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NovoHMM: a hidden Markov model for de novo peptide sequencing


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.

Abstract

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.

Abstract

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.

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Additional indexing

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
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Language:English
Date:2005
Deposited On:18 Dec 2009 05:35
Last Modified:06 Dec 2017 22:04
Publisher:American Chemical Society
ISSN:0003-2700
Publisher DOI:https://doi.org/10.1021/ac0508853
PubMed ID:16285674

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