大量RNA测序的简化协议:从数据提取到表达式分析
Abdullah Al Mohit1, Niher Ranjan Das2, Arushi Jain1
1Plant Molecular Biology Laboratory, Faculty of Life Sciences and Biotechnology, South Asian University, New Delhi, India.
Current protocols
|February 4, 2026
概括
该协议使用免费工具和云计算简化了RNA测序 (RNA-seq) 数据分析. 它使得基因表达研究能够被有限的硬件和技术专业知识的研究人员访问.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 下一代RNA测序 (RNA-seq) 是用于全基因组基因表达分析的强大工具.
- 传统的RNA-seq分析需要大量的计算资源和先进的生物信息技术技能.
- 对硬件和专业知识的有限访问阻碍了RNA序列的广泛采用.
研究的目的:
- 为RNA-seq数据分析提供一个简化,从开始到结束的协议.
- 为了使资源有限的研究人员能够进行全面的基因表达研究.
- 通过使用免费工具和云平台,使RNA-seq分析可复制和可访问.
主要方法:
- 使用免费的生物信息工具 (SRA工具包,FastQC,Trimmomatic,BWA/HISAT2,Samtools,Subread) 和基于云的平台 (谷歌 Colab).
- 涵盖整个工作流程:数据下载,质量控制,读取修剪,对齐,读取计数,规范化 (TPM) 和可视化.
- 集成Python和R用于差异基因表达分析 (pyDESeq2) 和功能丰富 (g:Profiler).
主要成果:
- 为RNA-seq数据分析建立了一个用户友好的,可重复的协议.
- 工作流成功地将原始测序数据处理成规范化表达式值和可视化.
- 差异基因表达和功能丰富分析得到了高效的执行.
结论:
- 该协议显著降低了RNA-seq数据分析的进入障碍.
- 它使得具有有限计算资源的研究人员能够进行先进的基因表达研究.
- 该方法提高了RNA-seq分析在研究环境中的可访问性和可负担性.
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