Preprocessing Tandem Mass Spectra Using Genetic Programming for Peptide Identification
peer-reviewed · Journal of the American Society for Mass Spectrometry · 2019
| Date | 2019-07-01 |
| Type | peer-reviewed |
| Venue | Journal of the American Society for Mass Spectrometry |
| Publisher | American Chemical Society (ACS) |
| Contribution | post-processor |
| DOI | 10.1007/s13361-019-02196-5 |
| Citations (OpenAlex) | 1 |
| Venue 2-year citedness | 2.84 |
Abstract
One of the major challenges in proteomics is peptide identification from mass spectra containing high noise ratio and small number of signal (b-/y-ions) peaks. However, the accuracy and reliability of peptide identification in such highly imbalanced MS/MS data can be improved by applying a preprocessing step prior to peptide identification aiming at discriminating b-/y-ions from noise peaks in the spectra. In this study, we report a genetic programming (GP)-based preprocessing method for de-noising highly imbalanced and noisy CID MS/MS spectra. GP now becomes a popular machine learning method via automatic programming. GP preprocesses the highly noisy MS/MS spectra by classifying peaks as noise peaks or signal peaks in a binary classification manner. Meanwhile, a set of spectral fragment features based on the MS/MS fragmentation rules is extracted from the dataset to investigate their discriminating abilities by GP. A MS/MS spectral dataset containing thousands of spectra are used to train the GP model. As the GP tree-based representation has the capability for implicit feature selection during the evolutionary process, the evolved GP model with the selected features is compared with the best threshold-based method. The results show that the GP method improved the reliability of peptide identification and increased the identification rate of a de novo sequencing tool, PEAKS, to 99.4% from 80.1% achieved by the best threshold-based method. Moreover, the result of peptide identification by a database search tool, SEQUEST, using the data preprocessed by the GP method was statistically significant compared to the other methods.
Methods and tools
- GP-based MS/MS spectrum denoising: Genetic programming classifiers that separate b/y-ion peaks from noise before identification, raising PEAKS de novo identification rates on noisy CID spectra.
Methods it uses
- PEAKS: Commercial DP-based de novo