SNPeBoT:用于预测转录因子等位基因特定结合的工具
1Department of Medicine and Life Sciences, SBI-GRIB, Universitat Pompeu Fabra, 08003, Barcelona, Catalonia, Spain.
BMC bioinformatics
|March 11, 2025
概括
我们开发了SNPeBoT,这是一种深度学习模型,可以准确预测单核酸多态 (SNP) 如何影响转录因子结合. 这种工具可以更好地发现致病的非编码突变.
科学领域:
- 基因组学就是基因组学.
- 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:
- 非编码DNA中的突变可以破坏转录因子的结合,可能导致疾病.
- 了解转录因子结合模式和遗传变异的影响至关重要,但有限.
- 深度学习提供了一个有前途的方法来建模这些复杂的相互作用.
研究的目的:
- 开发一种深度学习模型,用于预测单核酸多态 (SNP) 对转录因子结合的影响.
- 提高预测等位基特定结合 (ASB) 事件的准确性和回忆.
- 为识别与疾病相关的非编码突变提供一种工具.
主要方法:
- 训练了一个卷积神经网络 (CNN),使用来自ChIP-seq的基因特异结合 (ASB) 数据和来自蛋白结合微阵列 (PBM) 的DNA结合域数据.
- 衍生出的E-score资料用于与ASB事件相关的参考和替代DNA序列.
- 利用了来自113个转录因子的18211个E-score配置文件,分为培训,验证和测试数据集.
主要成果:
- 与现有的基准预测平台相比,开发的CNN模型显示出更高的准确性和ASB回忆力.
- 该模型有效地预测SNP是否导致转录因子结合的增加,损失或无变化.
- 对SNP效应预测的既定工具进行了性能验证.
结论:
- 引入了SNPeBoT (单核酸多态化对转录因子结合的影响),可作为独立工具和Web服务器.
- 增强的预测准确度和ASB事件的恢复可以帮助发现疾病相关的非编码突变.
- SNPeBoT为遗传研究和疾病关联研究提供了宝贵的资源.
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