Unassigned MS/MS Spectra: Who Am I?
Mohashin Pathan1, Monisha Samuel2, Shivakumar Keerthikumar1
1Department of Biochemistry and Genetics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, VIC, 3086, Australia.
Methods in Molecular Biology (Clifton, N.J.)
|December 16, 2016
Summary
High-quality mass spectrometry (MS) data requires advanced bioinformatics tools for protein identification. This work discusses methods to analyze unassigned tandem MS spectra, improving protein discovery.
Area of Science:
- Proteomics
- Bioinformatics
- Analytical Chemistry
Background:
- High-resolution tandem mass spectrometry (MS) generates high-quality data.
- Bioinformatics software has advanced for analyzing MS datasets.
- Despite advances, protein identification from MS data remains challenging.
Purpose of the Study:
- To discuss tools for protein identification using mass spectrometry.
- To explore methods for assigning unassigned tandem MS spectra.
- To address challenges in MS data analysis.
Main Methods:
- Review of bioinformatics tools for protein identification.
- Analysis of factors contributing to unassigned MS/MS spectra.
- Discussion of strategies for spectral assignment.
Main Results:
- Approximately 60% of MS/MS spectra remain unassigned in typical analyses.
- Unassigned spectra can result from low quality, search space limitations, annotation errors, or posttranslational modifications.
- High-quality unassigned spectra warrant further investigation.
Conclusions:
- Effective protein identification relies on robust MS data analysis.
- Assigning unassigned tandem MS spectra is crucial for comprehensive proteomic studies.
- Further research can elucidate the causes of unassigned spectra to enhance protein discovery.
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