pyRBDome: a comprehensive computational platform for enhancing RNA-binding proteome data.

Autor:	Chu LC; https://ror.org/01nrxwf90 Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK.; https://ror.org/01nrxwf90 Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, UK., Christopoulou N; https://ror.org/01nrxwf90 Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK.; https://ror.org/01nrxwf90 Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, UK., McCaughan H; https://ror.org/01nrxwf90 Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK.; https://ror.org/01nrxwf90 Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, UK., Winterbourne S; https://ror.org/01nrxwf90 Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, UK., Cazzola D; https://ror.org/01nrxwf90 Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK., Wang S; https://ror.org/01nrxwf90 Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK.; https://ror.org/01nrxwf90 Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, UK., Litvin U; https://ror.org/01nrxwf90 Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK.; MRC-University of Glasgow Centre for Virus Research, Glasgow, UK., Brunon S; https://ror.org/01nrxwf90 Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK.; Institut de Biologie de l'Ecole Normale Supérieure (IBENS), Paris, France., Harker PJ; https://ror.org/01nrxwf90 Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK.; Cancer Research UK Cancer Biomarker Centre, University of Manchester, Manchester, UK., McNae I; https://ror.org/01nrxwf90 Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, UK., Granneman S; https://ror.org/01nrxwf90 Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK Sander.Granneman@ed.ac.uk.; https://ror.org/01nrxwf90 Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, UK.
Jazyk:	angličtina
Zdroj:	Life science alliance [Life Sci Alliance] 2024 Jul 30; Vol. 7 (10). Date of Electronic Publication: 2024 Jul 30 (Print Publication: 2024).
DOI:	10.26508/lsa.202402787
Abstrakt:	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 although 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. (© 2024 Chu et al.)
Databáze:	MEDLINE
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