Understand the field of genomics, its applications, and its impact on modern science. Learn the fundamental principles of DNA structure, sequences, genes, proteins, NGS datasets and so much more.

Explore the history and evolution of sequencing technologies, and familiarize yourself with RNA-seq terminology, and gain insights into effective experimental planning for RNA-seq projects.

Learn techniques for searching and retrieving data from public databases and effectively access and utilize these databases to obtain RNA-seq datasets.

Assess the quality of RNA-seq data using industry-standard tools and apply methods to repair and improve low-quality data for accurate analysis.

Understand the importance of a reference genome in RNA-seq analysis, and interpret and analyze mapping results to ensure accurate alignment.

​Comprehend the concept of splicing, learn its relevance in gene expression, and generate a gene expression count matrix from mapped reads.

Grasp the fundamentals of differential gene expression analysis, annotate DEG results to identify biologically significant changes, extract statistically significant DEGs, and visualize them using volcano plots and heatmaps.

Perform Gene Ontology (GO) enrichment analysis to identify functional themes, conduct Gene Set Enrichment Analysis (GSEA) across the full dataset, and differentiate between GO and GSEA while understanding their unique applications.