多omics路径工作流程 (MOPAW):在癌症基因组学云上的一个自动化的多omics工作流程
Trinh Nguyen1, Xiaopeng Bian1, David Roberson2
1The Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA.
Cancer informatics
|June 21, 2023
概括
这项研究引入了一种自动化的多omics路径工作流,简化了研究人员的复杂数据分析. 该工具可以识别不同的生物通路,帮助制定有针对性的治疗策略.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 多学科数据分析对于理解复杂的生物系统至关重要,通常需要专门的编程技能和高性能计算.
- 现有的基因组路径分析工具对缺乏编码专业知识的研究人员构成重大挑战.
- 多主题数据准备和分析的复杂性阻碍了大数据研究的更广泛的可访问性.
研究的目的:
- 开发一个可访问的,自动化工作流程,用于多omics路径分析.
- 为了简化各种omics数据类型的数据准备,维度减少和路径分析的过程.
- 为研究人员提供一个用户友好的界面,以便在特定的子组中识别不同的生物途径.
主要方法:
- 引入了一种自动化的多omics路径工作流程,用于多变量单样样本基因组分析 (MOGSA) 的图形用户界面 (GUI).
- 工作流整合了用于数据准备,维度减少和MOGSA路径分析的工具,跨越拷贝编号更改,转录组学,蛋白质组学和光蛋白质组学数据.
- 另外一个工作流便于从癌症基因组图谱 (TCGA) 和临床蛋白质瘤分析联盟 (CPTAC) 下载和预处理数据.
主要成果:
- 工作流为用户定义的子组生成不同的路径信息,通过热图,图表和表可视化.
- 在特定的样本组中识别过度激活或失活的通路.
- 为潜在的治疗向提供可操作的见解.
结论:
- 多omics路径工作流不需要编码经验,使得更广泛的研究社区可以访问高级分析.
- 允许用户分析自己的数据或预处理TCGA和CPTAC的公共数据集.
- 促进发现不同的途径,这些途径对于有效的治疗策略至关重要.
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