Performance Evaluation of Existing De Novo Sequencing Algorithms
peer-reviewed · Journal of Proteome Research · 2006
| Date | 2006-11-01 |
| Type | peer-reviewed |
| Venue | Journal of Proteome Research |
| Publisher | American Chemical Society (ACS) |
| Contribution | benchmark |
| DOI | 10.1021/pr060222h |
| Citations (OpenAlex) | 105 |
| Venue 2-year citedness | 3.48 |
Abstract
Two methods have been developed for protein identification from tandem mass spectra: database searching and de novo sequencing. De novo sequencing identifies peptide directly from tandem mass spectra. Among many proposed algorithms, we evaluated the performance of the five de novo sequencing algorithms, AUDENS, Lutefisk, NovoHMM, PepNovo, and PEAKS. Our evaluation methods are based on calculation of relative sequence distance (RSD), algorithm sensitivity, and spectrum quality. We found that de novo sequencing algorithms have different performance in analyzing QSTAR and LCQ mass spectrometer data, but in general, perform better in analyzing QSTAR data than LCQ data. For the QSTAR data, the performance order of the five algorithms is PEAKS > Lutefisk, PepNovo > AUDENS, NovoHMM. The performance of PEAKS, Lutefisk, and PepNovo strongly depends on the spectrum quality and increases with an increase of spectrum quality. However, AUDENS and NovoHMM are not sensitive to the spectrum quality. Compared with other four algorithms, PEAKS has the best sensitivity and also has the best performance in the entire range of spectrum quality. For the LCQ data, the performance order is NovoHMM > PepNovo, PEAKS > Lutefisk > AUDENS. NovoHMM has the best sensitivity, and its performance is the best in the entire range of spectrum quality. But the overall performance of NovoHMM is not significantly different from the performance of PEAKS and PepNovo. AUDENS does not give a good performance in analyzing either QSTAR and LCQ data.
Methods and tools
- Performance evaluation of early de novo sequencing algorithms: Performance evaluation of existing de novo sequencing algorithms.
Cites (9)
- Proteomics-Grade de Novo Sequencing Approach (2005) crossref
- NovoHMM: A Hidden Markov Model for de Novo Peptide Sequencing (2005) crossref
- AUDENS: A Tool for Automated Peptide de Novo Sequencing (2005) crossref
- PepNovo: de novo peptide sequencing via probabilistic network modeling (2005) crossref
- New computational approaches for de novo peptide sequencing from MS/MS experiments (2002) crossref
- A Dynamic Programming Approach to De Novo Peptide Sequencing via Tandem Mass Spectrometry (2001) crossref
- De novo peptide sequencing via tandem mass spectrometry (1999) crossref
- Sequence database searches via de novo peptide sequencing by tandem mass spectrometry (1997) crossref
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Cited by (14)
- Prime-DiffNovo: Accurate Peptide De Novo Sequencing via Non-autoregressive Generation and Diffusion Refinement (2026) crossref
- Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly (2023) both
- De novo sequencing of proteins by mass spectrometry (2020) both
- Evaluating de novo sequencing in proteomics: already an accurate alternative to database-driven peptide identification? (2017) crossref
- Peptide de novo sequencing of mixture tandem mass spectra (2016) both
- Novor: Real-Time Peptide de Novo Sequencing Software (2015) both
- DeNovoGUI: An Open Source Graphical User Interface for de Novo Sequencing of Tandem Mass Spectra (2014) 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
- Identification of a novel Plasmopara halstedii elicitor protein combining de novo peptide sequencing algorithms and RACE-PCR (2010) semanticscholar
- pNovo: De novo Peptide Sequencing and Identification Using HCD Spectra (2010) crossref
- De novo sequencing of peptides by MS/MS (2010) crossref
- Assessing peptide de novo sequencing algorithms performance on large and diverse data sets (2007) crossref