Learning Bioinformatics

Project Title: RNA-Seq Data Analysis for Differential Gene Expression in Cancer Cells

Objective:

The aim of this project is to analyze RNA-Seq data from cancerous and normal cells to identify differentially expressed genes (DEGs) associated with cancer. This analysis could help uncover biomarkers or potential therapeutic targets in cancer research.

Project Workflow:

1. Problem Definition and Research Question

   • Goal: Identify genes that are upregulated or downregulated in cancer cells compared to normal cells.

   • Research Question: Which genes are significantly different in expression levels between cancerous and normal tissues, and what biological pathways do they affect?

2. Data Collection

   • Dataset: RNA-Seq dataset from a public repository such as The Cancer Genome Atlas (TCGA) or GEO (Gene Expression Omnibus).

   • Data Format: FASTQ or already processed datasets in counts format.

     • Example dataset: GEO Accession GSE62944 (RNA-Seq data from TCGA).

3. Preprocessing

   • Tools:

     • If raw FASTQ files are used, use FASTQC for quality control.

     • Align the reads to a reference genome using tools like HISAT2 or STAR.

     • Convert aligned reads to counts per gene using featureCounts or HTSeq.

   • Deliverable: A clean counts matrix with genes as rows and samples (cancer and normal) as columns.

4. Normalization and Quality Control

   • Step: Normalize the data to adjust for sequencing depth and technical bias using methods like TMM (Trimmed Mean of M-values) in edgeR or DESeq2.

   • Tools:

     • R packages: edgeR, DESeq2.

     • Visualize the quality of normalization using PCA plots, boxplots, or heatmaps.

   • Deliverable: A normalized expression matrix ready for differential expression analysis.

5. Differential Gene Expression Analysis

   • Tools: Use DESeq2 or edgeR in R to perform differential expression analysis.

   • Statistical Methods:

     • Perform hypothesis testing to identify significantly differentially expressed genes (DEGs).

     • Apply false discovery rate (FDR) correction to control for multiple comparisons.

   • Output: A list of DEGs with log-fold changes and adjusted p-values.

6. Functional Enrichment Analysis

   • Objective: Determine the biological significance of the DEGs by performing pathway analysis and gene ontology (GO) enrichment.

   • Tools:

     • clusterProfiler or DAVID for GO enrichment and pathway analysis.

     • Identify pathways affected by the up- or downregulated genes (e.g., cancer-related pathways like PI3K-AKT signaling).

   • Deliverable: Enriched pathways and functional categories related to cancer biology.

7. Visualization of Results

   • Visualizations:

     • Volcano plot to visualize DEGs.

     • Heatmap of top DEGs across all samples.

     • Barplots or network diagrams showing enriched GO terms or pathways.

   • Tools: Use ggplot2, pheatmap, and RColorBrewer in R for visualization.

   • Deliverable: Clear, publication-ready plots showing significant genes and pathways.

8. Biological Interpretation and Conclusions

   • Interpretation: Relate the DEGs and enriched pathways to known cancer biology.

   • Hypotheses: Propose hypotheses on the potential role of identified DEGs as biomarkers or drug targets.

9. Report Writing

   • Content:

     • Introduction to RNA-Seq and cancer research.

     • Methodology of data processing and analysis.

     • Results: DEGs, functional enrichment, and biological interpretation.

     • Conclusion: Summary of findings and future directions for research.

   • Deliverable: A final project report, including an abstract, methods, results, and discussion.

Skills and Tools You'll Apply:

• Bioinformatics Tools: FASTQC, HISAT2, featureCounts, DESeq2.

• Programming Languages: R and Python.

• Statistical Analysis: Differential expression, FDR correction.

• Visualization: ggplot2, heatmaps, PCA.

• Biological Databases: Gene Ontology (GO), KEGG for pathway analysis.

Extension Ideas:

• Apply machine learning models to classify cancer and normal samples based on gene expression data.

• Extend to other types of ‘omics’ data, like proteomics or metabolomics.

• Compare different types of cancers (e.g., breast cancer vs. lung cancer).