里博微生物:微生物的集成翻译原子地图
Yingshun Zhou1, Jinjing Luo1, Xiaoqiang Lang2,3,4,5,6
1Department of Pathogenic Biology, School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, P. R. China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|October 13, 2025
概括
RiboMicrobe 是一个用于 prokaryotic ribosome profiling (Ribo-seq) 数据的新数据库,提供用于翻译分析和小开放阅读框架 (sORF) 预测的工具,以推进微生物研究.
科学领域:
- 微生物基因组学和转录基因组学
- 计算生物学和生物信息学
- 分子生物学和遗传学 分子生物学和遗传学
背景情况:
- 核糖体概况 (Ribo-seq) 对于研究翻译动态和识别小开放阅读框架 (sORF) 至关重要.
- 现有的转原体数据库缺乏对 prokaryotic Ribo-seq 数据和 sORF 分析的全面覆盖.
- 需要专门的资源来促进转化在原核细胞的分析.
研究的目的:
- 开发一个全面的数据库,RiboMicrobe,用于 Ribo-seq 数据分析在原生微生物.
- 引入基于深度学习的新型模型 (sORFPredRibo和sORFPred) 来准确预测sORF.
- 为探索,预测和比较翻译数据提供综合生物信息学工具.
主要方法:
- 编制一个大规模的数据库,整合了原核生物和病毒的Ribo-seq,RNA-seq和蛋白质组数据集.
- 开发基于变压器的深度学习模型,用于sORF预测.
- 实现一个带有可视化,预测和比较分析工具的网络界面.
主要成果:
- 现在,RiboMicrobe数据库包含了891个Ribo-seq,369个RNA-seq和62个蛋白质组数据集,来自36个原核生物和2个病毒.
- 两个新的sORF预测模型,sORFPredRibo和sORFPred,已经开发和集成.
- 提供了一套生物信息学工具,用于数据探索,sORF预测和功能调查.
结论:
- RiboMicrobe提供了一个有价值的,中心化资源,用于 prokaryotic 翻译学研究.
- 集成的深度学习模型提高了 prokaryotes 中 sORF 识别的准确性.
- 预计这项资源将大大推进微生物翻译研究和sORF注释领域.
相关概念视频
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