Sujith Taridalu, Ayyappa Kumar Sista Kameshwar, Masako Suzuki
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引用次数: 0
摘要
RNA测序(RNA-seq)已成为生物医学研究中评估基因表达谱的一项重要技术。然而,对于没有广泛生物信息学专业知识的学生和研究人员来说,RNA-seq数据分析的编码复杂性仍然是一个重大障碍。我们介绍了教育RNA-Seq分析工具(ERSAtool),一个全面的R/Shiny界面,提供了完整RNA-Seq分析工作流程的直观图形可视化。该应用程序建立在已建立的Bioconductor封装上,并在分析中坚持高标准,同时显着减少了进行复杂转录组学分析所需的技术专业知识。ERSAtool支持各种输入格式,如原始计数矩阵和STAR对齐输出。它通过与国际公共存储库Gene Expression Omnibus (GEO)直接集成来生成样本信息元数据。该应用程序指导用户通过规范化,数据可视化,差异表达分析,并使用基因本体和基因集富集分析功能解释。所有结果都可以汇编成全面的、可下载的报告,以加强可重复性和知识共享。该设计的目标是支持教育用途的功能,使其特别有助于在本科到研究生水平的生物信息学课程中教授转录组学,并增强对高级转录组学分析的访问,潜在地加速各个生物学领域的发现。ERSAtool可以在https://github.com/SuzukiLabTAMU/ERSAtool上免费获得。
ERSAtool: A User-Friendly R/Shiny Comprehensive Transcriptomic Analysis Interface Suitable for Education
RNA sequencing (RNA-seq) has become an essential technology for assessing gene expression profiles in biomedical research. However, the coding complexity of RNA-seq data analysis remains a significant barrier for students and researchers without extensive bioinformatics expertise. We present the Educational RNA-Seq Analysis tool (ERSAtool), a comprehensive R/Shiny interface that provides an intuitive graphical visualization of the complete RNA-seq analysis workflow. The application is built on established Bioconductor packages and upholds high standards in analyses while significantly reducing the technical expertise required to conduct sophisticated transcriptomic analyses. ERSAtool supports various input formats, such as raw count matrices and STAR alignment outputs. It generates sample information metadata through direct integration with the international public repository, Gene Expression Omnibus (GEO). The application guides users through normalization, data visualization, differential expression analysis, and functional interpretation using Gene Ontology and Gene Set Enrichment Analyses. All results can be compiled into comprehensive, downloadable reports that enhance reproducibility and knowledge sharing. The design targets features that support educational use, making it especially helpful for teaching transcriptomics in undergraduate to graduate-level bioinformatics courses and enhancing access to advanced transcriptomic analysis, potentially accelerating discoveries across various biological fields. ERSAtool is available for free at https://github.com/SuzukiLabTAMU/ERSAtool.
期刊介绍:
Genes to Cells provides an international forum for the publication of papers describing important aspects of molecular and cellular biology. The journal aims to present papers that provide conceptual advance in the relevant field. Particular emphasis will be placed on work aimed at understanding the basic mechanisms underlying biological events.