|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/bioinformatics-rna-seqdifferential-expression-in-bash-r/
课程评论：没有评论
**生物信息学：RNA-Seq/Bash & R 差异表达分析** 本课程旨在教授学员如何利用 Next Generation Sequencing (NGS) 技术中的 RNA 测序 (RNA-Seq) 来解析基因表达模式。课程将引导学员全面了解并实际操作 RNA-Seq 分析过程，涵盖从数据下载到结果解读的每一个关键步骤。 **课程内容概述：** * **Bash 脚本基础与工具应用：** * 学习使用 Bash 脚本在 Google Colab 中运行重要的 RNA-Seq 分析工具。 * 理解适用于 HPC (High-Performance Computing) 服务器的 Bash 脚本编写规范。 * **R 语言基础及数据分析：** * 使用 RStudio 进行 R 语言基础编码学习。 * 进行 RNA-seq 数据的质量控制，包括使用 FastQC 和 MultiQC，以及在 R 中的自定义分析。 * 执行差异表达分析，例如使用 DESeq2，以识别不同生长阶段细胞之间的基因表达变化。 * 进行基因本体论 (Gene Ontology) 分析，利用 fgsea 和 clusterprofiler 了解通路的上调和下调情况。 * 将差异表达结果映射到 KEGG 通路，利用 Pathview 创建带有注解的 KEGG 图，以便深入研究特定通路。 **学习目标：** 完成本课程后，学员将能够： * 从 FTP 站点下载公开的 RNA-Seq 数据到 HPC 集群。 * 获取进行基因组比对所需的原始文件。 * 使用 Salmon 工具进行基因组比对。 * 使用 FastQC 和 MultiQC 分析 RNA-seq 数据的质量，并在 R 中进行自定义分析。 * 利用 DESeq2 进行差异表达分析。 * 通过 fgsea 和 clusterprofiler 进行基因本体论分析。 * 使用 Pathview 创建 KEGG 通路图。 **课程特色：** * **实践导向：** 课程以实践为主，包含 19 个讲座，详细指导从数据下载到结果解释的整个流程。 * **真实数据集与挑战：** 项目使用真实世界的数据，包含代码报错、非模式生物等实际问题，培养学员解决问题的能力。 * **适合人群：** 对下一代测序技术、基因与医学研究感兴趣的任何人，以及 RNA-Seq 初学者。 本课程将帮助您掌握分析细胞转录组变化的技能，理解基因在细胞生长中的重要作用，并为深入研究癌症标志物、遗传性疾病等领域奠定基础。
Ever wonder which technologies allow researchers to discover new markers of cancer or to get a greater understanding of genetic diseases? Or even just what genes are important for cellular growth? This is usually carried out using an application of Next Generation Sequencing Technology called RNA sequencing. RNA sequencing allows you to interpret the gene expression pattern of cells. Throughout this course, you will be equipped with the tools and knowledge to not only understand but perform RNA sequencing using bash scripting and R. Discover how the transcriptome of cell changes throughout its growth cycle. To ensure that you have a full understanding of how to perform RNA sequencing yourself every step of the process will be explained! You will first learn how to use bash scripting in Google Colab to understand how to run the important RNA-sequencing tools. I will then explain what a bash script that you may upload to an HPC server would look like. We will then take the data outputted from the pipeline and move into Rstudio where you will learn how to code with the basics of R! Here you will also learn how to quality control your counts, perform differential expression analysis and perform gene ontology analysis. As an added bonus I will also show you how to map differential expression results onto Kegg pathways! Once you've completed this course you will know how to:Download publically available data from a FTP site directly to a HPC cluster. Obtain the needed raw files for genome alignment. Perform genome alignment using a tool called Salmon.Analyse the quality of your RNA-seq data using FastQC and MultiQC, while also doing a custom analysis in R.Carry out a differential expression using DESeq2 to find out what changes between a cell on day 4 Vs day 7 of growth. Carry out gene ontology analysis to understand what pathways are up and down-regulated using fgsea and clusterprofiler.Use Pathview to create annotated KEGG maps that can be used to look at specific pathways in more detail.Practical BasedThe course has one initial lecture explaining some of the basics of sequencing and what RNA sequencing can be used for. Then it's straight into the practical! Throughout the 19 lectures, you are guided step by step through the process from downloading the data to how you could potentially interpret the data at the final stages. Unlike most courses, the process is not simplistic. The project has real-world issues, such as dealing with code errors, using a non-model organism and how you can get around them with some initiative! This course is made for anyone that has an interest in Next-Generation Sequencing and the technologies currently being used to make breakthroughs in genetic and medical research! The course is also meant for beginners in RNA-seq to learn the general process and complete a full walkthrough that is applicable to their own data!
