NovoHMM: A Hidden Markov Model for de Novo Peptide Sequencing
peer-reviewed · Analytical Chemistry · 2005
peer-reviewed · Analytical Chemistry · 2005. Bernd Fischer et al. De novo sequencing of peptides poses one of the most challenging tasks in data analysis for proteome…
| Date | 2005-11-15 |
| Type | peer-reviewed |
| Venue | Analytical Chemistry |
| Publisher | American Chemical Society (ACS) |
| Contribution | algorithm |
| DOI | 10.1021/ac0508853 |
| Citations (OpenAlex) | 192 |
| Venue 2-year citedness | 7.10 |
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.
Methods and tools
- NovoHMM: Generative HMM scoring
Cites (3)
Cited by (86)
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Cited in patents (4)
- US11573239B2 Methods and systems for de novo peptide sequencing using deep learning ↗ (Bioinformatics Solutions Inc., priority 2017-07-17, active)
- US11694769B2 Systems and methods for de novo peptide sequencing from data-independent acquisition using deep learning ↗ (Bioinformatics Solutions Inc., priority 2017-07-17, active)
- US12340874B1 Artificial neural network models for prediction of de novo sequencing of chains of amino acids ↗ (Verily Life Sciences, priority 2017-11-03, active)
- US11644470B2 Systems and methods for de novo peptide sequencing using deep learning and spectrum pairs ↗ (Bioinformatics Solutions Inc., priority 2019-04-15, active)
Patents that list this paper among the literature they cite, whether the applicant or the examiner added it.