• Home
  • Course
  • RNA-Seq Data Analysis using R and Bioconductor
Sale!
RNA-Seq Data Analysis using R and Bioconductor

RNA-Seq Data Analysis using R and Bioconductor

Original price was: INR ₹112.00.Current price is: INR ₹59.00.

Advanced Bioinformatics Internship | Duration: 1 Month | Mode: Offline/Online (Live + LMS) Register with NSTC for advanced learning built around real industry execution Register with NSTC for advanced learning built around real industry execution. Enroll now with NanoSchool (NSTC) to get certified through industry-ready, professional learning built for practical outcomes and career growth.

Add to Wishlist
Add to Wishlist
Aspect
Details
Format
Modular online course with applied concepts
Level
Intermediate
Duration
3 Weeks
Mode
Conceptual + hands-on bioinformatics workflows
Tools Used
R, Bioconductor, DESeq2, EdgeR, ggplot2
Hands-On
RNA-Seq pipeline, differential gene expression analysis
Target Audience
Life science researchers, bioinformatics learners, students
Domain
Bioinformatics, Genomics, Computational Biology
About the Course
RNA sequencing (RNA-Seq) has become a fundamental technology for studying gene expression, disease mechanisms, and biological pathways.
This course provides a practical introduction to RNA-Seq data analysis using R and Bioconductor. Participants learn how to process raw sequencing data, perform statistical analysis, and interpret biological results. The focus is on real-world bioinformatics workflows used in genomics research and precision medicine.
“The goal is to help learners understand how computational tools transform sequencing data into meaningful biological insights.”
Why This Topic Matters

RNA-Seq is widely used in modern biological and medical research for:

  • Understanding gene expression patterns
  • Identifying disease biomarkers
  • Studying cancer and genetic disorders
  • Drug discovery and precision medicine
  • Functional genomics research
What Participants Will Learn
• Understand RNA-Seq workflows
• Perform QC and preprocessing
• Conduct differential gene expression
• Visualize gene expression pathways
• Interpret results for research
• Build reproducible R pipelines
Course Structure
Module 1 — Introduction to RNA-Seq and R for Bioinformatics
  • Transcriptomics Fundamentals – Overview of RNA-Seq technology.
  • R Programming Refresher – Coding logic for biological datasets.
  • Bioconductor Ecosystem – Introduction to the core genomics analysis packages.
  • Workflow Overview – Mapping the path from raw reads to final results.
Module 2 — Data Preprocessing and Quality Control
  • Data Import – Handling FASTQ and raw count data formats.
  • Quality Assessment – Using FastQC and related tools to identify sequencing issues.
  • Read Alignment – Concepts of mapping reads to a reference genome.
  • Count Matrices – Transforming alignment data into a quantifiable format.
Module 3 — Differential Gene Expression Analysis
  • Statistical Foundations – Understanding count data distributions and normalization.
  • DESeq2 & EdgeR – Industry-standard tools for identifying differentially expressed genes.
  • Correction Methods – Multiple testing correction and FDR control.
  • Significance – Determining biological vs. statistical significance.
Module 4 — Visualization and Exploratory Analysis
  • PCA Analysis – Visualizing sample variation and outliers.
  • Heatmaps & Clustering – Grouping genes and samples by expression patterns.
  • Volcano & MA Plots – Interactive visualization of gene changes.
  • ggplot2 for Bioinformatics – Creating publication-quality plots.
Module 5 — Functional Enrichment and Pathway Analysis
  • Gene Ontology (GO) – Gaining insights into biological processes and molecular functions.
  • Pathway Enrichment – Utilizing KEGG and Reactome databases.
  • Mechanism Mapping – Linking significant genes to biological pathways.
  • Case Study – Analyzing expression data from a specific disease model.
Module 6 — Reproducible Bioinformatics Workflows
  • Reproducible Research – Writing clean scripts and R Markdown documentation.
  • Workflow Automation – Best practices for pipeline efficiency and scalability.
  • Reporting – Generating automated analysis reports for stakeholders.
  • Future Trends – Emerging techniques in single-cell and spatial transcriptomics.
Tools, Techniques, or Platforms Covered
R Language
Bioconductor
DESeq2 & EdgeR
ggplot2
Differential Expression
Who Should Attend
  • Life science researchers and graduate students
  • Bioinformatics and genomics learners
  • Biotechnology and pharmaceutical professionals
  • Researchers transitioning to computational workflows
FAQs

What is this course about?
This course teaches RNA-Seq data analysis using the R programming language and Bioconductor tools.

Who is this course suitable for?
Researchers and students looking to process their own sequencing data or enter the field of genomics.

Do I need prior knowledge?
Basic biology and a slight familiarity with R are helpful, but we provide refreshers for key concepts.

Is there hands-on work?
Yes. You will process real datasets from raw counts to pathway analysis in a guided environment.

What tools or platforms are used?
RStudio, Bioconductor, DESeq2, EdgeR, and standard visualization libraries.

RNA-Seq analysis is the bridge between raw biological data and clinical application. Mastering these workflows allows researchers to contribute to the cutting edge of precision medicine and molecular biology. Join us to start transforming your sequencing data into discovery.

Brand

NSTC
Format

Online (e-LMS)
Duration

5 Weeks
Level

Advanced
Domain

Biotechnology, Life Sciences, Bioinformatics, RNA
Hands-On

Yes – Practical projects with industrial datasets
Tools Used

Python, R, BLAST, Bioconductor, LMS, ML Frameworks

Reviews

There are no reviews yet.

Be the first to review “RNA-Seq Data Analysis using R and Bioconductor”

Your email address will not be published. Required fields are marked *

Related products