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RAPDOR: Using Jensen-Shannon Distance for the computational analysis of complex proteomics datasets
Luisa Hemm1, Dominik Rabsch2, Halie Rae Ropp1
1Genetics and Experimental Bioinformatics, of Biology, University of Freiburg, Freiburg, Germany.
Nature Communications
|September 26, 2025
Summary
We developed RAPDOR, a novel tool for analyzing complex proteomics data, aiding in the identification of RNA-binding proteins (RBPs) and understanding protein localization. This method enhances the discovery of proteins involved in crucial cellular functions.
Area of Science:
- Proteomics
- Computational Biology
- Molecular Biology
Background:
- Analyzing large proteomics datasets from gradient profiling and spatial proteomics is essential for biological discovery.
- Identifying RNA-binding proteins (RBPs) is critical due to their regulatory and structural roles, yet the complete set remains unknown for most species.
- Existing computational tools may not fully capture the complexity of protein interactions and localization in large datasets.
Purpose of the Study:
- To introduce RAPDOR, a user-friendly tool for analyzing and visualizing complex proteomics datasets.
- To apply RAPDOR for the identification of RNA-binding proteins (RBPs) in Synechocystis 6803 using gradient profiling.
- To demonstrate RAPDOR's utility in analyzing spatial proteomics and protein redistribution upon stimulation.
Main Methods:
- Development of RAPDOR, a computational tool utilizing Jensen-Shannon distance and similarity analysis.
- Application of gradient profiling with and without RNase treatment (GradR) to analyze protein complex distribution.
- Reanalysis of existing spatial proteomics datasets to showcase RAPDOR's versatility.
Main Results:
- RAPDOR identified 165 potential RBPs in Synechocystis 6803, including novel candidates and known ribosomal proteins.
- Experimental validation confirmed several high-ranking putative RBPs predicted by RAPDOR, suggesting uncharacterized RNA-binding domains.
- RAPDOR effectively analyzed protein redistribution in existing datasets upon growth factor stimulation, demonstrating its broad applicability.
Conclusions:
- RAPDOR is an effective, non-parametric tool for the intuitive analysis and visualization of complex proteomics data.
- The study identified a significant number of potential RBPs, expanding the known RBP repertoire in cyanobacteria.
- RAPDOR provides a valuable resource for researchers studying RNA-protein interactions and spatial proteomics across various biological systems.
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