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Differential Gene Expression Analysis
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Differential Gene Expression Analysis

This course provides a comprehensive understanding of gene expression analysis, teaching you how to process RNA-Seq data and identify differentially expressed genes. You’ll learn to interpret expression patterns, perform statistical analysis, and uncover biological insights from transcriptomic data.

What You will Learn

  • Process and analyze RNA-Seq data for gene expression studies.
  • Perform differential gene expression (DGE) analysis using statistical methods.
  • Visualize gene expression patterns and identify significant genes.
  • Conduct functional enrichment analysis to understand gene functions.
  • Generate automated reports for biological interpretation.

Who Should Enroll

Bioinformaticians, geneticists, and researchers interested in transcriptomics and gene expression studies.

Tools Covered

  • FASTQC & Trimmomatic (Quality control and preprocessing)
  • STAR & HISAT2 (Read alignment)
  • HTSeq & featureCounts (Quantification of gene expression)
  • DESeq2 & edgeR (Differential gene expression analysis)
  • ClusterProfiler (Functional enrichment analysis)
  • R and Python (Data visualization and statistical analysis)

Course Instructor

Dr. Neelima Chitturi
Dr. Neelima Chitturi is a distinguished bioinformatics expert with over 15 years of experience in transcriptomics, genomics, and computational biology. A Ph.D. holder from IIIT-Hyderabad and IBAB-Bangalore, she specializes in identifying genetic variants, analyzing microbiome data, and developing bioinformatics pipelines. As the founder of Bruhaspathi Institute of Biosciences (BIB), she is passionate about simplifying complex bioinformatics concepts and empowering students with practical, real-world skills. Her expertise spans clinical diagnostics, pharmacogenomics, and cancer genomics, making her a sought-after educator and researcher in the field.
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