RAPDOR: Using Jensen-Shannon Distance for the computational analysis of complex proteomics datasets

Hemm, L.; Rabsch, D.; Rae, H.; Reimann, V.; Gerth, P.; Bartel, J.; Brenes, M.; Maass, S.; Becher, D.; Hess, W. R.; Backofen, R.

doi:10.1101/2024.09.30.615781

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bioRxiv · 10.1101/2024.09.30.615781

RAPDOR: Using Jensen-Shannon Distance for the computational analysis of complex proteomics datasets

Hemm, L.·Rabsch, D.·Rae, H.·Reimann, V.·Gerth, P.·Bartel, J.·Brenes, M.·Maass, S.·Becher, D.·Hess, W. R.·Backofen, R.

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Abstract

The computational analysis of large proteomics datasets, such as those from gradient profiling or spatially resolved proteomics, is often as crucial as the experimental design. We present RAPDOR, a tool for intuitive analyzing and visualizing such datasets, based on the Jensen-Shannon distance and subsequent analysis of similarities between replicates, applied to three datasets. First, we examined the in-gradient distribution profiles of protein complexes with or without RNase treatment (GradR) to identify the set of RNA-binding proteins (RBPs) in the cyanobacterium Synechocystis sp. PCC 6803. RBPs play pivotal regulatory and structural roles; although numerous RBPs have been identified, the complete set is unknown for any species. RAPDOR identified 80 potential RBPs, including ribosomal proteins, likely RNA-modifying enzymes, and several proteins not previously associated with RNA binding. High-ranking putative RBPs, such as the universal stress protein Sll1388, or the translation inhibitor LrtA/RaiA, were predicted by RAPDOR but not the TriPepSVM algorithm, indicating uncharacterized RBP domains. These data are available online at https://synecho-rapdor.biologie.uni-freiburg.de, providing a comprehensive resource for RNase-sensitive protein complexes in cyanobacteria. We then show by reanalyzing existing datasets, that RAPDOR is effective in examining the intracellular redistribution of proteins under stress conditions. RAPDOR is a generic, non-parametric tool for the intuitive and versatile analysis of highly complex data sets such as the study of protein distributions using fractionation protocols.

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Hemm, L., Rabsch, D., Rae, H., Reimann, V., Gerth, P., Bartel, J., Brenes, M., Maass, S., Becher, D., Hess, W. R., Backofen, R.. 2024-10-01. RAPDOR: Using Jensen-Shannon Distance for the computational analysis of complex proteomics datasets. https://doi.org/10.1101/2024.09.30.615781

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