整合机器学习和功能基因组学,研究跨物种基因调控进化
Erin N Gilbertson1, Steven K Reilly2
1Department of Genetics, Yale School of Medicine, New Haven, CT, USA.
Current opinion in genetics & development
|December 20, 2025
概括
进化生物学寻求物种差异的遗传根源. 新的方法结合了比较基因组学和机器学习,以破译cis调节元素的变化如何推动基因表达进化.
科学领域:
- 进化生物学是进化的生物学.
- 基因组学就是基因组学.
- 基因调节 基因调节
背景情况:
- 了解物种的表型差异需要确定遗传基础.
- 哺乳动物基因组揭示了许多序列差异,主要是在非编码区域.
- 控制基因表达的Cis-regulatory元素 (CREs) 是复杂且难以研究的.
研究的目的:
- 审查研究基因调节进化的近期进展.
- 突出实验和计算策略,以了解跨物种的CRE功能.
主要方法:
- 比较基因组学以确定序列差异.
- 跨物种的功能分析,以评估基因表达和CRE活动.
- 高通量扰动测试用于测试CRE功能.
- 机器学习可以从DNA序列中预测CRE活动.
主要成果:
- 进步允许对基因表达和CRE功能的跨物种差异进行分类.
- 机器学习模型正在出现,仅从序列数据预测CRE活动.
结论:
- 整合实验和计算方法是解读基因调控进化的关键.
- 新的策略正在提高我们将CREs中的遗传变化与物种间的功能结果联系起来的能力.
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