在循环RNA上解码蛋白质结合景观,具有基本分辨率变压器模型.
Hehe Wu1, Xiaojian Liu1, Yi Fang1
1Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, And Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.
Computers in biology and medicine
|February 25, 2024
概括
在单核酸分辨率下,CircSite预测圆形RNA (circRNAs) 上的RNA结合蛋白 (RBP) 结合点. 这种深度学习工具准确地识别了circRNA转录中的结合位置和关键核酸.
科学领域:
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 循环RNAs (circRNAs) 是基因表达的关键调节者.
- 目前用于预测circRNAs上的RNA结合蛋白 (RBP) 结合位点的方法缺乏精度和分辨率.
- 目前的工具经常分析circRNA片段,未能精确地确定完整的转录中的特定结合点.
研究的目的:
- 开发一种新的计算工具CircSite,用于精确预测circRNAs上的RBP结合点.
- 为了实现单核酸分辨率,识别RBP与circRNAs的相互作用.
- 确定关键核酸对于RBP与circRNA转录物结合至关重要.
主要方法:
- 这是一种混合深度学习方法,结合了卷积神经网络 (CNN) 和变压器模型.
- 利用CNN用于本地特征提取,利用变压器进行全球序列表示.
- 在37个精选的circRNA-蛋白相互作用数据集上进行培训和基准测试.
主要成果:
- 在circRNA转录中,CircSite在单核酸分辨率上准确地预测RBP结合部位.
- 该工具成功地识别了有助于RBP结合的关键核酸.
- 预测的关键次序与已知的RBP结合动机保持一致,验证了模型的有效性.
结论:
- 在理解RBP与circRNAs相互作用方面,CircSite提供了显著的进步.
- 该工具为研究人员提供了一个用户友好的网络服务器,用于预测 circRNAs 上的 RBP 结合位点.
- CircSite促进了对circRNA调节机制和功能的更深入的了解.
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