte Carlo（MCMC）algorithm is used to simulate candidate peptides from the posterior distribution and to estimate the parameters for the Bayesian model. Our simulation and computational experimental results show that the model can identify peptide with a higher accuracy.
