DNA 形状特征提高了对CRISPR/Cas9活动的预测
Dhvani Sandip Vora1, Sakshi Manoj Bhandari2, Durai Sundar3
1Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi 110016, India.
Methods (San Diego, Calif.)
|April 19, 2024
概括
将DNA形状和表观遗传特征添加到机器学习模型中,可以显著改善CRISPR/Cas9基因组编辑非目标效应的预测. 这提高了识别意外Cas9活动的准确性.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 分子生物学分子生物学
背景情况:
- CRISPR/Cas9基因组编辑正在彻底改变生物和医学研究.
- 非目标效应和对Cas9机制的不完全理解限制了它的应用.
- 预测Cas9活动的机器学习模型需要有效的特征工程.
研究的目的:
- 评估将表观遗传和DNA形状特征纳入基于序列的数据集以预测Cas9活动的影响.
- 评估CRISPR/Cas9非目标效应的机器学习预测器的性能改进.
主要方法:
- 利用神经网络在具有不同特征组的数据集上进行训练:仅序列,序列和表观遗传特征,以及序列,表观遗传和DNA形状特征.
- 使用Akaike和贝叶斯信息标准系统评估绩效增加.
- 采用 permutation 和 LIME 方法来评估个别特征的重要性.
主要成果:
- 包括DNA形状特征在内显著提高了预测性能.
- 像不匹配和核酸组成这样的序列特征仍然很重要.
- DNA-RNA混合扭曲参数 (打开,拉伸) 也影响了模型结果.
结论:
- 表观遗传和DNA形状特征对于提高Cas9目标外预测模型的准确性至关重要.
- 一个全面的特征集,包括序列,表观遗传和DNA形状数据,导致更可靠的CRISPR/Cas9活动预测.
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