使用合成调节基因组学的深度学习模型的代改进
André M Ribeiro-Dos-Santos1, Matthew T Maurano1,2,3
1Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA.
bioRxiv : the preprint server for biology
|February 20, 2025
概括
生成型深度学习模型准确地预测表观遗传轨迹,但与新的DNA序列作斗争. 将这些模型与合成数据微调,可以提高它们对变体分类的概括性.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 表观遗传学 在表观遗传学中,表观遗传学是指表观遗传学.
背景情况:
- 生成型深度学习模型可以从基因组序列中重建表观遗传数据.
- 与参考基因组相异的序列的预测准确性,如疾病变异,仍然不清楚.
研究的目的:
- 评估Enformer模型在工程DNA序列上具有删除,反转和重新排列的预测能力.
- 用合成构造的实验数据来微调Enformer,以提高其通用性.
主要方法:
- 利用Enformer模型预测工程序列上的DNA可访问性.
- 应用合成调节基因组学来创建和描述数十个修改后的DNase I过敏位点 (DHS).
- 微调的Enformer与生成的数据以及对其他表观遗传轨道的评估性能.
主要成果:
- Enformer显示了预测和测量DNA可访问性之间的良好相关性.
- 与更简单的修改相比,对于改变DHS顺序或方向的序列,模型性能下降.
- 微调Enformer显著减少了预测误差,并保持了其他轨道的性能.
结论:
- 当前的深度学习模型表现出新型序列的局限性,缺乏训练数据中存在的特征.
- 用合成数据进行代微调可以提高模型的概括性.
- 这种方法可以帮助从人口研究中对监管变异的功能分类.
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