ProbPS: A new model for peak selection based on quantifying the dependence of the existence of derivative peaks on primary ion intensity
peer-reviewed · BMC Bioinformatics · 2011
| Date | 2011-08-17 |
| Type | peer-reviewed |
| Venue | BMC Bioinformatics |
| Publisher | Springer Science and Business Media LLC |
| Contribution | post-processor |
| DOI | 10.1186/1471-2105-12-346 |
| Citations (OpenAlex) | 3 |
| Venue 2-year citedness | 4.71 |
Abstract
Background The analysis of mass spectra suggests that the existence of derivative peaks is strongly dependent on the intensity of the primary peaks. Peak selection from tandem mass spectrum is used to filter out noise and contaminant peaks. It is widely accepted that a valid primary peak tends to have high intensity and is accompanied by derivative peaks, including isotopic peaks, neutral loss peaks, and complementary peaks. Existing models for peak selection ignore the dependence between the existence of the derivative peaks and the intensity of the primary peaks. Simple models for peak selection assume that these two attributes are independent; however, this assumption is contrary to real data and prone to error. Results In this paper, we present a statistical model to quantitatively measure the dependence of the derivative peak’s existence on the primary peak’s intensity. Here, we propose a statistical model, named ProbPS, to capture the dependence in a quantitative manner and describe a statistical model for peak selection. Our results show that the quantitative understanding can successfully guide the peak selection process. By comparing ProbPS with AuDeNS we demonstrate the advantages of our method in both filtering out noise peaks and in improving de novo identification. In addition, we present a tag identification approach based on our peak selection method. Our results, using a test data set, suggest that our tag identification method (876 correct tags in 1000 spectra) outperforms PepNovoTag (790 correct tags in 1000 spectra). Conclusions We have shown that ProbPS improves the accuracy of peak selection which further enhances the performance of de novo sequencing and tag identification. Thus, our model saves valuable computation time and improving the accuracy of the results.
Methods and tools
- ProbPS: Peak selection model that quantifies how the presence of a derivative peak depends on its primary ion’s intensity, so noise can be discarded before sequencing. Benchmarked on the de novo and sequence-tag performance it enables rather than on peak counts.
Cites (31)
- pNovo: De novo Peptide Sequencing and Identification Using HCD Spectra (2010) both
- De Novo Sequencing of Unique Sequence Tags for Discovery of Post-Translational Modifications of Proteins (2008) both
- DirecTag: Accurate Sequence Tags from Peptide MS/MS through Statistical Scoring (2008) both
- MSNovo: A Dynamic Programming Algorithm for de Novo Peptide Sequencing via Tandem Mass Spectrometry (2007) both
- De Novo Peptide Identification via Tandem Mass Spectrometry and Integer Linear Optimization (2007) both
- De Novo Peptide Sequencing and Identification with Precision Mass Spectrometry (2007) both
- De Novo Analysis of Peptide Tandem Mass Spectra by Spectral Graph Partitioning (2006) both
- NovoHMM: A Hidden Markov Model for de Novo Peptide Sequencing (2005) crossref
- Robust accurate identification of peptides (RAId): deciphering MS2 data using a structured library search with de novo based statistics (2005) both
- AUDENS: A Tool for Automated Peptide de Novo Sequencing (2005) both
- Peptide Sequence Tags for Fast Database Search in Mass-Spectrometry (2005) crossref
- PRIME: A Mass Spectrum Data Mining Tool for De Nova Sequencing and PTMs Identification (2005) both
- An effective algorithm for peptide de novo sequencing from MS/MS spectra (2005) both
- A graph-theoretic approach for the separation of b and y ions in tandem mass spectra (2005) both
- PepNovo: de novo peptide sequencing via probabilistic network modeling (2005) both
- A Hidden Markov Model for de Novo Peptide Sequencing (2004) semanticscholar
- PPM-chain - de novo peptide identification program comparable in performance to sequest (2004) both
- De Novo Peptide Sequencing Based on a Divide-and-Conquer Algorithm and Peptide Tandem Spectrum Simulation (2004) both
- Sequit: software for de novo peptide sequencing by matrix-assisted laser desorption/ionization post-source decay mass spectrometry (2004) both
- GutenTag: High-Throughput Sequence Tagging via an Empirically Derived Fragmentation Model (2003) both
- PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry (2003) both
- MultiTag: Multiple Error-Tolerant Sequence Tag Search for the Sequence-Similarity Identification of Proteins by Mass Spectrometry (2003) both
- A Suboptimal Algorithm for De Novo Peptide Sequencing via Tandem Mass Spectrometry (2003) both
- Searching Sequence Databases via De Novo Peptide Sequencing by Tandem Mass Spectrometry (2002) both
- A Dynamic Programming Approach to De Novo Peptide Sequencing via Tandem Mass Spectrometry (2001) both
- Implementation and Uses of Automated de Novo Peptide Sequencing by Tandem Mass Spectrometry (2001) both
- De novo peptide sequencing via tandem mass spectrometry (1999) both
- Automated interpretation of high-energy collision-induced dissociation spectra of singly protonated peptides by ‘seqms’, a software aid forde novo sequencing by tandem mass spectrometry (1998) both
- Sequence database searches via de novo peptide sequencing by tandem mass spectrometry (1997) both
- Error-Tolerant Identification of Peptides in Sequence Databases by Peptide Sequence Tags (1994) both
- Fast algorithm for peptide sequencing by mass spectroscopy (1990) both
Cited by (1)
- Tandem mass intensity estimation for de novo peptide sequencing (2018) crossref