规则从少数样本的信息中推断出签署的监管关系,使用离散和概率约束
Marine Louarn1,2, Guillaume Collet1, Ève Barré1
1Univ Rennes, CNRS, Inria, IRISA - UMR 6074, Rennes, France.
PLoS computational biology
|January 22, 2024
概括
我们开发了Regulus,这是一种从有限的数据中推断转录调节电路的新方法. 它集成了TF结合,基因表达和区域可访问性,以预测基因激活或抑制,改善生物发现.
科学领域:
- 基因组学就是基因组学.
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
背景情况:
- 转录调节通过与DNA结合的转录因子 (TF) 控制基因表达.
- 目前推断调节电路的方法需要大量的样本,并且很难预测激活/抑制.
- 在人体研究中,有限的样本可用性需要更有效的推断方法.
研究的目的:
- 从更少的样本中开发一种推断转录调节电路的方法.
- 整合多样化的生物数据,以进行可靠的TF基因关系预测.
- 准确地将推断出来的关系定义为基因激活或抑制.
主要方法:
- Regulus集成了TF结合,基因表达和监管区域可访问性数据.
- 通过将数据聚合成模式并存储在资源描述框架 (RDF) 终点中来减少数据的复杂性.
- 基于生物学的概率约束和SPARQL查询过TF-区域-基因关系,并将其视为激活或抑制.
主要成果:
- 规则计算TF基因关系,提供签名的预测 (激活/抑制).
- 该方法的结果与公共数据库一致.
- 适用于已知和新型转录调节器识别的生物数据.
结论:
- 规则使得特定于背景的转录电路推断成为可能,特别是在人体样本少的环境中.
- 该方法的模式分类和概率推理确定了强大的监管关系.
- 规则增强了对基因调节机制的发现.
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