基准测试和构建DNA结合亲和模型,使用等位基因特异性和等位基因异性转录因子结合数据
Xiaoting Li1, Lucas A N Melo1, Harmen J Bussemaker2,3
1Department of Biological Sciences, Columbia University, New York, NY, 10027, USA.
Genome biology
|November 1, 2024
概括
我们开发了从DNA序列中预测异位基因特异性转录因子结合 (ASB) 的方法,改进了对非编码变异的分析. 我们的方法提高了基因组数据预测功能影响的准确性.
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.
- 分子生物学分子生物学
背景情况:
- 转录因子 (TFs) 表现出序列特定的DNA结合.
- 在异构位上可以观察到TFs的基因特异结合 (ASB) 差异.
- 目前的基因组规模测定如ChIP-seq在检测ASB方面存在局限性,原因是读取覆盖范围和变体表示.
研究的目的:
- 开发和基准测试方法,从序列中预测TF结合性基失衡.
- 量化评估预测TF结合中的等位基差异的可靠性.
- 从体内数据中方便从新推断高质量的TF结合模型.
主要方法:
- 建议使用基于过分散的二项式分布的概率函数对序列至亲和度模型进行基准测试的方法.
- 推出了PyProBound,这是一个可扩展的生物物理解释机器学习框架的重新实现,用于新的模型推断.
- 在使用ChIP-seq数据训练模型时,纳入了DNA碎片化速率的测试特异偏差.
主要成果:
- 开发了一种方法,在不需要单个变异显著性的情况下,汇总全基因组对等位基因偏好的证据.
- 在PyProBound中,PyProBound使用基因特异性ChIP-seq计数来促进新型基因的发现.
- 考虑到DNA碎片化偏差,改善了在ChIP-seq数据上训练的TF结合模型.
结论:
- 为预测非编码变体的功能影响提供了新的策略.
- 能够更准确地评估基因层面的TF结合变异.
- 从序列数据提升了TF结合的预测.
关键词:
对等位基特异性的结合.机器学习是生物物理上可以解释的.在CTCF,EBF1,PU.1/SPI1中使用.ChIP-seq,ChIP-exo,CUT&Tag 这两个字符是什么意思?基因表达调节 基因表达调节动机发现 动机发现没有编码的变体.统计建模 统计建模转录因子 转录因子更多相关视频
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