ArticleMethods in molecular biology (Clifton, N.J.)2025
A Ready-to-Use Data Analysis Pipeline for BioID Experiments Using Data-Dependent or Data-Independent Acquisition Mass Spectrometry.
Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Proximity labeling combined with mass spectrometry (MS)-based proteomics has become an essential tool in interactomics. Proximity-dependent biotin identification (BioID) is a versatile method for identifying interacting and neighboring proteins within their native cellular environments. In BioID, the target (bait) protein is fused to a mutated BirA tag that biotinylates vicinal proteins (preys), which are subsequently purified and analyzed using LC-MS/MS. While data-dependent acquisition (DDA) has been the standard for MS-based proteomics, it suffers from bias toward abundant peptides, leading to missing data. In contrast, data-independent acquisition (DIA) improves the identification of low-abundant peptides, providing a more comprehensive proteomic analysis. This chapter outlines a data analysis workflow for BioID experiments in both DDA and DIA modes, using data from a study on the glucocorticoid receptor (GR) as an example. Data analysis was performed using MaxQuant, FragPipe, and DIA-NN, with downstream processing and statistical analysis conducted in R, incorporating SAINTq to enhance the reliability of bait-prey interaction identification.
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