MS2PIP: a tool for MS/MS peak intensity prediction
peer-reviewed · Bioinformatics · 2013
| Date | 2013-09-27 |
| Type | peer-reviewed |
| Venue | Bioinformatics |
| Publisher | Oxford University Press (OUP) |
| Contribution | adjacent |
| DOI | 10.1093/bioinformatics/btt544 |
| Citations (OpenAlex) | 178 |
| Venue 2-year citedness | 5.93 |
Abstract
MOTIVATION: Tandem mass spectrometry provides the means to match mass spectrometry signal observations with the chemical entities that generated them. The technology produces signal spectra that contain information about the chemical dissociation pattern of a peptide that was forced to fragment using methods like collision-induced dissociation. The ability to predict these MS(2) signals and to understand this fragmentation process is important for sensitive high-throughput proteomics research. RESULTS: We present a new tool called MS(2)PIP for predicting the intensity of the most important fragment ion signal peaks from a peptide sequence. MS(2)PIP pre-processes a large dataset with confident peptide-to-spectrum matches to facilitate data-driven model induction using a random forest regression learning algorithm. The intensity predictions of MS(2)PIP were evaluated on several independent evaluation sets and found to correlate significantly better with the observed fragment-ion intensities as compared with the current state-of-the-art PeptideART tool. AVAILABILITY: MS(2)PIP code is available for both training and predicting at http://compomics.com/.
Methods and tools
- MS2PIP: Original MS2PIP: random-forest predictor of MS/MS fragment-ion peak intensities used to rescore search-engine and de novo sequencing results. Later updates (2019 web server, 2024 DL backbone) live in algorithm.short_description.
Cited by (7)
- Limitations of de novo sequencing in resolving sequence ambiguity (2025) crossref
- PepGo: a deep learning and tree search-based model for de novo peptide sequencing (2025) crossref
- Bridging the Gap between Database Search and De Novo Peptide Sequencing with SearchNovo (2024) both
- MS2Rescore: Data-Driven Rescoring Dramatically Boosts Immunopeptide Identification Rates (2022) both
- Deep Learning in Proteomics (2020) both
- A potential golden age to come—current tools, recent use cases, and future avenues for de novo sequencing in proteomics (2018) crossref
- Evaluating de novo sequencing in proteomics: already an accurate alternative to database-driven peptide identification? (2017) crossref