TRANSAID:用于翻译站点预测的混合深度学习框架,集成生物特征评分
Yan Li1,2,3, Boran Wang4, Zhen Liu3
1Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Frontiers in bioinformatics
|February 4, 2026
概括
一个人工智能工具TRANSAID准确地预测了翻译的开始和结束地点. 这种深度学习框架增强了转录组注释,并有助于发现跨物种的新型蛋白质编码事件.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 准确预测翻译启动 (TIS) 和终结 (TTS) 位点对于理解蛋白质合成至关重要.
- 计算挑战,包括训练数据偏差和转录复杂性,限制了当前预测的准确性.
- 在转录组注释中,区分编码和非编码转录仍然是一个重要的障碍.
研究的目的:
- 开发一个新的深度学习框架,TRANSAID,用于同时准确地预测TIS和TTS站点.
- 通过处理全长的成绩单和减轻数据偏差来克服现有方法的局限性.
- 为了改善蛋白质编码和非编码转录的识别.
主要方法:
- 开发了TRANSAID,这是一个深度学习框架,具有层次结构,用于处理长的成绩单.
- 在人类转录组数据集上训练 TRANSAID,在基因层面严格分区以防止数据泄露.
- 在培训数据中包含了蛋白质编码 (NM) 和非编码 (NR) 转录,以及一个集成的生物评分系统.
主要成果:
- TRANSAID在预测TIS和TTS站点方面取得了高准确性,正确识别了73.61%的NR转录为非编码.
- 在编码序列的"完美ORF预测"中获得了94.94%的准确性,在"正确的非编码预测"中获得了82.00%的准确性.
- 证明了跨物种适用性,并通过质谱测量确定了以前未注释的蛋白质异型,其验证率为76.28%.
结论:
- TRANSAID提供了一种强大且易于访问的开源工具,用于增强转录组注释和蛋白质组发现.
- 该框架能够处理长长的成绩单并识别神秘的翻译事件,这为生物研究开辟了新的途径.
- TRANSAID的高精度和跨物种适用性使其成为科学界的宝贵资源.
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