DeepECC:一个深度学习框架,用于全基因组识别和分析人类癌症的eccDNAs
Changcheng Wang1, Yisen Xu1, Rufeng Li1
1Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710061, China.
Nucleic acids research
|March 5, 2026
概括
深度学习工具DeepECC识别出染色体外圆形DNA (eccDNA) 以及它们在癌症中的调节作用. 它分析了序列断点,以改善eccDNA发现和功能洞察力.
科学领域:
- 基因组学就是基因组学.
- 计算生物学 计算生物学
- 癌症研究 癌症研究
背景情况:
- 超染色体圆形DNA (eccDNA) 在真核生物中普遍存在,影响基因调节和癌症.
- 目前用于eccDNA检测的实验方法受到成本和完整性的限制.
- 需要计算方法来解决eccDNA异质性和生物发生.
研究的目的:
- 开发一个深度学习框架,DeepECC,用于准确高效的eccDNA识别.
- 分析eccDNA序列特征和基因组分布,以了解它们的调节功能.
- 为可视化和查询与癌症相关的eccDNA提供一个工具.
主要方法:
- 采用了两阶段的深度学习策略,在eccDNA断点周围建模序列上下文.
- DeepECC在多个物种数据集 (人类,老鼠,) 上受过训练和验证.
- 对预测的eccDNA进行了全基因组扫描和模式分析.
主要成果:
- 深度ECC有效地捕获保存的断点特征,有利于富含GC和转录活跃的区域.
- 人类癌症eccDNA显示分布不均,在增强剂,eQTL和CTCF位点附近丰富.
- 动机分析表明eccDNA参与了核糖体活动,转化调节和DNA损伤反应.
- 预测的eccDNA被集成到UCSC基因组浏览器中进行可视化和功能解释.
结论:
- DeepECC为系统的eccDNA发现提供了一个可泛化的计算框架.
- 在癌症的进展中,eccDNAs发挥着重要的调节作用.
- DeepECC工具通过方便查询和可视化与癌症相关的eccDNAs来促进实验研究.
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