What will I learn in the RNA-Seq Differential Expression Analysis Course?

You will learn RNA-Seq differential expression analysis, including transcriptome data processing, gene expression profiling, DESeq2, edgeR, data visualization, and biological interpretation of gene expression results.

Who should attend this RNA-Seq differential expression analysis course?

This course is suitable for students, researchers, biotechnology learners, bioinformatics professionals, genomics learners, molecular biology researchers, data analysis learners, and participants interested in transcriptomics and RNA-Seq workflows.

Does this course cover DESeq2 and edgeR for differential expression analysis?

Yes, the course covers DESeq2 and edgeR, explaining how these tools support RNA-Seq count data analysis, differential expression testing, gene expression profiling, statistical comparison, and interpretation of transcriptomic results.

Is this course useful for transcriptome data processing and biological interpretation?

Yes, the course is useful for learners interested in transcriptome data processing, RNA-Seq analysis, gene expression visualization, biological interpretation, functional insights, and research applications in genomics and molecular biology.

RNA-Seq Differential Expression Analysis Course

Attribute

Detail

Format

Recorded Lectures (Self-Paced)

Level

Intermediate

Duration

3 Days (1.5 Hours per day)

Certification

e-Certification + e-Marksheet

Tools

FastQC, HISAT2, featureCounts, DESeq2, R, ggplot2

About the Rna-Seq Course

RNA sequencing (RNA-Seq) has revolutionized transcriptomics by enabling researchers to study gene expression comprehensively and quantitatively. This course offers an in‑depth introduction to RNA-Seq data analysis, focusing on Differential Gene Expression (DGE) workflows using real‑life datasets and open‑source bioinformatics tools.

Participants will master sequence data formats, quality checks, read mapping, count normalization, expression profiling, advanced visualisations, and functional enrichment analysis. No prior coding experience is required, though basic molecular biology knowledge is beneficial.

Program Highlights

• Comprehensive coverage of RNA Seq Differential Expression Analysis from fundamentals to advanced applications

• Hands-on projects and real-world case studies in bioinformatics

• Expert-curated curriculum aligned with current industry standards

• Access to recorded lectures and e-LMS platform for flexible, self-paced learning

• e-Certification and e-Marksheet upon successful completion

• Dedicated mentor support and interactive doubt-clearing sessions

• Practical experience with tools: FastQC, HISAT2, featureCounts, DESeq2

• Career-oriented training for academic and professional growth in bioinformatics

Course Curriculum

Module 1: Day 1 – Foundations of RNA‑Seq

Understand the RNA‑Seq workflow and DGE concepts
Acquire raw data and explore common file formats
Perform sequence quality checks using FastQC

Module 2: Day 2 – Mapping & Normalisation

Map reads to a reference genome with HISAT2
Quantify gene‑level counts using featureCounts
Apply count‑normalisation techniques (e.g., size‑factor)

Module 3: Day 3 – Differential Expression & Interpretation

Conduct differential expression analysis with DESeq2
Visualise results using heat maps and volcano plots in R/ggplot2
Perform functional enrichment and pathway analysis

Tools, Techniques, or Platforms Covered

FastQC
HISAT2
featureCounts
DESeq2
R
ggplot2
pheatmap
clusterProfiler

Real-World Applications

Apply RNA Seq Differential Expression Analysis skills directly to academic research, thesis work, and publications
Build a professional portfolio showcasing practical bioinformatics competencies
Solve industry-relevant problems using RNA Seq Differential Expression Analysis methodologies and tools
Contribute to open-source projects and collaborative research in bioinformatics
Prepare for competitive examinations, interviews, and professional certifications in bioinformatics

Who Should Attend & Prerequisites

Industry‑recognised e‑Certification + e‑Marksheets from NSTC
Hands‑on training with practical projects and authentic RNA‑Seq datasets
Dedicated expert mentorship and doubt‑resolution sessions

Prerequisites:

Frequently Asked Questions

1. What is the format of this RNA Seq Differential Expression Analysis course?

This is an Recorded Lectures (Self-Paced) course delivered via our e-LMS platform. You will have access to pre-recorded video lectures, reading materials, assignments, quizzes, and hands-on projects that you can complete at your own pace.

2. Will I receive a certificate after completing this course?

Yes! Upon successful completion of all modules, assignments, and assessments, you will receive an e-Certification along with an e-Marksheet from NanoSchool (NSTC) that you can showcase on your CV and LinkedIn profile.

3. What are the prerequisites for this course?

Learners should have a foundational understanding of bioinformatics concepts. Familiarity with basic tools and programming is recommended.

4. How long will I have access to the course materials?

You will have access to all course materials for the duration of 3 Days (1.5 Hours per day). The self-paced format allows you to learn according to your own schedule through our online learning management system.

5. Is mentor support available during the course?

Yes, dedicated mentor support is available throughout the course. You can reach out for doubt-clearing sessions, project guidance, and career advice related to bioinformatics. Our mentors are industry experts and experienced professionals.

Enroll in RNA Seq Differential Expression Analysis today and take the next step in your professional journey. With expert-curated content, practical projects, and industry-recognized certification, this course is your gateway to mastering bioinformatics skills that matter.

Brand	NSTC
Format	Recorded Lectures
Duration	3 Days (1.5 Hours per day)
Level	Intermediate
Domain	bioinformatics
Hands-On	Yes – Practical projects with industrial datasets
Tools Used	FastQC, HISAT2, featureCounts, DESeq2, R, ggplot2, pheatmap, clusterProfiler

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