A Hidden Markov Model for de Novo Peptide Sequencing
ML conference · NIPS 2004 · 2004
| Date | 2004-12-13 |
| Type | ML conference |
| Venue | NIPS 2004 |
| Publisher | MIT Press |
| Contribution | algorithm |
| Link | https://papers.nips.cc/paper_files/paper/2004 |
| Citations (OpenAlex) | 192 |
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
Cited by (9)
- Deep Learning Methods for De Novo Peptide Sequencing (2024) crossref
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2024) both
- Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model (2023) semanticscholar
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2023) crossref
- DePS: An improved deep learning model for de novo peptide sequencing (2022) semanticscholar
- De novo mass spectrometry peptide sequencing with a transformer model (2022) both
- De novo sequencing of proteins by mass spectrometry (2020) semanticscholar
- Peptide de novo sequencing of mixture tandem mass spectra (2016) semanticscholar
- Antilope—A Lagrangian Relaxation Approach to the de novo Peptide Sequencing Problem (2012) semanticscholar