MSNovo: A Dynamic Programming Algorithm for de Novo Peptide Sequencing via Tandem Mass Spectrometry
peer-reviewed · Analytical Chemistry · 2007
| Date | 2007-06-15 |
| Type | peer-reviewed |
| Venue | Analytical Chemistry |
| Publisher | American Chemical Society |
| Contribution | algorithm |
| DOI | 10.1021/ac070039n |
| Citations (OpenAlex) | 94 |
| Venue 2-year citedness | 6.29 |
Abstract
Tandem mass spectrometry (MS/MS) has become the experimental method of choice for high-throughput proteomics-based biological discovery. The two primary ways of analyzing MS/MS data are database search and de novo sequencing. In this paper, we present a new approach to peptide de novo sequencing, called MSNovo, which has the following advanced features. (1) It works on data generated from both LCQ and LTQ mass spectrometers and interprets singly, doubly, and triply charged ions. (2) It integrates a new probabilistic scoring function with a mass array-based dynamic programming algorithm. The simplicity of the scoring function, with only 6-10 parameters to be trained, avoids the problem of overfitting and allows MSNovo to be adopted for other machines and data sets easily. The mass array data structure explicitly encodes all possible peptides and allows the dynamic programming algorithm to find the best peptide. (3) Compared to existing programs, MSNovo predicts peptides as well as sequence tags with a higher accuracy, which is important for those applications that search protein databases using the de novo sequencing results. More specifically, we show that MSNovo outperforms other programs on various ESI ion trap data. We also show that for high-resolution data the performance of MSNovo improves significantly. Supporting Information, executable files and data sets can be found at http://msms.usc.edu/supplementary/msnovo.
Methods and tools
- MSNovo: Mass-array dynamic programming
Cites (9)
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Cited by (20)
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- PepGo: a deep learning and tree search-based model for de novo peptide sequencing (2025) crossref
- Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model (2023) both
- Algorithms for de-novo sequencing of peptides by tandem mass spectrometry: A review (2023) crossref
- Uncovering Hidden Members and Functions of the Soil Microbiome Using De Novo Metaproteomics (2022) semanticscholar
- DePS: An improved deep learning model for de novo peptide sequencing (2022) semanticscholar
- De novo sequencing of proteins by mass spectrometry (2020) both
- A potential golden age to come—current tools, recent use cases, and future avenues for de novo sequencing in proteomics (2018) crossref
- De novo peptide sequencing by deep learning (2017) crossref
- Evaluating de novo sequencing in proteomics: already an accurate alternative to database-driven peptide identification? (2017) crossref
- UVnovo: A de Novo Sequencing Algorithm Using Single Series of Fragment Ions via Chromophore Tagging and 351 nm Ultraviolet Photodissociation Mass Spectrometry (2016) both
- Novor: Real-Time Peptide de Novo Sequencing Software (2015) both
- pNovo+: De Novo Peptide Sequencing Using Complementary HCD and ETD Tandem Mass Spectra (2013) crossref
- De Novo Sequencing and Homology Searching (2012) both
- Algorithms for the de novo sequencing of peptides from tandem mass spectra (2011) crossref
- pNovo: De novo Peptide Sequencing and Identification Using HCD Spectra (2010) crossref
- A high-throughput de novo sequencing approach for shotgun proteomics using high-resolution tandem mass spectrometry (2010) both
- De novo sequencing of peptides by MS/MS (2010) crossref
- Spectral Dictionaries: Integrating de novo Peptide Sequencing with Database Search of Tandem Mass Spectra (2009) both
- Automated de novo protein sequencing of monoclonal antibodies (2008) both