RNA-Seq Mastery: Analyze Differential Gene Expression Like a Pro- recorded course
RNA-Seq Mastery: Analyze Differential Gene Expression Like a Pro
Course Description
This comprehensive RNA-Seq masterclass is meticulously designed for the 2026 genomic landscape, where Next-Generation Sequencing (NGS) data volume demands elite analytical precision. You will dive deep into the "Dry Lab" environment, learning to transform chaotic raw reads into structured biological insights. The course covers the entire bioinformatics pipeline, including advanced Quality Control (QC), ultra-fast alignment using HISAT2/STAR, and sophisticated Differential Gene Expression (DGE) analysis. We go beyond basic counting by integrating AI-powered pathway enrichment and machine learning for sample clustering and diagnostic prediction. You will gain hands-on experience with the R/Bioconductor ecosystem, mastering packages like DESeq2 and edgeR. By the end of this program, you will be capable of navigating complex transcriptomic datasets to uncover the molecular drivers of disease, making you an invaluable asset in precision medicine and biotech R&D.
What You'll Learn
End-to-End Pipeline Execution: Architect a complete RNA-Seq workflow from raw data preprocessing to downstream biological interpretation.
Advanced Read Alignment: Master splice-aware mapping of reads to reference genomes using STAR and HISAT2 algorithms.
Statistical Rigor in DGE: Apply Generalized Linear Models (GLMs) and shrinkage estimators to identify significant fold changes in gene expression.
Functional Genomics: Use AI-driven tools and the Gene Ontology (GO) framework to map expressed genes to biological pathways and KEGG networks.
Interactive Data Visualization: Create publication-ready Volcano plots, Heatmaps, and PCA (Principal Component Analysis) plots to communicate complex results.
Curriculum
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End-to-End Pipeline Execution: Architect a complete RNA-Seq workflow from raw data preprocessing to downstream biological interpretation.
Lesson -
Advanced Read Alignment: Master splice-aware mapping of reads to reference genomes using STAR and HISAT2 algorithms.
Lesson -
Statistical Rigor in DGE: Apply Generalized Linear Models (GLMs) and shrinkage estimators to identify significant fold changes in gene expression.
Lesson -
Functional Genomics: Use AI-driven tools and the Gene Ontology (GO) framework to map expressed genes to biological pathways and KEGG networks.
Lesson -
Interactive Data Visualization: Create publication-ready Volcano plots, Heatmaps, and PCA (Principal Component Analysis) plots to communicate complex results.
Lesson
