器官限定的基因调节网络在各种过程中提供了高精度的候选转录因子选择
Rajeev Ranjan1,2, Sonali Srijan1, Somaiah Balekuttira1
1Department of Horticulture and Landscape Architecture, Purdue University, West Lafayette, IN 47907.
概括
本研究引入了一种机器学习模型,用于自动分类植物器官基因表达,从而能够创建器官特异性基因调节网络 (GRNs). 这种方法识别了新的转录因子调节器,用于像种子脂质生物合成这样的特征.
科学领域:
- 植物分子生物学 植物分子生物学
- 生物信息学是一种生物信息学.
- 系统生物学 系统生物学
背景情况:
- 器官特异性基因调控网络 (GRNs) 对于了解植物发育至关重要.
- 用于GRN重建的基因表达数据集的手动策划是劳动密集型和耗时的.
研究的目的:
- 开发一种用于准确分类植物器官转录组的计算方法.
- 为了构建全基因组,受器官限制的GRNs (OD-GRNs) 对于*Arabidopsis thaliana*.
- 确定器官特异性特征的新型转录因子 (TF) 调节剂,特别是种子脂质生物合成.
主要方法:
- 训练了一个基于K-最近邻居的多类分类器来预测植物转录组的起源器官.
- 应用GRN推断来生成叶子,根,芽,花和种子的OD-GRNs.
- 经过实验验证,预测了种子脂质生物合成的TF调节器.
主要成果:
- 成功分类植物转录组的起源器官,使得可以创建器官特定数据集.
- 确定了对各种器官和生物过程具有影响力的TF,包括许多新型调节剂.
- 验证了MybS2,TGA4,SPL12,AGL18和DiV2作为种子脂质生物合成的调节剂,并阐明了MybS2的机制.
结论:
- 开发的分类模型自动创建器官特定基因表达数据集.
- 这种方法有效地重建了OD-GRNs,并揭示了新的监管因素.
- 该方法有可能在各种植物物种中发现特征特异性调节剂.
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