Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-24597
Fischer, B; Roth, V; Roos, F; Grossmann, J; Baginsky, S; Widmayer, P; Gruissem, W; Buhmann, J M (2005). NovoHMM: a hidden Markov model for de novo peptide sequencing. Analytical Chemistry, 77(22):7265-7273.
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De novo sequencing of peptides poses one of the most challenging tasks in data analysis for proteome research. In this paper, a generative hidden Markov model (HMM) of mass spectra for de novo peptide sequencing which constitutes a novel view on how to solve this problem in a Bayesian framework is proposed. Further extensions of the model structure to a graphical model and a factorial HMM to substantially improve the peptide identification results are demonstrated. Inference with the graphical model for de novo peptide sequencing estimates posterior probabilities for amino acids rather than scores for single symbols in the sequence. Our model outperforms state-of-the-art methods for de novo peptide sequencing on a large test set of spectra.
|Item Type:||Journal Article, refereed, original work|
|Communities & Collections:||04 Faculty of Medicine > Functional Genomics Center Zurich|
08 University Research Priority Programs > Systems Biology / Functional Genomics
|DDC:||570 Life sciences; biology|
610 Medicine & health
|Deposited On:||18 Dec 2009 05:35|
|Last Modified:||27 Nov 2013 18:35|
|Publisher:||American Chemical Society|
|Citations:||Web of Science®. Times Cited: 68|
Scopus®. Citation Count: 76
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