Transcriptome Library Preparation and Data Analysis: From RNA Extraction to Bioinformatics Interpretation
From RNA Extraction to Biological Insights: Unlock the Power of Transcriptome Analysis
About This Course
RNA sequencing (RNA-Seq) has become the gold standard for transcriptome analysis, enabling researchers to measure gene expression, alternative splicing, and identify new transcripts in a high-throughput manner. While sequencing technologies have evolved, accurate data analysis remains challenging, requiring expertise in bioinformatics pipelines, quality control, and statistical analysis. The ability to prepare high-quality RNA samples and interpret complex RNA-Seq data is essential for biological research and clinical applications.
This workshop provides hands-on training in RNA-Seq library preparation, focusing on sample quality, library construction methods, sequencing strategies, and the subsequent data analysis steps. Participants will learn to process raw sequencing data, perform quality control (QC), align reads, quantify expression levels, and identify differentially expressed genes (DEGs). Additionally, they will gain experience in functional annotation, pathway enrichment analysis, and data visualization using popular bioinformatics tools, preparing them for real-world applications in genomics and systems biology.
Aim
This workshop aims to provide participants with practical knowledge and skills in RNA-Seq transcriptome library preparation, followed by data analysis and bioinformatics interpretation. It covers the entire workflow from RNA extraction, library preparation, and sequencing to differential expression analysis and functional annotation. The program equips participants with the tools to process RNA-Seq data and extract meaningful biological insights for genomic research, transcriptomics, and systems biology.
Workshop Objectives
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.
Workshop Structure
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
Who Should Enrol?
- 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.
Important Dates
Registration Ends
01/16/2026
IST 07:00 PM
Workshop Dates
01/16/2026 – 01/18/2026
IST 08:00 PM
Workshop Outcomes
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.
Fee Structure
Student Fee
₹1799 | $70
Ph.D. Scholar / Researcher Fee
₹2799 | $80
Academician / Faculty Fee
₹3799 | $95
Industry Professional Fee
₹4799 | $110
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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