CCPA:基于云的自学模块,用于使用GO,KEGG和Reactome进行共识途径分析
Ha Nguyen1, Van-Dung Pham1, Hung Nguyen1
1Department of Computer Science and Software Engineering, Auburn University, AL 36849, USA.
Briefings in bioinformatics
|July 23, 2024
概括
本研究介绍了一种基于云的学习模块,用于共识途径分析,为研究人员简化复杂的生物数据解释. 它提供交互式教程,并支持多种物种,增强自我学习和比较分析能力.
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:
- 路径分析对于理解生物机制至关重要,但由于方法复杂性,编码要求和物种支持有限而受到阻碍.
- 现有的工具往往缺乏在不同实验和方法中比较结果的能力,这使得识别共识结果变得复杂.
- 由于这些局限性,生物医学研究人员在自我学习和有效执行途径分析方面面临着挑战.
研究的目的:
- 在NIGMS沙盒平台内开发一个基于云的交互式学习模块,用于共识途径分析.
- 提供一个自我学习资源,解决生物医学研究人员在执行和解释途径分析时遇到的困难.
- 为了使多个途径分析方法和实验结果的比较,以确定共识的结果.
主要方法:
- 开发了一个模块,包含五个Jupyter笔记本,涵盖数据处理,差异分析 (limma,t-test,edgeR,DESeq2),路径数据库处理 (GO,KEGG,Reactome) 和路径分析 (ORA,CAMERA,KS测试,Wilcoxon测试,FGSEA,GSA,SAFE,PADOG).
- 集成的云资源用于数据访问和分析,支持多个模型和非模型物种.
- 包括示例,源代码,解释和教学视频,提供全面的学习体验.
主要成果:
- 该模块方便表达式数据的处理,并使用四种不同的方法进行微分分析.
- 它允许使用八种不同的方法进行路径分析,并将结果整合到共识识别中.
- 该资源支持各种物种,包括人类,老鼠,果和斑马鱼,并可供公众使用.
结论:
- 开发的模块有效地解决了自我学习和执行共识途径分析的挑战.
- 它提供了一个有价值的,互动的,基于云的资源,用于培训学生和研究人员进行复杂的生物数据分析.
- 该模块促进有效的比较分析,并支持广泛的物种,提高其在生物研究中的实用性.
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