用大型体外和体外数据预测SNP对TF-DNA结合的影响的模型的比较分析
Dongmei Han1, Yurun Li1, Linxiao Wang1
1CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China.
Briefings in bioinformatics
|March 22, 2024
概括
这项研究系统地评估了计算模型,预测了从DNA变异中转录因子 (TF) 结合变化的变化. 机器学习和深度学习模型在体外与体外的准确性各不相同,这凸显了对特定环境的模型选择的需要.
科学领域:
- 基因组学和生物信息学
- 计算生物学 计算生物学
- 分子遗传学 分子遗传学
背景情况:
- 影响复杂特征的非编码基因变异可以改变转录因子 (TF) -DNA结合基因.
- 存在许多计算模型来预测这些变异效应,但它们的预测准确性缺乏系统的评估.
研究的目的:
- 系统地评估和比较14个计算模型的性能,以预测非编码变体对TF结合的影响.
- 在体外 (SNP-SELEX) 和体内 (异位基特结合,ASB) 设置中评估模型准确性.
主要方法:
- 评估了14个模型,包括位置重量矩阵 (PWM),支向量机,普通最小平方和深度神经网络 (DNN).
- 使用大规模的体外 (SNP-SELEX) 和体内 (ASB) TF 约束数据进行模型性能评估.
- 基于架构 (PWM,ML,DNN) 和训练数据 (体外,ChIP-seq) 的分类模型.
主要成果:
- 预测SNP效应的模型准确性在体外数据中明显高于体外数据.
- 对于体外预测,基于kmer/gkm的机器学习方法 (deltaSVM_HT-SELEX,QBiC-Pred) 的表现最好.
- 对于体内ASB预测,基于DNN的多任务模型 (DeepSEA,Sei,Enformer) 显示出卓越的性能.
- tRap (基于PWM) 在两个设置中都表现良好;预测准确性因TF类而异 (例如,基本的氨酸拉链与C2H2指).
结论:
- 预测TF结合变异效应的模型性能在体外和体内条件之间存在很大差异.
- 为非编码变体优先级选择合适的计算模型取决于实验环境 (体外与体外).
- 非序列因素 (例如,cis-regulatory元素类型,TF表达) 对于改善体内预测性能至关重要.
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