Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
RNA-Seq Data Analysis using R and Bioconductor centres on the Bioconductor toolchain that dominates transcriptomics. You learn the RNA-Seq-specific workflow: importing count data, normalising, and running differential expression with DESeq2 or edgeR, then downstream enrichment and visualisation — all within the Bioconductor ecosystem and its data structures. The course emphasises the RNA-Seq design and statistical considerations that make results trustworthy. You finish able to run a Bioconductor RNA-Seq analysis end to end. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers RNA-Seq data analysis using R and Bioconductor — the RNA-Seq-specific workflow from counts through differential expression to enrichment, in the Bioconductor ecosystem.
📋 Course Objectives
1. Import and normalise RNA-Seq count data.
2. Run differential expression in Bioconductor.
3. Use DESeq2 and edgeR effectively.
4. Perform enrichment and visualisation.
5. Apply sound RNA-Seq design and statistics.
👥 Who Should Enroll?
• Molecular biologists and geneticists
• Bioinformatics students and staff
• Transcriptomics researchers
• Anyone using RNA-Seq data
🚀 Key Learning Outcomes
• The ability to analyse RNA-Seq in Bioconductor.
• A transcriptomics workflow.
• A reproducible R/Bioconductor project.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Foundations of RNA-Seq Data Analysis Using R and Bioconductor and Core Biological Principles
Analyze the principles of RNA-Seq technology and its applications in gene expression analysis • Develop a comprehensive understanding of the R and Bioconductor packages for RNA-Seq data analysis • Configure the R environment for RNA-Seq data analysis, including installation of necessary packages and libraries
Laboratory Techniques, Protocols, and Data Collection
Evaluate the laboratory protocols for RNA-Seq library preparation and sequencing • Design experiments for RNA-Seq data collection, including sample preparation and quality control • Implement quality control measures for RNA-Seq data, including assessment of sequencing depth and coverage
Bioinformatics Tools and Computational Analysis
Implement bioinformatics tools for RNA-Seq data analysis, including read alignment and quantification • Develop scripts for data processing and analysis using R and Bioconductor • Analyze the results of RNA-Seq data analysis, including differential gene expression and pathway analysis
Research Methodology and Experimental Design
Design experiments for RNA-Seq data analysis, including hypothesis testing and sample size calculation • Develop a comprehensive understanding of the research methodology for RNA-Seq data analysis • Evaluate the statistical methods for RNA-Seq data analysis, including hypothesis testing and confidence intervals
Advanced RNA-Seq Data Analysis Using R and Bioconductor Applications and Translational Research
Apply advanced RNA-Seq data analysis techniques, including single-cell RNA-Seq and spatial transcriptomics • Develop a comprehensive understanding of the applications of RNA-Seq data analysis in translational research • Implement RNA-Seq data analysis pipelines for large-scale datasets, including batch effect correction and data integration
Regulatory Compliance, Bioethics, and Safety Standards
Evaluate the regulatory compliance requirements for RNA-Seq data analysis, including HIPAA and IRB regulations • Develop a comprehensive understanding of the bioethics principles for RNA-Seq data analysis • Implement safety standards for RNA-Seq data analysis, including data security and confidentiality
Industry Applications, Career Pathways, and Case Studies
Analyze the industry applications of RNA-Seq data analysis, including pharmaceutical and biotechnology industries • Develop a comprehensive understanding of the career pathways for RNA-Seq data analysts • Evaluate the case studies of RNA-Seq data analysis in real-world applications, including precision medicine and personalized therapy
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Bioconductor |
| Covered Tool / Platform | Linux |
| Covered Tool / Platform | command-line interfaces |
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