RNA-Seq Data Analysis using R and Bioconductor

Introduction to the Course

This internship is a deep, hands-on introduction to RNA-Seq data analysis—where you learn not just the theory, but the real workflow researchers use to turn raw sequencing outputs into clear biological insights. Using R and the Bioconductor ecosystem, you’ll learn how to import RNA-Seq count data, clean and normalize gene expression values, identify differentially expressed genes (DEGs), and present your findings with publication-style visualizations.
Most importantly, you’ll practice with real datasets and learn how to interpret results like a bioinformatician—so you understand what your plots and DEG lists actually mean in biological context.

Course Objectives

• Understand the full RNA-Seq workflow—from raw counts to biological interpretation.
• Learn how to import, preprocess, and normalize RNA-Seq count data correctly.
• Perform differential gene expression analysis using DESeq2 and edgeR.
• Create clear visualizations like heatmaps, volcano plots, MA plots, and PCA to explain expression patterns.
• Run functional enrichment analysis using GO and KEGG to understand pathways and biological functions.
• Build reproducible analysis pipelines using Bioconductor best practices.

What Will You Learn (Modules)

Module 1: Introduction to RNA-Seq and R Environment

• What RNA-Seq is and why it’s used in modern biology and medicine.
• Key biological applications: disease profiling, biomarker discovery, drug response, and more.
• Overview of common RNA-Seq data formats and what each one means.
• Setting up R and RStudio for bioinformatics and getting comfortable with Bioconductor.

Module 2: Importing and Exploring RNA-Seq Data

• Understanding the difference between raw FASTQ data and count matrices (and where your analysis usually begins).
• Working with common input formats: GTF, CSV, TXT.
• Importing and exploring count data using readr and tximport.
• Quick checks to understand sample quality and expression distribution before analysis.

Module 3: Quality Control & Normalization

• Filtering low-count genes so your analysis isn’t driven by noise.
• Normalization methods explained simply: TPM, RPKM, TMM, and DESeq2 size factors.
• Tools you’ll use: edgeR, DESeq2, limma.
• Learn how to tell if normalization worked (and what “bad normalization” looks like).

Module 4: Differential Expression Analysis

• Understanding experimental design (groups, replicates, confounders) and why it matters.
• Running differential expression analysis using DESeq2.
• Extracting significant DEGs using adjusted p-values (FDR) and interpreting fold-changes safely.
• How to avoid common mistakes like over-trusting small sample sizes or noisy results.

Module 5: Data Visualization Techniques

• Learn the most-used plots in RNA-Seq reporting: MA plot, volcano plot, heatmap, PCA plot.
• Libraries you’ll use: ggplot2, pheatmap, EnhancedVolcano.
• Hands-on practice with a real dataset (e.g., cancer, COVID-19 RNA-Seq) to build confidence.
• How to make plots “publication-ready” (clean labels, readable scales, correct comparisons).

Module 6: Gene Annotation and Enrichment Analysis

• Mapping gene IDs to gene symbols so your DEG list becomes biologically readable.
• Functional enrichment using clusterProfiler, biomaRt, GOstats.
• Pathway analysis with KEGG and Reactome to understand biological meaning.
• Learn how to tell a clear biological story from enrichment outputs (instead of just listing pathways).

Module 7: Case Study & Biological Interpretation

• Case study example: differential expression in cancer (e.g., BRCA vs Normal).
• Annotating top DEGs and connecting findings to real biological function.
• How to interpret results with scientific caution and confidence (what your data can and cannot claim).

Module 8: Automating Workflows & Reproducibility

• Writing reusable R scripts to automate RNA-Seq workflows.
• Using R Markdown to generate clean, shareable reports.
• Best practices for reproducible analysis: versioning, parameters, and transparent reporting.

Final Project

For the final project, you’ll work like a real bioinformatics analyst. You’ll select a real dataset (from sources like GEO or TCGA), process it using the full pipeline, and deliver results that look and read like a research report.

Deliverables include: an annotated DEG result file, interpretation summary, key plots (PCA, volcano, heatmap), and a short presentation or PDF report.

Who Should Take This Course?

This internship is perfect for:

• PG/PhD Students: Biotechnology, molecular biology, genomics, or related life-science fields.
• Bioinformatics Learners: Anyone building skills in computational biology and omics analysis.
• Researchers: Working with transcriptomics, gene expression profiling, or biomedical datasets.

Job Opportunities

After completing this internship, you’ll be prepared for roles such as:

• RNA-Seq Analyst / Transcriptomics Analyst: Running expression pipelines and DEG interpretation for studies.
• Bioinformatics Research Assistant: Supporting omics projects with analysis and reporting.
• Computational Biology Intern/Associate: Handling expression datasets and functional interpretation.
• Genomics Data Analyst: Working with gene expression datasets for diagnostics and research.

Why Learn With Nanoschool?

At Nanoschool, we focus on practical learning that feels like real research work—not just theory.

• Hands-on Learning: You’ll run real RNA-Seq pipelines, not just read about them.
• Bioconductor-Focused Training: Learn the tools researchers actually use in publications.
• Real Dataset Practice: Work with public datasets (GEO/TCGA-style) to build confidence.
• Portfolio Output: Finish with a report + plots you can showcase for internships, roles, or research applications.

Key outcomes of the course

• Run a complete RNA-Seq analysis workflow using R and Bioconductor.
• Identify and interpret differentially expressed genes (DEGs) using trusted statistical pipelines.
• Create publication-style plots (PCA, volcano, heatmaps) that clearly communicate findings.
• Perform enrichment analysis (GO/KEGG) to connect gene changes to biological pathways.
• Build reproducible analysis reports using scripts and R Markdown.

FAQs

• What is RNA-Seq analysis?
  RNA-Seq analysis is the process of converting sequencing-based expression data into biological insights—by cleaning, normalizing, identifying DEGs, and interpreting pathways/functions.
• Do I need prior bioinformatics experience?
  Basic biology knowledge helps, but this internship is designed to guide you step-by-step through the workflow using practical examples.
• Will I learn DESeq2 and edgeR?
  Yes. You will learn how to run differential expression analysis using both DESeq2 and edgeR and understand how to interpret the results.
• What will I submit for the final project?
  You’ll submit a DEG results file, key plots (PCA/volcano/heatmap), an interpretation summary, and a short presentation or PDF report.
• Is the workflow reproducible?
  Yes. You’ll learn how to create reproducible scripts and reports using R scripting and R Markdown.