Gene Expression Analysis using R Programming

Day 1:

• Introduction to RNA seq and R
• Data types structures in R
• Installing packages in R

Day 2:

• Quality control and preprocessing
• Read Mapping using HISAT2
• Quantification of reads using FeatureCount

Day 3:

• Differential Expression for RNA-seq using DESeq2
• Visualization DEG and generation of heatmap
• Perform functional analysis on gene lists with R-based tools
• Pathway analysis

Installation Requirements

1. Download the most recent versions of R and RStudio for your laptop:

• R
• RStudio

1. Packages to be installed: FASTQC, Trimmomatic, HTSeq2, FeatureCount

Graduates, Post Graduates, Research Scholars, Academicians, Industry Professionals Bioinformatics, Computational Biology, Biology and Life Sciences,Biotechnology and Pharmaceutical Sciences, Medical and Clinical Research, Genomics and Genetics,

• Proficiency in R programming for gene expression analysis.
• Hands-on data analysis skills with real-world gene expression datasets.
• Interpretation of gene expression patterns to identify differentially expressed genes.
• Data visualization and effective presentation of gene expression analysis results.
• Understanding of data quality control and normalization for reliable analysis.
• Interpretation of gene expression data in the context of biological pathways and networks.
• Application of statistical methods for gene expression analysis and hypothesis testing.
• Knowledge of best practices and reproducibility in gene expression analysis.
• Collaborative learning and networking with peers and instructors.
• Confidence in conducting independent gene expression analysis using R programming.