在癌症中推断动态生长调节网络使用高通量转录基因数据
1Centre of Bioinformatics, Institute of Interdisciplinary Studies, University of Allahabad, Prayagraj, India.
Methods in molecular biology (Clifton, N.J.)
|October 6, 2023
概括
本研究介绍了使用RNA-Seq数据构建动态生长调节网络 (dGRNs) 的逐步协议. 它指导研究人员通过分析差异表达基因 (DEGs) 来洞察癌症进展等生物过程.
科学领域:
- 文字转录学 (Transcriptomics) 是一个学科.
- 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:
- 基因表达的变异调节了整个发育和疾病的生物过程.
- 动态生长调节网络 (dGRNs) 模拟癌症中特定阶段的基因表达变化.
- 使用差异表达基因 (DEG) 的基于相关性的方法对于dGRN重建是常见的.
研究的目的:
- 为从RNA-Seq数据中推断dGRNs提供一个全面的,逐步的协议.
- 引导早期的研究人员分析dGRN构建的转录组学数据.
- 详细说明将外部交互数据集成到dGRNs中的方法.
主要方法:
- RNA-Seq数据采集和预处理.
- 映射读取到一个参考基因组.
- 从DEGs构建基于关联的共同表达网络.
- 公众互动/监管数据的整合.
- 推断dGRNs的拓分析.
主要成果:
- 这里介绍了DEG分析和dGRN推断的详细方案.
- 该协议涵盖了从原始数据到网络分析的整个工作流.
- 包括将外部生物知识纳入dGRNs的方法.
结论:
- 该协议可以使用转录组学数据来简化dGRNs的推断.
- 它为对dGRN分析新手的研究人员提供了实用指南.
- 概述的方法使我们能够更深入地了解生物系统中的基因调节.
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