EPInformer:一个可扩展的深度学习框架,用于通过将促进器-增强器序列与多式表观基因组数据集成来预测基因表达
Jiecong Lin1,2, Ruibang Luo2, Luca Pinello1
1Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Department of Pathology, Harvard Medical School, Boston, Massachusetts 02129, USA.
bioRxiv : the preprint server for biology
|August 12, 2024
概括
EPInformer是一个新的深度学习框架,通过整合增强器-基因相互作用和表观基因组数据,准确地预测基因表达. 这种可扩展的工具改进了现有的方法,有助于理解转录调节.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 分子生物学分子生物学
背景情况:
- 转录调节控制细胞功能,并依赖于DNA,蛋白质和染色素之间的复杂相互作用.
- 了解调节元件 (CREs) 及其在基因表达中的动态作用至关重要,但具有挑战性.
- 现有的深度学习模型难以捕捉远程监管元素的影响,需要大量资源.
研究的目的:
- 开发一个可扩展的深度学习框架,EPInformer,用于准确的基因表达预测.
- 为了整合促进剂-增强剂相互作用,DNA序列,表观遗传学信号和染色质接触.
- 克服当前方法在预测远端元素的监管效应方面的局限性.
主要方法:
- 开发了EPInformer,一个可扩展的深度学习框架.
- 综合促进剂-增强剂相互作用,DNA序列,表观基因组数据和染色体联系信息.
- 进行了严格的染色体交叉验证,并与现有的基因表达预测模型进行了比较.
主要成果:
- 在染色体交叉验证中,EPInformer在现有模型中表现出优异的性能.
- 该模型准确预测了增强剂-基因相互作用,并通过CRISPR扰乱实验验验证.
- 在调控序列内确定了关键转录因子动机,增强了生物洞察力.
结论:
- EPInformer为基因表达预测提供了可扩展和准确的深度学习方法.
- 该框架有效地整合了各种基因组数据,以建模转录调节.
- EPInformer推进了对基因调节和增强器功能的研究,提供了一个开源解决方案.
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