pyRBDome: A comprehensive computational platform for enhancing and interpreting RNA-binding proteome data
pyRBDome: A comprehensive computational platform for enhancing and interpreting RNA-binding proteome data
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DOI：
10.1101/2023.12.08.570608
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发表时间：
2023-12
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通讯作者：
Liang-Cui Chu;Niki Christopoulou;Hugh McCaughan;Sophie Winterbourne;Davide Cazzola;Shichao Wang;Ulad Litvin;Salomé Brunon;Patrick J.B. Harker;Iain McNae;S. Granneman
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作者：
Liang-Cui Chu;Niki Christopoulou;Hugh McCaughan;Sophie Winterbourne;Davide Cazzola;Shichao Wang;Ulad Litvin;Salomé Brunon;Patrick J.B. Harker;Iain McNae;S. Granneman
高通量蛋白质组学方法已经彻底改变了跨生物体的RNA结合蛋白（RBPome）和RNA结合序列（RBDome）的鉴定。然而，与这些方法相关的噪声（包括假阳性）的程度难以量化，因为用于验证结果的实验方法通常吞吐量较低。为了解决这个问题，我们引入了pyRBDome，这是一个用于增强RNA结合蛋白质组数据的管道。它将实验结果与来自不同机器学习工具的RNA结合位点（RBS）预测相匹配，并在可用时整合高分辨率结构数据。它对RBDome数据的统计评估可以快速识别实验数据集中可能的真正RNA结合物。此外，通过利用pyRBDome结果，我们通过训练新的集成机器学习模型提高了RBS检测的灵敏度和特异性。与已知的结构数据相比，人类RBDome数据集的pyRBDome分析显示，虽然UV交联的氨基酸更可能包含预测的RBS，但它们很少以高分辨率结构结合RNA。这种差异强调了结构数据作为基准的局限性，将pyRBDome定位为增加RBDome数据集信心的有价值的替代方案。
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.