Next-Generation Sequencing (NGS) has revolutionized life science research by enabling high-throughput profiling of genomes and transcriptomes. Transcriptomic analysis (RNA-Seq) helps uncover gene expression patterns, regulatory changes, and molecular signatures linked to disease, development, and environmental response. However, converting sequencing output into biological insights requires structured computational pipelines, careful data quality checks, and correct statistical interpretation.
This workshop provides a complete dry-lab workflow for RNA-Seq/transcriptomic data analysis, including raw read QC, trimming, alignment or pseudo-alignment, expression quantification, differential expression testing, and downstream biological interpretation. Participants will also learn how to create publication-ready plots, understand batch effects, and connect gene-level findings to pathways and functional mechanisms.
Aim
This workshop aims to train participants to perform end-to-end NGS and transcriptomics data analysis, starting from raw sequencing reads to actionable biological interpretation. It covers quality control, alignment/mapping, quantification, normalization, and differential expression workflows. Participants will learn how to interpret results using functional enrichment, pathway analysis, and visualization. The program is designed to build practical, job-ready skills for research, clinical genomics, and biotech applications.
What Participants Will Learn
Understand NGS and RNA-Seq data formats, experimental design, and analysis goals.
Perform raw read QC, trimming, and contamination checks.
Run alignment/pseudo-alignment and quantify transcript/gene expression.
Conduct differential expression analysis with appropriate statistics and controls.
Interpret outputs using pathway/enrichment analysis and visualization.
