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Day 5: Student project

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Abdoallah Sharaf edited this page Jul 14, 2026 · 4 revisions

Student Project: RNA-seq Analysis of UV-Induced Cell Apoptosis

Project Overview

For your final project, you will independently analyze an RNA-seq dataset generated during the VTK 2024 practical course. The objective is to apply the complete RNA-seq workflow that you learned throughout this practical course to investigate the transcriptional response of HeLa Kyoto wild-type (WT) cells following UV-induced DNA damage.

Unlike the guided exercises based on Boström et al. (2017), this project is designed to give you the opportunity to perform the analysis independently. You are encouraged to consult software documentation, online resources, and the course materials whenever necessary.

The aim is not simply to obtain a list of differentially expressed genes, but to demonstrate that you understand each stage of an RNA-seq analysis workflow—from raw sequencing reads to biological interpretation.

Experimental Design

Biological Question

How does UV-induced DNA damage alter gene expression in HeLa cells over time, and which biological pathways are associated with the cellular response and apoptosis?

Experimental Setup

At the beginning of each VTK practical week, 5 × 10⁶ HeLa Kyoto WT cells were seeded into a Petri dish containing 5 mL growth medium and cultured for 24 hours.

Cells were then exposed to 20 mJ UV irradiation to induce DNA damage.

Samples were collected at:

  • 0 hours (Control)

  • 1 hour

  • 3 hours

Each condition contains three independent biological replicates, giving a total of 9 RNA samples.

After harvesting, cells were lysed directly on the culture dish using:

  • 300 μL Milli-Q water

  • followed by an additional 100 μL wash

The lysates were combined, immediately snap-frozen in liquid nitrogen, and stored at −80°C before RNA extraction and sequencing.

Experimental Design

Time Point | Treatment | Biological Replicates -- | -- | -- 0 h | Control | 3 1 h | UV-treated | 3 3 h | UV-treated | 3

Sequencing Data

The RNA-seq data are paired-end Illumina FASTQ files.

Example filenames:

T0_MAZP0_1.fastq.gz
T0_MAZP0_2.fastq.gz

T1_MW1h_1.fastq.gz
T1_MW1h_2.fastq.gz


T3_SP3_1.fastq.gz
T3_SP3_2.fastq.gz

Each sample contains two FASTQ files representing:

  • Read 1 (R1)

  • Read 2 (R2)

Project Workflow

You should perform the complete RNA-seq workflow covered during the practical sessions.

1. Data Pre-processing

  • Organize your project directory.

  • Inspect the raw FASTQ files.

  • Perform quality control using FastQC.

  • Assess sequencing quality.

  • Remove adapters and low-quality bases using fastp.

  • Verify that trimming improved sequence quality.

2. Reference Preparation

  • Obtain the appropriate reference transcriptome.

  • Build (or use) the Kallisto index.

  • Understand why transcriptome indexing is required.

3. Transcript Quantification

Use Kallisto to quantify transcript abundance for every sample.

After quantification:

  • Inspect the output files.

  • Understand TPM and estimated counts.

  • Organize all outputs into a single directory.

4. Differential Expression Analysis

Using R and DESeq2:

  • Import transcript abundances using tximport.

  • Convert transcript counts to gene counts.

  • Create the DESeq2 dataset.

  • Perform normalization.

  • Identify differentially expressed genes.

Generate standard visualizations including:

  • PCA plot

  • Sample distance heatmap

  • MA plot

  • Volcano plot

  • Heatmap of selected genes

5. Functional Enrichment Analysis

Using the methods covered during the practical:

  • Gene Ontology (GO) enrichment

  • KEGG pathway enrichment

  • Visualization of enriched pathways

  • Interpretation of enriched biological processes

6. Biological Interpretation

Interpret your findings.

Consider questions such as:

  • Which genes respond to UV treatment?

  • Which pathways are activated or repressed?

  • Are apoptosis-related pathways enriched?

  • How do responses differ between 1 h and 3 h?

  • Do your findings agree with current biological knowledge?

Using Nextflow (Optional)

During the practical sessions, each analysis step was performed manually to help you understand the underlying methods and software.

Since you are now familiar with Nextflow and workflow management systems, you are encouraged to perform your project using an appropriate Nextflow RNA-seq pipeline (for example, an nf-core workflow) if you wish. Workflow management systems improve reproducibility, scalability, and automation, and are widely used in modern bioinformatics research.

Whether you perform the analysis manually or with Nextflow, you should understand every stage of the workflow and be able to explain and interpret your results.

Deliverables

Submit the following:

  • Organized project directory

  • Analysis scripts (R scripts or R Markdown)

  • Generated figures

  • Differential expression results

  • Functional enrichment results

In addition, prepare a short report (approximately 2–4 pages) including:

  1. Introduction

  2. Experimental design

  3. Methods

  4. Results

  5. Functional enrichment analysis

  6. Biological interpretation

  7. Conclusions

Assessment

Your project will be assessed on:

  • Correct application of the RNA-seq workflow

  • Organization and reproducibility

  • Quality of figures and analyses

  • Appropriate use of statistical methods

  • Biological interpretation of the results

  • Quality and clarity of the final report

The emphasis is on demonstrating your understanding of the complete RNA-seq analysis workflow rather than simply reproducing expected results.

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