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Updated: Dec 30, 2025

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Published on: August 19, 2025
Removing the Hidden Data Dependency of DIA with Predicted Spectral Libraries
Bart Van Puyvelde1, Sander Willems1, Ralf Gabriels2,3
1ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, 9000, Ghent, Belgium.
Data-independent acquisition (DIA) now offers deep peptide detection without data-dependent acquisition (DDA) libraries. Combining predicted intensities and retention times with narrow window DIA makes the workflow deterministic and improves clinical proteomics comparisons.
Area of Science:
- Proteomics
- Mass Spectrometry
- Analytical Chemistry
Background:
- Data-independent acquisition (DIA) in mass spectrometry generates complex data.
- Deep peptide detection typically requires data-dependent acquisition (DDA) libraries.
- Current DIA workflows can have variations and stochastic sampling issues.
Purpose of the Study:
- To eliminate the dependency on DDA libraries for DIA.
- To develop a deterministic DIA workflow for enhanced peptide detection.
- To facilitate inter-laboratory comparisons in clinical proteomics.
Main Methods:
- Combining predicted fragment intensities with retention times.
- Implementing narrow window DIA acquisition.
- Developing a library-free DIA approach.
Main Results:
- Achieved deep peptide-centric detection in DIA without DDA libraries.
- Demonstrated a fully deterministic DIA workflow.
- Eliminated variation associated with library building and stochastic sampling.
Conclusions:
- DIA can be performed without DDA libraries by integrating predicted data.
- The novel DIA approach enhances reproducibility and comparability, particularly for clinical applications.
- This advancement simplifies and standardizes proteomic data analysis.
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