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Updated: Jan 24, 2026

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评估使用序列到功能建模的单细胞ATAC-seq图谱技术
Hannah Dickmänken1,2,3, Marta Wojno4, Lukas Mahieu1,2,3,5
1Laboratory of Computational Biology, VIB Center for AI & Computational Biology, Leuven, Belgium.
Nature communications
|January 22, 2026
概括
这项研究对单细胞染色体可访问性 (scATAC-seq) 平台进行基准测试,用于训练深度学习模型以了解基因调节. 来自各种平台的数据的整合使得成本效益高的大型地图集可以用于监管建模.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 分子生物学分子生物学
背景情况:
- 了解 cis 调节逻辑对于细胞身份至关重要.
- 单细胞染色体可访问性 (scATAC-seq) 图谱有助于训练顺序到功能 (S2F) 深度学习模型.
- 对scATAC-seq培训数据集的最佳标准和S2F模型的平台适用性尚不清楚.
研究的目的:
- 为S2F模型培训和转录因子 (TF) 足迹进行scATAC-seq平台的基准测试.
- 评估细胞数和碎片计数对培训数据质量的影响.
- 评估在不同数据源上训练的S2F模型的性能.
主要方法:
- 介绍了HyDrop v2,一个改进的自定义滴滴scATAC-seq方法.
- 对 scATAC-seq 平台进行S2F模型培训和TF足迹的比较.
- 在定制和商业scATAC-seq数据上训练的S2F模型的比较分析.
主要成果:
- 较低的碎片数量可以通过增加训练数据集中的细胞数来补偿.
- 在定制或商业 scATAC-seq 数据上训练的 S2F 模型在增强器预测,序列解释性和 TF 足迹方面表现相似.
- 来自不同scATAC-seq平台的数据集成有助于大规模,经济高效的地图集构建.
结论:
- scATAC-seq平台的选择影响S2F模型培训和TF足迹能力.
- 数据整合策略可以克服个别平台的局限性,以建立全面的监管地图.
- 这项工作为构建有效的scATAC-seq数据集提供了指导方针,用于基于深度学习的监管建模.
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