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A Ready-to-Use Data Analysis Pipeline for BioID Experiments Using Data-Dependent or Data-Independent Acquisition Mass
Sara Dufour1,2,3, Teresa Mendes Maia1,2,3, Laura Van Moortel1,2
1VIB-UGent Center for Medical Biotechnology, Ghent, Belgium.
Methods in Molecular Biology (Clifton, N.J.)
|July 10, 2025
Summary
This study presents a data analysis workflow for proximity-dependent biotin identification (BioID) experiments using mass spectrometry (MS). It compares data-dependent acquisition (DDA) and data-independent acquisition (DIA) methods for improved protein interaction identification.
Area of Science:
- Proteomics
- Molecular Biology
- Biochemistry
Background:
- Proximity labeling techniques like BioID are crucial for interactomics.
- Traditional data-dependent acquisition (DDA) in mass spectrometry (MS) has limitations in identifying low-abundance peptides.
Purpose of the Study:
- To outline a comprehensive data analysis workflow for BioID experiments.
- To compare the efficacy of DDA and data-independent acquisition (DIA) for BioID.
- To enhance the identification of protein-protein interactions.
Main Methods:
- Utilized proximity-dependent biotin identification (BioID) with mass spectrometry (MS).
- Applied both data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes.
- Employed MaxQuant, FragPipe, DIA-NN, R, and SAINTq for data analysis and statistical validation.
Main Results:
- Developed and validated a data analysis pipeline for BioID experiments.
- Demonstrated that DIA offers more comprehensive proteomic analysis compared to DDA.
- Successfully identified bait-prey interactions using the developed workflow.
Conclusions:
- The presented workflow enhances the reliability of identifying protein interactions from BioID experiments.
- DIA-MS is recommended for comprehensive proteomic profiling in BioID studies.
- This approach provides a robust method for exploring protein proximity in native cellular contexts.
