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基于基因表达的染色质可访问性的全基因组预测

Weiqiang Zhou¹, Ji Hongkai¹

¹Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA.

Wiley interdisciplinary reviews. Computational statistics

|October 11, 2024

概括

从基因表达数据中预测基因调节网络至关重要. 本综述涵盖了从转录组数据中预测染色质可访问性的方法,以扩展调节组目录并改进基因调节分析.

科学领域:

基因组学就是基因组学.
系统生物学系统生物学
生物信息学是一种生物信息学.

背景情况:

了解基因调节需要整合转录组 (基因表达) 和调节组 (调节元素活动) 数据.
公共数据库包含大量的转录组数据,但有限的调节组数据.
从基因表达数据中预测 regulome 信息是一种有前途的方法.

研究的目的:

通过基因表达数据,回顾最近在预测染色质可访问性的进展.
突出这些预测方法在基因组学研究中的应用.
讨论转录组和调节组数据的整合,以增强生物学洞察力.

主要方法:

从基因表达特征预测染色质可访问性的计算方法的审查.
分析利用预测规律数据的策略.
讨论整合预测和实验衍生的基因组数据的技术.

主要成果:

基因表达数据可以有效地用于预测开放色素区域,这是调控元素的关键指标.
预测方法可以扩展现有的规则类目录.
这些方法有助于改进调节组分析和整合不同的基因组数据集.
对基因调节的单细胞分析通过预测的染色质可访问性来增强.

结论:

从基因表达数据中预测染色质可访问性是一种强大的策略,可以克服实验性 regulome 数据的稀缺性.
这种方法显著提高了我们在批量和单细胞水平上研究基因调节的能力.
预测的规律组信息与转录组数据的整合为生物学发现提供了新的途径.

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