Evaluation of novel computational methods to identify RNA-binding protein footprints from structural data.

IF 4.2 3区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
RNA Pub Date : 2025-05-21 DOI:10.1261/rna.080215.124
Orel Mizrahi, Meredith Corley, Ori Feldman, Thorben Fröhlking, Lei Sun, Alison Ziesel, Maciej Antczak, Mattia Bernetti, Shaimae I Elhajjajy, Wenze Huang, Grady G Nguyen, Samuel S Park, Raul I Perez Martell, Luke Trinity, Kui Xu, Tomasz Zok, Giovanni Bussi, Hosna Jabbari, Yaron Orenstein, Sharon Aviran, Michelle M Meyer, Gene Yeo
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引用次数: 0

Abstract

RNA-binding proteins (RBP) play diverse roles in mRNA processing and function. However, from thousands of RBPs encoded in the human genome, a detailed molecular understanding of their interactions with RNA is available only for a small fraction. In most cases, our knowledge of the combination of RNA sequence and structure required for specific RBP-binding is insufficient for accurately predicting binding sites transcriptome-wide. In this context, the rapidly expanding collection of transcriptomic datasets that map distinct, yet intertwined post-transcriptional marks, such as RNA structure and RBP binding, presents an opportunity for integrative analysis to better characterize RBP binding. A grand challenge faced by our community is that relatively little information on the secondary structure context within and near RBP binding sites has been gleaned from integrating such datasets, partially due to lack of suitable computational methods. To engage scientists from diverse backgrounds in addressing this gap, the RNA Society organized the RBP Footprint Grand Challenge in 2021, an international community effort to develop new methods or leverage existing ones for predicting RBP binding sites through analysis of a growing volume of sequence, structure, and binding data and to experimentally validate select predictions. Here, we report the initiative, analyses and methods developed by the participants, validation results, and five new in vivo binding datasets generated for validation. We hope our work will inspire additional innovation in computational methods, further utilization of available data resources, and future endeavors to engage the community in collaborating towards closing other critical data-analysis gaps.

评估从结构数据中识别rna结合蛋白足迹的新型计算方法。
rna结合蛋白(RBP)在mRNA的加工和功能中发挥着多种作用。然而,从人类基因组中编码的数千个rbp中,对它们与RNA相互作用的详细分子理解仅占一小部分。在大多数情况下,我们对特定rbp结合所需的RNA序列和结构组合的了解不足以准确预测转录组范围内的结合位点。在这种背景下,转录组数据集的迅速扩大,这些转录组数据集绘制了不同的,但相互交织的转录后标记,如RNA结构和RBP结合,为更好地表征RBP结合的综合分析提供了机会。我们面临的一个重大挑战是,通过整合这些数据集收集到的RBP结合位点内部和附近的二级结构背景信息相对较少,部分原因是缺乏合适的计算方法。为了吸引来自不同背景的科学家来解决这一差距,RNA学会在2021年组织了RBP足迹大挑战,这是一项国际社会的努力,旨在通过分析越来越多的序列、结构和结合数据,开发新方法或利用现有方法来预测RBP结合位点,并通过实验验证选择的预测。在这里,我们报告了参与者的倡议、分析和开发的方法、验证结果以及为验证而生成的五个新的体内结合数据集。我们希望我们的工作将激发计算方法的进一步创新,进一步利用现有数据资源,并在未来努力使社区参与合作,以缩小其他关键的数据分析差距。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
RNA
RNA 生物-生化与分子生物学
CiteScore
8.30
自引率
2.20%
发文量
101
审稿时长
2.6 months
期刊介绍: RNA is a monthly journal which provides rapid publication of significant original research in all areas of RNA structure and function in eukaryotic, prokaryotic, and viral systems. It covers a broad range of subjects in RNA research, including: structural analysis by biochemical or biophysical means; mRNA structure, function and biogenesis; alternative processing: cis-acting elements and trans-acting factors; ribosome structure and function; translational control; RNA catalysis; tRNA structure, function, biogenesis and identity; RNA editing; rRNA structure, function and biogenesis; RNA transport and localization; regulatory RNAs; large and small RNP structure, function and biogenesis; viral RNA metabolism; RNA stability and turnover; in vitro evolution; and RNA chemistry.
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