A Ranking-Based Scoring Function for Peptide-Spectrum Matches
peer-reviewed · Journal of Proteome Research · 2009
| Date | 2009-05-01 |
| Type | peer-reviewed |
| Venue | Journal of Proteome Research |
| Publisher | American Chemical Society (ACS) |
| Contribution | algorithm |
| DOI | 10.1021/pr800678b |
| Citations (OpenAlex) | 85 |
| Venue 2-year citedness | 3.83 |
Abstract
The analysis of the large volume of tandem mass spectrometry (MS/MS) proteomics data that is generated these days relies on automated algorithms that identify peptides from their mass spectra. An essential component of these algorithms is the scoring function used to evaluate the quality of peptide-spectrum matches (PSMs). In this paper, we present new approach to scoring of PSMs. We argue that since this problem is at its core a ranking task (especially in the case of de novo sequencing), it can be solved effectively using machine learning ranking algorithms. We developed a new discriminative boosting-based approach to scoring. Our scoring models draw upon a large set of diverse feature functions that measure different qualities of PSMs. Our method improves the performance of our de novo sequencing algorithm beyond the current state-of-the-art, and also greatly enhances the performance of database search programs. Furthermore, by increasing the efficiency of tag filtration and improving the sensitivity of PSM scoring, we make it practical to perform large-scale MS/MS analysis, such as proteogenomic search of a six-frame translation of the human genome (in which we achieve a reduction of the running time by a factor of 15 and a 60% increase in the number of identified peptides, compared to the InsPecT database search tool). Our scoring function is incorporated into PepNovo+ which is available for download or can be run online at http://bix.ucsd.edu.
Methods and tools
- PepNovo: Probabilistic network + DP
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- Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins (2024) crossref
- PeposX-Exhaust: A lightweight and efficient tool for identification of short peptides (2024) both
- Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins (2023) both
- Peposx-Exhaust: A Lightweight and Efficient Tool for Identification of Short Peptides (2023) crossref
- Uncovering Hidden Members and Functions of the Soil Microbiome Using De Novo Metaproteomics (2022) semanticscholar
- Validation of De Novo Peptide Sequences with Bottom-Up Tag Convolution (2021) both
- DNMSO; an ontology for representing de novo sequencing results from Tandem-MS data (2020) both
- SWPepNovo: An Efficient De Novo Peptide Sequencing Tool for Large-scale MS/MS Spectra Analysis (2019) semanticscholar
- Postnovo: Postprocessing Enables Accurate and FDR-Controlled de Novo Peptide Sequencing (2018) crossref
- De Novo Sequencing of Proteins and Peptides: Algorithms, Applications, Perspectives (2018) crossref
- Comprehensive de Novo Peptide Sequencing from MS/MS Pairs Generated through Complementary Collision Induced Dissociation and 351 nm Ultraviolet Photodissociation (2017) both
- Peptide de novo sequencing of mixture tandem mass spectra (2016) both
- Neutron-encoded Signatures Enable Product Ion Annotation From Tandem Mass Spectra (2013) both
- UniNovo: a universal tool for de novo peptide sequencing (2013) both
- Sequencing-Grade De novo Analysis of MS/MS Triplets (CID/HCD/ETD) From Overlapping Peptides (2013) both
- ScanRanker: Quality Assessment of Tandem Mass Spectra via Sequence Tagging (2011) both
- Gapped Spectral Dictionaries and Their Applications for Database Searches of Tandem Mass Spectra (2011) both
- Gapped Spectral Dictionaries and Their Applications for Database Searches of Tandem Mass Spectra (2010) crossref
- A high-throughput de novo sequencing approach for shotgun proteomics using high-resolution tandem mass spectrometry (2010) both
- Spectral Profiles, a Novel Representation of Tandem Mass Spectra and Their Applications for de Novo Peptide Sequencing and Identification (2009) both