RNA Seq Differential Expression Analysis

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

Through this interactive training, participants will gain practical skills in sequence data formats, quality checks, read mapping, count normalization, and expression profiling. They will also explore advanced visualization tools such as heat maps and volcano plots, culminating with functional enrichment analysis for biological interpretation. No prior coding experience is required, but familiarity with basic molecular biology is beneficial.

This 3-day workshop aims to provide hands-on understanding and theoretical foundations in RNA-Seq analysis, from raw data to biological interpretation. Participants will learn key workflows including data acquisition, quality control, mapping, normalization, differential expression analysis, and visualization techniques using R and DESeq2.

• Understand the RNA-Seq workflow from sequencing to biological inference

• Learn to perform sequence quality control and read alignment

• Explore normalization techniques and statistical analysis using DESeq2

• Visualize results with heatmaps, volcano plots, and pathway analysis

• Gain confidence in interpreting and presenting differential expression data

• B.Tech / B.Sc / M.Sc / M.Tech graduates or final-year students from Biotechnology, Bioinformatics, Life Sciences, or allied disciplines

• Professionals and researchers working in Genomics, Molecular Biology, Pharmaceutical R&D, or related areas

• Enthusiasts and learners interested in Transcriptomics, Computational Biology, or exploring careers in bioinformatics

• Clear understanding of RNA-Seq experimental design and pipelines

• Familiarity with common tools: FastQC, STAR, HTSeq, DESeq2

• Hands-on practice with R-based visualizations

• Competence in interpreting gene expression changes

• Ability to run basic enrichment and pathway analysis

• Readiness to apply skills in academic or industry settings