Transcriptome Library Preparation and Data Analysis: From RNA Extraction to Bioinformatics Interpretation

Participants will learn to:

• Perform high-quality RNA extraction and assess RNA integrity.
• Understand RNA-Seq library preparation protocols and sequencing platforms.
• Perform RNA-Seq data preprocessing (quality control, trimming, filtering).
• Analyze RNA-Seq data for differential gene expression using bioinformatics tools.
• Interpret and visualize results through pathway analysis, GO terms, and network analysis.

Day 1: RNA Extraction and Library Preparation

• Basics of RNA-seq technology and its applications in gene expression analysis
• Best practices for RNA isolation from different tissues and conditions, RNA quality assessment (RNA Integrity Number – RIN)
• Overview of RNA library preparation methods, including Poly-A selection, ribosomal RNA depletion, and cDNA synthesis
• Next-generation sequencing (NGS) platforms (Illumina, Oxford Nanopore, PacBio) and their impact on transcriptome data quality
• Tools: Qiagen, Thermo Fisher RNA extraction kits, NEBNext Library Prep, Illumina protocols Mini task: Perform RNA quality assessment (RIN score calculation) and design a basic RNA library preparation workflow 

Day 2: RNA-seq Data Processing and Quality Control

• Overview of raw RNA-seq data processing from FASTQ to aligned reads (quality trimming, adapter removal)
• Using tools like STAR, HISAT2, or TopHat for aligning reads to reference genomes or transcriptomes
• Assessing RNA-seq data quality using metrics like read depth, GC content, and alignment rate
• Tools: FastQC, Trimmomatic, STAR, HISAT2, MultiQC
• Process raw RNA-seq data, perform quality control, and generate alignment statistics using FastQC and STAR 

Day 3: Differential Expression, Pathway Analysis, and Reporting

• Introduction to differential expression analysis using tools like DESeq2 and edgeR
• Methods for data normalization, filtering low-expressed genes, and performing statistical analysis for differential expression
• Gene ontology (GO) enrichment, pathway analysis using tools like GSEA, DAVID, and KEGG
• Visualizing RNA-seq data through heatmaps, volcano plots, PCA, and MA plots
• Best practices for RNA-seq data reporting and reproducibility
• Tools:DESeq2, edgeR, ggplot2, ClustVis, GSEA, DAVID, KEGG
• Perform differential expression analysis using DESeq2 and create a volcano plot visualizing the results

Participants will be able to:

• Prepare high-quality RNA samples and generate RNA-Seq libraries.
• Conduct data quality control (QC) and preprocessing for RNA-Seq datasets.
• Perform differential expression analysis to identify genes of interest.
• Analyze functional enrichment and pathway analysis to interpret results.
• Visualize and report RNA-Seq results with actionable biological insights.

• 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.