pyRBDome: A comprehensive computational platform for enhancing and interpreting RNA-binding proteome data

Chu, L.-C.; Christopoulou, N.; McCaughan, H.; Winterbourne, S.; Cazzola, D.; Wang, S.; Litvin, U.; Brunon, S.; Harker, P. J. B.; McNae, I.; Granneman, S.

doi:10.1101/2023.12.08.570608

bioRxiv · 10.1101/2023.12.08.570608

pyRBDome: A comprehensive computational platform for enhancing and interpreting RNA-binding proteome data

Chu, L.-C.·Christopoulou, N.·McCaughan, H.·Winterbourne, S.·Cazzola, D.·Wang, S.·Litvin, U.·Brunon, S.·Harker, P. J. B.·McNae, I.·Granneman, S.

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Abstract

High-throughput proteomics approaches have revolutionised the identification of RNA-binding proteins (RBPome) and RNA-binding sequences (RBDome) across organisms. Yet the extent of noise, including false-positives, associated with these methodologies, is difficult to quantify as experimental approaches for validating the results are generally low throughput. To address this, we introduce pyRBDome, a pipeline for enhancing RNA-binding proteome data in silico. It aligns the experimental results with RNA-binding site (RBS) predictions from distinct machine learning tools and integrates high-resolution structural data when available. Its statistical evaluation of RBDome data enables quick identification of likely genuine RNA-binders in experimental datasets. Furthermore, by leveraging the pyRBDome results, we have enhanced the sensitivity and specificity of RBS detection through training new ensemble machine learning models. pyRBDome analysis of a human RBDome dataset, compared with known structural data, revealed that while UV cross-linked amino acids were more likely to contain predicted RBSs, they infrequently bind RNA in high-resolution structures. This discrepancy underscores the limitations of structural data as benchmarks, positioning pyRBDome as a valuable alternative for increasing confidence in RBDome datasets.

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Chu, L.-C., Christopoulou, N., McCaughan, H., Winterbourne, S., Cazzola, D., Wang, S., Litvin, U., Brunon, S., Harker, P. J. B., McNae, I., Granneman, S.. 2023-12-08. pyRBDome: A comprehensive computational platform for enhancing and interpreting RNA-binding proteome data. https://doi.org/10.1101/2023.12.08.570608

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