pyRBDome:用于增强 RNA 结合蛋白质组数据的综合计算平台。

IF 3.3 2区 生物学 Q1 BIOLOGY
Life Science Alliance Pub Date : 2024-07-30 Print Date: 2024-10-01 DOI:10.26508/lsa.202402787
Liang-Cui Chu, Niki Christopoulou, Hugh McCaughan, Sophie Winterbourne, Davide Cazzola, Shichao Wang, Ulad Litvin, Salomé Brunon, Patrick Jb Harker, Iain McNae, Sander Granneman
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引用次数: 0

摘要

高通量蛋白质组学方法为鉴定生物体内的 RNA 结合蛋白(RBPome)和 RNA 结合序列(RBDome)带来了革命性的变化。然而,由于验证结果的实验方法通常通量较低,与这些方法相关的噪声(包括假阳性)程度难以量化。为了解决这个问题,我们推出了 pyRBDome,这是一种用于增强 RNA 结合蛋白质组数据的硅学方法。它将实验结果与来自不同机器学习工具的 RNA 结合位点(RBS)预测结果进行比对,并整合可用的高分辨率结构数据。通过对 RBDome 数据进行统计评估,可以快速识别实验数据集中可能存在的真正 RNA 结合位点。pyRBDome 对人类 RBDome 数据集的分析与已知结构数据的比较显示,虽然 UV 交联氨基酸更有可能包含预测的 RBS,但它们在高分辨率结构中却很少与 RNA 结合。这种差异强调了结构数据作为基准的局限性,使 pyRBDome 成为提高 RBDome 数据集可信度的重要替代方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
pyRBDome: a comprehensive computational platform for enhancing RNA-binding proteome data.

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.

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来源期刊
Life Science Alliance
Life Science Alliance Agricultural and Biological Sciences-Plant Science
CiteScore
5.80
自引率
2.30%
发文量
241
审稿时长
10 weeks
期刊介绍: Life Science Alliance is a global, open-access, editorially independent, and peer-reviewed journal launched by an alliance of EMBO Press, Rockefeller University Press, and Cold Spring Harbor Laboratory Press. Life Science Alliance is committed to rapid, fair, and transparent publication of valuable research from across all areas in the life sciences.
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