Aim
This course trains participants to perform end-to-end RNA-Seq data analysis using R and Bioconductor—from raw count matrices to differential expression, visualization, functional enrichment, and publication-ready reporting. You’ll learn practical workflows used in research labs, understand quality checks and common pitfalls, and build a reproducible analysis pipeline that you can reuse for your own transcriptomics projects.
Program Objectives
- Understand RNA-Seq Workflows: Learn what RNA-Seq measures and how analysis steps connect logically.
- Work Confidently in R: Handle expression matrices, metadata, and Bioconductor objects cleanly.
- Differential Expression (DE): Run DE analysis using standard Bioconductor tools and interpret results correctly.
- Quality Control & Visualization: Perform QC, normalization checks, PCA/clustering, and key plots.
- Functional Interpretation: Convert DE genes into pathways, GO terms, and biological insights.
- Reproducible Reporting: Build a clean, reusable pipeline with documented steps and outputs.
- Hands-on Outcome: Complete a mini-project analysis and generate a report with figures and gene lists.
Program Structure
Module 1: RNA-Seq Fundamentals (What You’re Actually Measuring)
- RNA-Seq overview: reads → genes → counts → expression change.
- Experimental design essentials: replicates, confounders, batch effects.
- Common data formats: FASTQ, BAM, counts matrix, sample metadata.
- What makes RNA-Seq analysis “wrong” (and how to avoid it).
Module 2: Setting Up R + Bioconductor Workflow
- Installing key packages and setting project structure.
- Understanding Bioconductor objects (SummarizedExperiment concept).
- Importing counts + metadata and validating inputs.
- Basic data handling in R for transcriptomics (filters, joins, tidy summaries).
Module 3: QC and Preprocessing (Before You Trust Any Result)
- Initial QC checks: library size, count distributions, missing values.
- Filtering low-count genes (why it matters).
- Normalization concepts (size factors, variance stabilization).
- Exploratory plots: boxplots, density plots, sample correlation heatmaps.
Module 4: Differential Expression with DESeq2 / edgeR (Core Skill)
- Design formula: modeling conditions, covariates, batch effects.
- Running DE analysis step-by-step.
- Understanding output: log2 fold change, p-values, adjusted p-values (FDR).
- Interpreting results responsibly (effect size vs significance).
Module 5: Visualization for RNA-Seq Results
- PCA and clustering for sample separation and batch detection.
- Volcano plots and MA plots: what they show and how to read them.
- Heatmaps for top DE genes and gene panels.
- Exporting publication-ready figures and gene tables.
Module 6: Functional Enrichment & Pathway Analysis
- Gene ID conversion and annotation basics (ENSEMBL, Entrez, symbols).
- GO enrichment concepts and interpretation.
- Pathway analysis (KEGG/Reactome concepts) and common pitfalls.
- GSEA basics: why ranked lists can be more informative.
Module 7: Advanced Topics (Optional but Valuable)
- Batch effect handling and diagnostics (conceptual + practical strategies).
- Contrast design: multi-condition comparisons and interaction terms.
- Time-series RNA-Seq overview and common analysis approach.
- Single-cell vs bulk RNA-Seq: what changes in analysis logic (overview).
Module 8: Reproducible Reporting & Best Practices
- Project organization: data, scripts, outputs, and version tracking.
- Creating reproducible reports (RMarkdown / Quarto concept).
- Documenting parameters, software versions, and assumptions.
- Preparing results for publication: tables, figures, supplementary files.
Final Project
- Analyze an RNA-Seq dataset (provided or your own) end-to-end in R.
- Deliverables: QC summary, DE gene list, key plots, enrichment results, and final report.
- Example projects: treated vs control cells, disease vs healthy samples, stress response in plants, knockout vs wild type.
Participant Eligibility
- UG/PG/PhD students in Biotechnology, Bioinformatics, Genetics, Molecular Biology, or related fields
- Researchers working on transcriptomics or gene expression studies
- Professionals seeking upskilling in RNA-Seq analysis using R
- Basic familiarity with R is helpful, but beginners can follow with guided practice
Program Outcomes
- End-to-End Pipeline Skill: Ability to run a complete RNA-Seq analysis workflow in R/Bioconductor.
- Correct Interpretation: Confidence in reading DE results, QC signals, and biological meaning.
- Publication Readiness: Generate figures and outputs suitable for papers and reports.
- Reproducibility: Build reusable scripts and a structured reporting workflow.
- Portfolio Deliverable: A complete RNA-Seq analysis report you can showcase.
Program Deliverables
- Access to e-LMS: Full access to course materials, datasets (where applicable), and tutorials.
- Code Templates: Ready-to-use R scripts for QC, DE analysis, plotting, and enrichment.
- Hands-on Exercises: Guided tasks to build the workflow step-by-step.
- Project Guidance: Mentor support for your final dataset analysis and reporting.
- Final Assessment: Certification after completion of assignments + final project submission.
- e-Certification and e-Marksheet: Digital credentials provided upon successful completion.
Future Career Prospects
- Bioinformatics Analyst (Transcriptomics)
- Genomics Data Analyst / Research Assistant
- Computational Biology Associate
- NGS Data Analyst (R/Bioconductor workflow)
- Research Intern (Bioinformatics / Omics)
Job Opportunities
- Research Labs & Universities: Transcriptomics analysis for publications and grants.
- Biotech & Pharma: Omics analysis teams supporting discovery and biomarker workflows.
- Clinical Genomics & CROs: RNA-Seq analysis pipelines and reporting support.
- Healthtech Startups: Data-driven genomics and precision medicine platforms.
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