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Updated: Sep 23, 2025
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Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
Published on: March 14, 2013
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Building Spectral Libraries from Narrow-Window Data-Independent Acquisition Mass Spectrometry Data
Lilian R Heil1, William E Fondrie1, Christopher D McGann1
1Department of Genome Sciences, University of Washington, Seattle, Washington 98105, United States.
Journal of Proteome Research
|May 13, 2022
Summary
This study introduces a new workflow for creating peptide spectral libraries from mass spectrometry data, improving accuracy and detecting modified peptides without common library biases.
Area of Science:
- Proteomics
- Analytical Chemistry
- Biochemistry
Background:
- Data-independent acquisition (DIA) mass spectrometry enables large-scale peptide detection.
- Existing library-based methods require comprehensive spectral libraries, often generated with biases.
- In silico libraries struggle with post-translational modifications.
Purpose of the Study:
- To develop a false discovery rate-controlled workflow for generating spectral libraries directly from DIA data.
- To enable accurate peptide detection and quantitation, including phosphorylated peptides.
- To overcome limitations of existing spectral library generation methods.
Main Methods:
- A spectrum-centric search workflow was developed for gas-phase fractionated DIA tandem mass spectrometry data.
- The workflow generates spectral libraries directly from DIA data.
- Performance was compared against library-free approaches.
Main Results:
- The proposed workflow successfully detects phosphorylated peptides.
- It generates a spectral library suitable for accurate peptide detection and quantitation in wide-window DIA data.
- The method is competitive with library-free approaches in accuracy and sensitivity.
Conclusions:
- The developed workflow effectively generates spectral libraries from DIA data.
- This approach avoids biases associated with empirical libraries and limitations of in silico methods.
- It offers a robust strategy for enhancing peptide identification and quantitation in proteomics.
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