RNA-Seq Data Analysis using R and Bioconductor
Unlock the Power of RNA-Seq: Comprehensive Data Analysis with R and Bioconductor for Accurate Insights
About This Course
This 4-week course will provide you with a deep dive into RNA-Seq data analysis, guiding you through the entire process using the powerful R programming language and Bioconductor package. Perfect for bioinformaticians, researchers, and students, this course will teach you how to clean, process, analyze, and visualize RNA-Seq data to uncover key biological insights.
Aim
The aim of the RNA-Seq Data Analysis using R and Bioconductor course is to provide participants with a comprehensive understanding of RNA-Seq data analysis from raw data processing to interpreting the results.
Program Objectives
- Understand RNA-Seq technology and its applications in gene expression and transcriptome analysis.
- Gain proficiency in RNA-Seq data preprocessing, quality control, and alignment using R and Bioconductor.
- Perform differential expression analysis with R-based tools like edgeR, DESeq2, and limma.
- Visualize RNA-Seq data using key techniques such as heatmaps, PCA plots, and volcano plots.
- Conduct pathway enrichment and gene ontology analysis to derive biological insights from RNA-Seq data.
Program Structure
Module 1: Foundations of RNA-Seq Data Analysis Using R and Bioconductor & Core Biological Principles
- Introduction to RNA-Seq technology and the biological principles behind RNA sequencing.
- RNA-Seq technology, core biological principles, data structures, and initial processing.
- Establish a foundational understanding of RNA-Seq and its biological relevance.
Module 2: Laboratory Techniques, Protocols, and Data Collection
- Explore lab techniques, protocols, and processes for RNA-Seq data generation.
- RNA isolation, library preparation, sequencing, and data collection techniques.
- Learn the practical aspects of collecting high-quality RNA-Seq data for analysis.
Module 3: Bioinformatics Tools and Computational Analysis
- Hands-on experience with bioinformatics tools and computational analysis for RNA-Seq.
- R programming, Bioconductor, data processing, differential expression analysis.
- Gain proficiency in applying bioinformatics tools for RNA-Seq analysis.
Module 4: Research Methodology and Experimental Design
- Learn research methodologies and experimental design principles for RNA-Seq studies.
- Experimental design, hypothesis formulation, sample size calculation, data collection strategy.
- Develop the skills to design effective RNA-Seq experiments aligned with research goals.
Module 5: Advanced RNA-Seq Data Analysis Using R and Bioconductor Applications and Translational Research
- Advanced techniques for analyzing RNA-Seq data with a focus on translational research.
- Advanced differential expression analysis, functional enrichment, multi-omics integration.
- Master the skills required to apply RNA-Seq in translational research and complex biological systems.
Module 6: Regulatory Compliance, Bioethics, and Safety Standards
- Understanding the ethical and regulatory aspects of RNA-Seq research.
- Bioethics, regulatory standards, safety practices in genomic research.
- Conduct RNA-Seq research while adhering to ethical guidelines and compliance standards.
Module 7: Industry Applications, Career Pathways, and Case Studies
- Explore industry applications, career opportunities, and real-world case studies in RNA-Seq.
- RNA-Seq applications in biotech, pharmaceuticals, diagnostics, and beyond.
- Understand how RNA-Seq is applied in industry and gain insights into career pathways.
Module 8: Publication-Ready Research and Scientific Documentation
- Learn how to document RNA-Seq research for publication.
- Research paper writing, reporting standards, scientific documentation for RNA-Seq analysis.
- Produce professional, publication-ready documentation for RNA-Seq studies.
Module 9: Capstone: End-to-End RNA-Seq Data Analysis Using R and Bioconductor Research Project
- Conduct a comprehensive research project that covers the entire RNA-Seq analysis workflow.
- End-to-end RNA-Seq analysis from raw data to biological insights.
- Complete a capstone project that demonstrates your full proficiency in RNA-Seq data analysis.
Who Should Enrol?
- Basic understanding of molecular biology, genomics, or biotechnology.
- Familiarity with statistical concepts and data analysis.
- Basic knowledge of programming, preferably in R (although beginners can also benefit, as foundational programming concepts will be covered).
- A background in bioinformatics or related fields is helpful but not mandatory.
- Strong interest in genomics, transcriptomics, or RNA-Seq analysis.
Program Outcomes
- Ability to independently analyze RNA-Seq data using R and Bioconductor.
- Proficiency in differential expression analysis and data visualization.
- Capability to make data-driven decisions in genomics research.
- Skills to integrate RNA-Seq with other omics data.
- Expertise in performing pathway enrichment and gene ontology analysis.
Fee Structure
Standard: ₹10,998 | $118
Discounted: ₹5499 | $59
We accept 20+ global currencies. View list →
Frequently asked questions
Will I get the recording?
Yes. Paid participants get the session recordings and study material in their NanoSchool dashboard (My registrations → Recorded Lectures / Study Material).
What if I miss a live session?
Watch the recording in your dashboard and send your questions to the mentor through the support desk.
Do I get a certificate?
Yes, an e-certificate of participation after the workshop, with a QR code anyone can verify and an "Add to LinkedIn" button.
Which fee category applies to me?
Choose the category that matches your current role: Student, Ph.D. Scholar / Researcher, Academician / Faculty or Industry Professional.
How can I pay?
By card, UPI or PayPal on the payment page; international bank transfer is also available there.
What is the refund policy?
See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy
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