NGS and Transcriptomic Data Analysis: From Raw Reads to Biological Insights
Turn Raw Reads into Research-Ready Biological Insights
About This Course
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.
Workshop Objectives
- 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.
Workshop Structure
Day 1: NGS/RNA-Seq Basics + QC & Preprocessing
- RNA-Seq experiment design: replicates, batch effects, metadata
- File formats: FASTQ, BAM/SAM, GTF/GFF, count matrices
- Quality control: reads, adapters, base quality, GC, duplication
- Trimming/filtering strategy + common pitfalls
- Applications: gene expression profiling, biomarker discovery
- Tools (hands-on): FastQC, MultiQC, fastp/Trimmomatic, Galaxy (optional)
Day 2: Mapping/Quantification + Counts Generation
- Reference-based alignment vs pseudo-alignment
- Alignment overview + QC: mapping rate, insert size, strandness
- Quantification: gene-level vs transcript-level counts
- Building a clean count matrix for downstream stats
- Applications: isoform changes, pathway-level signals
- Tools: STAR/HISAT2, Salmon/Kallisto, SAMtools, featureCounts/HTSeq, IGV
Day 3: Differential Expression + Functional Interpretation + Reporting
- DEA concepts: normalization, dispersion, FDR, contrasts
- Differential expression workflow + volcano/MA plots, heatmaps
- Enrichment: GO/KEGG/pathways; gene set analysis basics
- Reproducibility: pipelines, containers, sharing results
- Applications: disease vs control signatures, drug response, stress biology
- Tools: R/Bioconductor (DESeq2/edgeR/limma, tximport), clusterProfiler/g:Profiler, nf-core/rnaseq
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
01/10/2026
IST 07:00 PM
Workshop Dates
01/10/2026 – 01/12/2026
IST 08:00 PM
Workshop Outcomes
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.
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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