Day 5: Student project

Student Project: RNA-seq Analysis of Cell Apoptosis

In this project, you will independently analyze an RNA-seq dataset investigating cell apoptosis by applying the same workflow that we followed for Boström et al. (2017). The objective is to reinforce the concepts and tools introduced during the practical sessions while giving you the opportunity to perform a complete RNA-seq analysis from raw sequencing reads to biological interpretation.

Your analysis should include the following stages:

1. Data Pre-processing

• Access the raw sequencing data.
• Perform quality assessment using FastQC.
• Evaluate the FastQC reports and identify any quality concerns.
• Trim adapters and low-quality bases using fastp.
• Organize your output files in a logical directory structure.

2. Reference Preparation

• Obtain the appropriate reference transcriptome.
• Prepare the reference (if required).
• Build or use an existing Kallisto index.

3. Transcript Quantification

• Quantify transcript abundances using Kallisto.
• Organize all quantification results into a single output directory.
• Examine the generated output files and understand the reported metrics (counts, TPM, estimated counts, etc.).

4. Differential Gene Expression Analysis

Using R, perform the complete downstream analysis:

• Import Kallisto results using tximport.

• Create the DESeq2 dataset.

• Perform normalization and differential expression analysis using DESeq2.

• Generate the standard quality-control visualizations, including:

  • PCA plot
  • Sample distance heatmap
  • MA plot
  • Volcano plot

• Identify significantly differentially expressed genes.

5. Functional Enrichment Analysis

Perform functional interpretation of the differentially expressed genes using the approaches covered during the practical sessions.

This should include:

• Gene Ontology (GO) enrichment analysis
• KEGG pathway enrichment analysis (where applicable)
• Visualization of enriched biological processes and pathways

6. Biological Interpretation

Interpret your results in the context of cell apoptosis by addressing questions such as:

• Which genes are significantly up- or down-regulated?
• Which biological pathways are enriched?
• Are the enriched pathways consistent with the expected apoptosis response?
• How do your findings compare with the published study or current biological knowledge?

Using Nextflow (Optional)

Throughout the practical sessions, we performed each analysis step manually to understand the underlying concepts and tools involved in an RNA-seq workflow. Since you are now familiar with Nextflow and workflow management, you are encouraged to use an appropriate Nextflow pipeline (e.g., an nf-core RNA-seq workflow or another suitable pipeline) for your student project if you wish. Using a workflow management system is considered good bioinformatics practice, as it improves reproducibility, scalability, and ease of execution. Regardless of the approach you choose, you should understand the purpose of each analysis step and be able to interpret the resulting outputs.

Deliverables

By the end of the project, you should submit:

• A well-organized project directory containing all analysis outputs.

• An R script (or R Markdown notebook) documenting your downstream analyses.

• A short report (approximately 2–4 pages) describing:

  • Methods used
  • Key results
  • Figures generated
  • Functional enrichment results
  • Biological interpretation and conclusions

Throughout the project, you are encouraged to consult the software documentation, published literature, and online resources. The goal is not simply to reproduce results, but to develop the ability to independently perform and critically evaluate an RNA-seq analysis workflow from raw sequencing data to biological interpretation.