深度学习模型结合了DNA序列之外的内源因素,提高了基编辑结果的预测准确性
Tanglong Yuan1, Leilei Wu2, Shiyan Li3
1Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, China.
Cell discovery
|February 20, 2024
概括
与集成序列相比,氨酸和氨酸基编辑器 (ABEs和CBEs) 在内源部位显示不同的结果. 表观遗传因素在体内显著影响基因编辑效率.
科学领域:
- 分子生物学分子生物学
- 遗传学 遗传学 是一个
- 生物信息学是一种生物信息学.
背景情况:
- 腺基编辑器 (ABE) 和细胞基编辑器 (CBE) 提供精确的单核酸编辑,没有双链断裂.
- 影响体内基因编辑结果的基因组特征在很大程度上仍未被描述.
- 以前的研究主要使用集成图书馆,可能忽视了内源因素.
研究的目的:
- 研究内源性基因组特征对ABE和CBE结果的影响.
- 为了比较基因编辑效率在内源性网站与基因组集成序列.
- 开发一个基准编辑结果的预测模型,考虑内源因素.
主要方法:
- 在5012个内源基因组位点和11868个基因组集成目标序列中评估了ABE和CBE编辑结果.
- 分析了序列特征和内源因素 (表观遗传修饰,转录活性).
- 开发了一个深度学习算法 (BE_Endo) 来预测基础编辑结果.
主要成果:
- 在内源部位和集成序列之间,ABE和CBE的编辑结果显著不同.
- 在内源部位的基编辑效率受到表观遗传修饰和转录活性的影响.
- BE_Endo算法在预测基础编辑结果方面取得了很高的准确性.
结论:
- 内生因素在确定基准编辑效率和体内结果方面发挥着至关重要的作用.
- BE_Endo提供了一个强大的工具来预测基础编辑的成功.
- 这些发现支持基础编辑技术的发展,用于研究和基因治疗.
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