|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/hands-on-rna-seq-analysis-crash-course-from-fastq-to-degs/
课程评论：没有评论
**Coursera 课程总结：从 FASTQ 到 DEGs 的 RNA-Seq 分析实战速成班** 本课程是学习 RNA 测序 (RNA-Seq) 数据分析的绝佳机会，尤其是在基因组学和转录组学飞速发展的今天。掌握 RNA-Seq 分析对于在学术界、生物信息学行业以及任何需要处理大规模生物数据的研究领域生存和发展至关重要。 **为什么学习 RNA-Seq？** * **海量数据处理:** 传统生物学方法已不足以应对现代生物学研究产生的大量数据，计算生物学和生物信息学工具发挥着关键作用。 * **基因表达研究:** RNA-Seq 是研究基因表达、发现差异表达基因 (DEGs) 最强大的技术之一。 * **揭示分子机制:** 帮助理解疾病的分子机制、药物反应以及所有生命体的调控通路。 **课程亮点：** 本课程提供一个完整的 RNA-Seq 分析实战速成班，从原始的 FASTQ 文件开始，逐步进行到 DEGs 和基因富集分析。您将通过命令行工具和 R 语言的结合，掌握完整的 RNA-Seq 分析流程。 **课程大纲（9个部分）：** 1. **课程与 Linux 入门:** 介绍课程概况以及 Linux 操作系统的基础知识。 2. **生物信息学基础 Linux:** 深入学习 Linux 在生物信息学分析中的常用命令和操作。 3. **RNA-Seq 基础:** 掌握 RNA-Seq 的基本原理、技术和应用。 4. **数据获取与预处理:** 学习如何获取 RNA-Seq 数据，并进行初步的数据质量检查和预处理。 5. **比对参考基因组:** 使用工具将测序 reads 比对到参考基因组。 6. **定量与标准化:** 对比对结果进行基因定量，并对数据进行标准化处理。 7. **R 和 RStudio 设置:** 学习 R 语言和 RStudio 环境的安装与使用。 8. **下游分析：DEGs 与 GSEA:** 使用 R 包进行差异表达基因分析 (DEG) 和基因集富集分析 (GSEA)。 9. **期末测验与毕业项目:** 通过测验和实际项目巩固所学知识，提升实战能力。 **主要工具介绍：** * **FastQC:** 用于测序数据质量控制。 * **BWA:** 用于reads比对。 * **Samtools 和 FeatureCounts:** 处理 BAM 文件及基因定量。 * **R 和 DESeq2:** 进行差异表达基因分析。 * **clusterProfiler:** 进行基因富集和通路分析。 **课程成果：** 完成本课程后，您将能够独立构建一套完整的 RNA-Seq 分析流程，并具备使用命令行工具和 R 语言处理实际数据的能力。这将极大地提升您的专业竞争力，并改变您对转录组学和生物数据分析的认知。 本课程是理论与实践相结合的典范，通过实际操作和项目，助您在不断发展的生物信息学领域开辟新的机遇。
This RNA-Seq Data Analysis course is going to be a game changer for you. In the modern era of genomics and transcriptomics, we are witnessing an explosion of RNA sequencing data. If you want to survive and grow in research, academia, or the bioinformatics industry, learning RNA-Seq is no longer optional - it's essential. Traditional biology is no longer sufficient to handle this scale of data. This is where computational biology and bioinformatics come into play, helping researchers make sense of massive datasets through efficient pipelines and analysis tools.RNA-Seq (RNA sequencing) is one of the most powerful technologies used to study gene expression and discover differentially expressed genes (DEGs). It helps uncover the molecular mechanisms behind diseases, responses to treatments, and regulatory pathways in all living organisms.Keeping this demand in view, we have brought you a complete hands-on crash course on RNA-Seq analysis that takes you from raw FASTQ files all the way to DEGs and gene enrichment results. This course will help you master the complete pipeline of RNA-Seq analysis using a blend of command-line tools and R programming.This course is divided into 9 comprehensive sections:(1) Course & Linux Introduction(2) Basic Linux for Bioinformatics(3) Foundations of RNA-Seq(4) Data Acquisition & Preprocessing(5) Mapping to the Reference Genome(6) Quantification & Normalization(7) R and RStudio Setup(8) Downstream Analysis: DEGs & GSEA(9) Final Quiz & Capstone ProjectThis course is a unique blend of theory and hands-on practice. First, you will learn the basics of RNA-Seq and Linux. Then, you will perform real-time preprocessing, alignment, quantification, and downstream analysis using publicly available RNA-Seq data. You'll also be completing assignments and a capstone project, giving you the practical experience needed to confidently handle real-world datasets.You'll work with some of the most widely used bioinformatics tools such as:FastQC for quality checkBWA for alignmentSamtools and FeatureCounts for BAM file handling and quantificationR and DESeq2 for DEG analysisclusterProfiler for enrichment and pathway analysisWe assure you that by the end of this course, you will be able to build your own RNA-Seq analysis pipeline from scratch using command-line tools and R. This will not only add a valuable skill to your CV but also transform the way you look at transcriptomics and biological data analysis.We hope this course will be worth your time and investment - and it will open up new opportunities for you in the ever-evolving field of bioinformatics.
