Quick Search:

uzh logo
Browse by:

Zurich Open Repository and Archive 

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.

[img] PDF - Registered users only


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
Publisher DOI:10.1021/ac0508853
PubMed ID:16285674
Citations:Web of Science®. Times Cited: 70
Google Scholar™
Scopus®. Citation Count: 77

Users (please log in): suggest update or correction for this item

Repository Staff Only: item control page