Differential Gene Expression Profiling and Functional Pathway Enrichment in High-Throughput RNA-Seq Datasets

This project presents an end-to-end bioinformatics pipeline for processing and analyzing high-throughput RNA-Sequencing (RNA-Seq) datasets. The workflow encompasses rigorous raw read quality control (FastQC), alignment-free transcript quantification, and robust differential expression analysis using DESeq2 in R.

Key statistical filters (Log2 Fold Change thresholds and adjusted P-values / Benjamini-Hochberg FDR < 0.05) were applied to isolate statistically significant Differentially Expressed Genes (DEGs). Functional annotation and pathway enrichment analyses (GO Terms and KEGG Pathways) were conducted to elucidate underlying biological mechanisms. Publication-ready visualizations, including Volcano Plots and Hierarchical Heatmaps, were generated to map global transcriptomic shifts.