推断的调节子与大肠杆菌中的调节器结合序列一致
Sizhe Qiu1, Xinlong Wan1, Yueshan Liang1
1Department of Bioengineering, University of California San Diego, La Jolla, CA, United States of America.
PLoS computational biology
|January 22, 2024
概括
机器学习模型证实,从RNA-seq数据推断出的细菌规律在促进体DNA序列中具有强大的生物化学基础. 促销器序列特征,包括动机和DNA形状,成功预测了调节活动,验证了用于发现转录调节网络的自上而下的推理方法.
科学领域:
- 微生物学 微生物学
- 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:
- 大肠杆菌中的转录调节网络 (TRN) 涉及调节剂和DNA序列之间的复杂相互作用.
- 调节因子通过实验性结合部位测量或从基因表达数据推断来确定.
- RNA-seq数据的独立组件分析 (ICA) 是推断细菌规律的强大工具.
研究的目的:
- 用促进子序列特征研究ICA推断的调节子结构的生物化学基础.
- 开发和验证机器学习模型,以基于促进器序列来预测大肠杆菌规律结构.
- 评估促进子序列特征在多大程度上解释ICA推断的规律组织.
主要方法:
- 机器学习模型的开发,以预测大肠杆菌的规律结构.
- 利用促进子序列特征,包括调节器图案,DNA形状和用于多分子结合的扩展图案.
- 使用AUROC (接收器操作特征曲线下的面积) 进行交叉验证,用于模型性能评估.
- 对最初模型未能识别出新的序列特征的 regulon 的分析.
主要成果:
- 机器学习模型成功预测了85%的ICA推断的大肠杆菌规律的规律结构 (AUROC >= 0.8).
- 仅仅促进者动机就预测了40%的监管活动.
- 额外的特征,如DNA形状和扩展的图案,改善了预测剩余的60%的规律.
- 对模型故障的调查揭示了新的监管器特定特征,提高了准确性.
结论:
- 根据ICA推断的规则的结构在很大程度上可以通过促进区域的监管绑定站点的强度来解释.
- 促进子序列特征为自上而下的规律推理提供了生化基础.
- 这项研究加强了ICA和机器学习在发现细菌转录性调节网络方面的实用性.
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