RNA-Seq Data Analysis using R and Bioconductor

This internship offers an in-depth, hands-on introduction to the processing and interpretation of RNA-Seq data using R and the Bioconductor ecosystem. Participants will learn how to handle raw RNA sequencing data, normalize gene expression values, and identify differentially expressed genes (DEGs). The internship emphasizes real-world analysis using statistical pipelines, visualizations, and functional annotation tools.

🎯 Learning Objectives

By the end of this internship, participants will:

• Understand the RNA-Seq workflow from raw data to biological interpretation

• Import, preprocess, and normalize RNA-Seq count data

• Perform differential gene expression analysis using DESeq2 and edgeR

• Visualize expression patterns using heatmaps, volcano plots, and PCA

• Conduct functional enrichment analysis using GO and KEGG databases

• Use Bioconductor packages effectively for reproducible analysis

🧩 Program Structure

Introduction to RNA-Seq and R Environment

• What is RNA-Seq and its biological applications

• Overview of RNA-Seq data formats

• Setting up R and RStudio for bioinformatics

• Introduction to Bioconductor

Importing and Exploring RNA-Seq Data

• Count matrix vs raw FASTQ

• Input formats: GTF, CSV, TXT

• Importing and visualizing count data using readr, tximport

Quality Control & Normalization

• Filtering low-count genes

• Normalization methods: TPM, RPKM, TMM, DESeq2’s size factor

• Tools: edgeR, DESeq2, limma

Differential Expression Analysis

• Experimental design and statistical models

• Running DE analysis using DESeq2

• Extracting significant DEGs with adjusted p-values (FDR)

Data Visualization Techniques

• MA plot, volcano plot, heatmap, PCA plot

• Libraries: ggplot2, pheatmap, EnhancedVolcano

• Practice with real dataset (e.g., cancer, COVID-19 RNA-Seq)

Gene Annotation and Enrichment Analysis

• Mapping gene IDs to gene symbols

• Functional enrichment using clusterProfiler, biomaRt, GOstats

• Pathway analysis with KEGG and Reactome

Case Study & Biological Interpretation

• Case Study: Differential expression in cancer (e.g., BRCA vs Normal)

• Annotate top DEGs and connect to biological function

Automating Workflows & Reproducibility

• Writing R scripts for RNA-Seq pipelines

• R Markdown for report generation

Final Project & Presentation

• Participants analyze a real dataset from GEO or TCGA

• Submit annotated result file, interpretation summary, and visualizations

• Present findings in a short presentation or PDF report

Tools & Packages Used

• R & RStudio

• DESeq2, edgeR, limma – Differential analysis

• tximport, biomaRt, clusterProfiler, ggplot2, pheatmap, EnhancedVolcano

• GEOquery, org.Hs.eg.db, KEGG.db, AnnotationHub

📂 Assignments & Final Project

• Analyze and interpret a real-world RNA-Seq dataset

• Identify DEGs and create publication-ready plots

• Annotate genes and perform enrichment analysis

• Submit a short bioinformatics report

📜 Certification

Participants will receive:

• Certificate of Completion

• Project Experience Letter upon successful submission of project

👥 Target Audience

• Postgraduate/PhD students in biotechnology, molecular biology, genomics

• Bioinformatics and computational biology learners

• Researchers working with transcriptomics or gene expression profiling