翻译签名分数:数据驱动的方法来评估积极翻译的证据
Devika Menon1,2, Owen Rackham1,3, Sonia Chothani4,5
1Program in Cardiovascular and Metabolic Disorders, Duke-National University of Singapore, Singapore, Singapore.
Methods in molecular biology (Clifton, N.J.)
|November 16, 2025
概括
从小型开放式读取框架 (smORF) 中识别微蛋白是具有挑战性的. 翻译签名方法使用聚合的核糖体分析数据来稳定地识别真正翻译的smORF,帮助基因组注释.
科学领域:
- 基因组学就是基因组学.
- 蛋白质组学是指蛋白质组学.
- 分子生物学分子生物学
背景情况:
- 小开放式读取框架 (smORF) 编码具有潜在生理作用的微蛋白,但很难识别,并且经常被排除在基因组注释之外.
- 核糖体分析 (Ribo-seq) 已经揭示了成千上万的新型ORF,但由于数据的变化,将真正的smORF与噪声区分开来是具有挑战性的.
研究的目的:
- 描述一种强大的方法,即翻译签名方法,用于识别和优先考虑翻译的smORF.
- 为分析大规模的核糖体分析数据提供一个框架,以发现新型微蛋白.
主要方法:
- 利用聚合的核糖体分析 (Ribo-seq) 数据,以增强对个人ORF中翻译的可视化.
- 开发了翻译签名分数,将P站点纳入框架,统一性和掉落指标.
- 使用注释的蛋白质编码ORF作为参考,以建立主动翻译的预期得分范围.
主要成果:
- 翻译签名方法可以更清晰地可视化和量化smORF中的翻译证据.
- 优先考虑的新型ORF显示翻译签名得分,表明正在进行翻译.
- 建立了一个网络资源,用于访问使用此工作流识别的默认smORFs集.
结论:
- 翻译签名方法为识别由smORFs编码的微蛋白提供了一个强大的框架.
- 这种方法通过可靠地检测新的翻译区域来提高基因组注释的准确性.
- 高质量的Ribo-seq数据的可用性增加将进一步加强这种微蛋白发现方法.
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