对于非编码基因变异效应预测的深度学习方法:目前的进展和未来的前景
Xiaoyu Wang1,2, Fuyi Li3, Yiwen Zhang4
1Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.
Briefings in bioinformatics
|September 14, 2024
概括
本综述探讨了使用批量和单细胞测序数据预测非编码变异效应的深度学习模型. 它强调了理解基因调节的进步和局限性,并为生物信息学家提供了一份指南.
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.
- 计算生物学 计算生物学
背景情况:
- 高通量测序推进了对基因调控过程的理解.
- 鉴定变异效应对于基因调节机制至关重要.
- 越来越多地研究非编码变体,超过所有变体的90%.
研究的目的:
- 使用批量和单细胞测序数据,提供对非编码变异模型的全面概述.
- 对变异效应进行基于模型的解释和下游任务的审查.
- 引导生物信息学界推进遗传变异效应研究.
主要方法:
- 对用于变量效应预测的深度学习方法的审查.
- 分析用于表观遗传分析的测序技术.
- 基于批量和单细胞测序数据的模型的检查.
主要成果:
- 深度学习模型为全球监管格局提供了新的见解.
- 为了辨别非编码变异效应,存在各种方法.
- 目前的方法在变异效应预测方面存在局限性.
结论:
- 在变异效应预测方面需要进一步的进步.
- 本综述为生物信息学界提供了一份实用指南.
- 了解非编码变体是解开基因调节的关键.
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