Self Paced

RNA-Seq Data Analysis using R and Bioconductor

Unlock the Power of RNA-Seq: Comprehensive Data Analysis with R and Bioconductor for Accurate Insights

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Early access to the e-LMS platform is included

  • Mode: Online/ e-LMS
  • Type: Self Paced
  • Level: Moderate
  • Duration: 4 Weeks

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 →

What You’ll Gain

  • Full access to e-LMS
  • Real-world dry lab projects
  • One-on-one project guidance
  • Publication opportunity
  • Self-assessment & final exam
  • e-Certificate & e-Marksheet

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