基于序列的染色体活动建模和监管影响预测的基因变异在农场动物使用深度学习的基因变异
Dat Thanh Nguyen1, Tim Martin Knutsen2, Simen R Sandve1
1Centre for Integrative Genetics, Faculty of Biosciences, Norwegian University of Life Sciences, 1432 Ås, Norway.
NAR genomics and bioinformatics
|November 5, 2025
概括
我们开发了一个深度学习框架,以预测农场动物中非编码基因组变异的监管影响. 这种方法增强了对畜牧业和水产养殖业遗传特征的理解和预测.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 动物育种 动物育种
背景情况:
- 非编码的基因组变异调节特征,但它们在养殖动物中的功能尚不清楚.
- 有限的基因组资源和计算工具阻碍了对牲畜和水产养殖中的这些变异的研究.
研究的目的:
- 开发一种深度学习框架,用于预测牛,,猪和大西洋鱼中非编码变异的监管影响.
- 利用功能性基因组数据进行准确的变异影响预测.
主要方法:
- 使用了功能性基因组数据 (检测可转移酶可访问的染色体,DNase I过敏部位,染色体免疫沉测序).
- 训练并优化特定物种的深度神经网络.
- 进行了动图分析和in silico和突变发生,用于模型解释.
主要成果:
- 为每个物种实现了强大的序列建模准确度.
- 模型捕捉了监管语法,并提供了功能影响解释.
- 衍生功能评分预测了表达量特征位点 (eQTL) 的因果变体和改进的基因组预测.
结论:
- 深度学习模型可以准确地预测农场动物中非编码变体的监管影响.
- 这一框架有助于优先考虑因果变异,并加强畜牧业和水产养殖业的基因组预测.
- 序列到功能模型为动物育种和遗传改进提供了变革性的潜力.
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