在谷歌云上的转录学和表观遗传数据集成学习模块
Nathan A Ruprecht1, Joshua D Kennedy1,2, Benu Bansal1
1Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States.
Briefings in bioinformatics
|August 5, 2024
概括
本研究介绍了一个基于谷歌云的学习模块,用于整合转录学和表观遗传学数据分析. 它为研究人员提供教程来进行多omics分析,有助于癌症研究和精密医学.
科学领域:
- 计算生物学和生物信息学
- 基因组学和表观基因组学
- 癌症研究和精准医学精准医学
背景情况:
- 多omics方法对于理解复杂的人类生物学至关重要,特别是在癌症研究和精密医学中.
- 高通量测序和计算资源的进步使多种分子数据的综合分析成为可能.
- 有效地分析大型生物数据集需要熟练掌握先进的工具,如谷歌云平台.
研究的目的:
- 开发一个指导式学习模块,用于整合转录学和表观遗传学数据分析协议.
- 为研究人员提供谷歌云上的综合管道,用于多omics数据分析.
- 促进个性化医学的生物标志物和治疗点的识别.
主要方法:
- 开发一个利用谷歌云平台基础设施的三个子模块学习模块.
- 教程涵盖RNA序列 (转录学) 和减少表现的二硫酸盐序列 (表观遗传学) 数据分析.
- 在带有 R 内核的 Vertex AI Jupyter 笔记本电脑上进行分析,使用来自 Gene Expression Omnibus 的乳腺癌病例研究.
主要成果:
- 该模块引导用户从数据收集和预处理到下游分析和结果可视化.
- 分析结果存储在谷歌云桶中,从本地资源中卸载计算负担.
- 为研究人员提供一个从头到尾的教程,为研究人员提供有限的多学科经验.
结论:
- 开发的学习模块使研究人员能够有效地整合转录学和表观遗传学数据分析.
- 该资源支持在癌症和其他复杂疾病中推进多学科研究.
- 该模块是NIGMS基于云的学习沙箱的一部分,促进基于云的生物信息学教育.
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