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Updated: Jul 24, 2025

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Chromatin Immunoprecipitation ChIP using Drosophila tissue
Published on: March 23, 2012
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在Drosophila中染色质可访问性的进化和突变强度
Samuel Khodursky1,2, Eric B Zheng1,2, Nicolas Svetec1
1Laboratory of Evolutionary Genetics and Genomics, The Rockefeller University, New York, NY 10065, USA.
bioRxiv : the preprint server for biology
|July 10, 2023
概括
深度神经网络揭示了DNA序列决定了染色质的可访问性,这种染色质在物种之间得到保护,并且具有突变强度. 尽管强壮,但染色质的可访问性是可塑的,在选择下演变,保留的图案影响调节.
科学领域:
- 基因组学就是基因组学.
- 进化生物学 进化生物学
- 计算生物学 计算生物学
背景情况:
- 监管要素的演变推动了生物多样性.
- 了解基于序列的调节和进化是复杂的.
- 深度学习为研究这些因素提供了新的方法.
研究的目的:
- 使用深度神经网络调查染色质可访问性的序列决定因素.
- 评估调节序列的保存和进化动态.
- 探索染色质可访问性的强度和可塑性.
主要方法:
- 训练有素的混合卷积注意力神经网络,从DNA序列中预测ATAC-seq峰值.
- 利用深度学习模型进行跨物种分析和in silico mutagenesis.
- 在强选择和弱突变 (SSWM) 下进行了形进化实验.
主要成果:
- 染色质可访问性的序列决定因素在Drosophila物种中高度保留.
- 具有特定物种可访问性的区域显示出祖先的进化潜力.
- 染色体可访问性是突变强大的,但在选择下高度可塑性.
- 确定了预测染色质可访问性的已知监管动机.
结论:
- 深度神经网络是监管基因组学和进化的强大工具.
- 染色体可访问性表现出显著的保存和强度.
- 尽管强壮,但可访问性是进化的动态,并受到组织特异性选择的影响.
关键词:
ATAC-seqq 的使用情况.这种植物是Drosophila.这种植物是Drosophila melanogaster.这种植物是Drosophila simulans.这种植物是Drosophila yakuba.染色质可访问性 染色质可访问性深度学习是一种深度学习.在的突变发生过程中.坚固性 坚固性 坚固性更多相关视频
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