可重复处理的TCGA监管网络的重复处理
Viola Fanfani1, Katherine H Shutta1,2, Panagiotis Mandros1
1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
bioRxiv : the preprint server for biology
|November 22, 2024
概括
我们开发了tcga-data-nf,这是一个Nextflow工作流程,用于从The Cancer Genome Atlas (TCGA) 数据中进行可再生癌症调节网络分析. 该工具集成了多omics数据来推断基因和蛋白质相互作用,帮助发现疾病机制.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 技术进步使得疾病分子基础的深入探索成为可能.
- 生物网络对于分析奥米克数据和建模基因/蛋白质相互作用至关重要.
- 像癌症基因组图谱 (TCGA) 这样的大型项目为计算方法开发提供了丰富的数据.
研究的目的:
- 开发一个连贯和可重复使用的工作流程,对癌症监管网络进行端到端分析.
- 为了使监管网络可从TCGA多omics数据中得出可复制的推断.
主要方法:
- 开发了tcga-data-nf,一个Nextflow工作流.
- 整合来自TCGA的多omics数据 (RNA-seq,甲基化).
- 使用netZoo软件工具进行监管网络推断.
- 利用NetworkDataCompanion R包进行数据管理.
主要成果:
- 工作流允许通过单一命令从数千个TCGA样本中对监管网络进行可重现的推断.
- 在研究结肠癌亚型之间的表观遗传差异方面有明显的应用.
- 为10种最常见的癌症类型提供预先生成的网络.
结论:
- tcga-data-nf为癌症监管网络分析提供了一个完整,灵活和可扩展的框架.
- 工作流确保可重现的推断和分析,解决现有软件工具的差距.
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