ProbPS: A New Model for Peak Selection in Mass Spectrometry
Author Information
Author(s): Zhang Shenghui, Wang Yaojun, Bu Dongbo, Zhang Hong, Sun Shiwei
Primary Institution: Institute of Computing Technology, Chinese Academy of Sciences
Hypothesis
The existence of derivative peaks is dependent on the intensity of primary peaks in mass spectrometry.
Conclusion
ProbPS improves the accuracy of peak selection, enhancing de novo sequencing and tag identification performance.
Supporting Evidence
- ProbPS outperformed the existing method AuDeNS in filtering out noise peaks.
- ProbPS achieved a higher true positive rate compared to relevance values used in AuDeNS.
- The tag identification method based on ProbPS found more correct tags than PepNovoTag.
Takeaway
This study created a new method to help scientists pick the right peaks in mass spectrometry, which makes identifying proteins easier and faster.
Methodology
A statistical model named ProbPS was developed to quantify the dependence of derivative peaks on primary peak intensity using a training set of mass spectra.
Limitations
The study primarily focused on specific types of peaks and may not generalize to all mass spectrometry scenarios.
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website