Zebrafish Bulk RNA-seq Analysis Pipeline

Course Description: Bulk RNA-seq Analysis Pipeline

This teaching resource provides a comprehensive guide to performing bulk RNA-seq analysis especially for zebrafish. The pipeline is designed for researchers and students looking to understand and implement RNA-seq data processing and analysis using a step-by-step approach. The resource includes detailed instructions, code snippets, visual aids, and explanations to ensure a clear understanding of each stage of the pipeline, from raw data acquisition to differential expression analysis.

Structure of the Pipeline:

1. Introduction to Bulk RNA-seq
   • Overview of RNA-seq technology and its applications.
   • Differences between bulk RNA-seq and single-cell RNA-seq.
   • Key considerations and challenges in RNA-seq analysis.
2. Data Acquisition and Quality Control
   • Images: Flowchart of data acquisition steps.
   • Code: Scripts for downloading raw RNA-seq data from public repositories (e.g., SRA, GEO).
   • Text: Explanation of quality control processes, including tools like FastQC and MultiQC.
3. Read Alignment and Quantification
   • Images: Diagram of alignment process.
   • Code: Example commands for using STAR or HISAT2 for read alignment.
   • Text: Discussion of different aligners and their advantages/disadvantages.
4. Transcript Assembly and Quantification
   • Images: Flowchart illustrating transcript assembly.
   • Code: Example pipeline for transcript quantification using tools like StringTie or featureCounts.
   • Text: Considerations for choosing transcript assembly methods.
5. Differential Expression Analysis
   • Images: Example of a volcano plot for differential expression results.
   • Code: R scripts for performing differential expression analysis with DESeq2 or edgeR.
   • Text: Guidelines for interpreting results, including p-value adjustments and biological relevance.
6. Visualization and Interpretation
   • Images: Examples of heatmaps, PCA plots, and gene ontology enrichment results.
   • Code: R scripts for visualizing RNA-seq data.
   • Text: How to present and interpret the results in a biological context.
7. Final Thoughts and Best Practices
   • Text: Summary of best practices in bulk RNA-seq analysis.
   • Images: Checklist of steps for ensuring robust analysis.

Additional Resources:

• Links to external resources, tools, and databases.
• References to relevant literature and tutorials for deeper understanding.

By following this pipeline, users will be equipped with the knowledge and tools necessary to conduct robust bulk RNA-seq analyses, interpret the results, and apply them to their research projects.