NGS and Transcriptomic Data Analysis: From Raw Reads to Biological Insights

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.

• 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.

• Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
• Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
• University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
• Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
• Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.

Participants will be able to:

• Run a complete RNA-Seq workflow from FASTQ to differential expression results.
• Produce key plots (QC, PCA, heatmap, volcano) for reporting.
• Interpret gene expression changes with functional and pathway context.
• Understand common pitfalls: batch effects, low counts, bias, and overfitting.
• Prepare results suitable for thesis work, publications, or industry reports.