TRANSCRIPTOMICS 30 days · 6 modules · Online

RNA-Seq Reference-Based Data Analysis Workshop (30-Day, Online)

Go from raw FASTQ reads to alignment, counting, differential expression and pathway analysis using a reference genome.

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About this workshop

RNA-Seq measures which genes are active in a sample and how strongly, by sequencing the RNA a cell has produced. When a good reference genome exists for your organism, the reads can be mapped straight onto it, which makes the analysis fast, well supported and easy to repeat. This workshop follows that route from raw files to biological meaning. You will check read quality, map the reads, count them per gene, and compare conditions statistically to find genes that really change. You finish with the outputs a paper or thesis needs: a differential-expression table, enrichment results for pathways and processes, and clear plots such as volcano plots and heatmaps. It suits anyone with RNA-Seq data, or about to generate some, who wants to understand every step rather than run a black-box tool.

Tools and pipelines you will use

This workshop emphasizes standard, open-source bioinformatics tools widely used in peer-reviewed genomics literature:

FastQC ↗fastp ↗STAR ↗HISAT2 ↗featureCounts ↗DESeq2 ↗edgeR ↗clusterProfiler ↗

What you will learn

Module 1 Foundations
Week 1
Linux and command-line basics Navigate files, run programs and keep analyses organised.
RNA-Seq experiment design Replicates, library types and depth, and why they matter.
File formats FASTQ, FASTA, GTF, BAM and count tables explained.
Read quality control Judge read quality and trim adapters and weak bases.
Module 2 Alignment
Reference genome and annotation Choose and prepare the right genome and gene model files.
Splice-aware mapping Align reads across exon junctions.
Alignment quality Check mapping rates, strandedness and coverage.
Visual inspection View alignments in a genome browser.
Module 3 Quantification
Gene-level counting Turn alignments into a count per gene.
Transcript-level estimates Estimate isoform abundance without full alignment.
Normalization Make samples comparable (TPM, CPM, size factors).
Sample-level checks Use PCA and clustering to spot outliers and batch effects.
Module 4 Differential expression
Statistical model How count-based testing works and what its assumptions are.
Running the comparison Set up groups and contrasts in R.
Reading the results Fold change, p-value and adjusted p-value in plain terms.
Complex designs Handle batches, pairs and multiple factors.
Module 5 Interpretation
Volcano plots and heatmaps Present results clearly and honestly.
Gene Ontology enrichment Find biological processes behind changed genes.
Pathway analysis Map changes onto KEGG and similar pathways.
Gene-set approaches Detect coordinated, subtle changes with ranked lists.
Module 6 Mini-project and report
Week 4
Project dataset Analyse a full public dataset end to end.
Reproducible workflow Record steps and settings so work can be repeated.
Figures and write-up Prepare publication-style figures and methods text.
Review and next steps Discuss results and how to extend them.

What you receive & prerequisites

What you receive

  • 30 days of structured online self-paced access & mentor guidance
  • Curated real-world NGS benchmark datasets
  • Reusable pipeline scripts and command-line code templates
  • Independent capstone mini-project evaluation
  • Certificate of Completion issued by Xenoi Biotech Pvt. Ltd.

Prerequisites & setup

  • Basic life science background (B.Sc., M.Sc., B.Tech, or research scholar)
  • No prior programming or Linux experience required (covered in Module 1)
  • Laptop or PC with 8 GB RAM and internet access
  • Remote compute setup instructions provided during onboarding

Course curriculum designed by Anjali Chaudhary, Bioinformatics Scientist at Xenoi Biotech.

Last updated: October 2026

Related workshops & services

Broaden your computational biology expertise with complementary workshops:

Need this analysis done for your laboratory data? Explore our Genomics Services or consult on Custom Bioinformatics Pipelines.

Frequently asked questions

Is this RNA-Seq (reference-based) workshop delivered online?

Yes. All Xenoi Biotech workshops are delivered 100% online, allowing researchers and students across India and internationally to participate with remote compute support.

How long is the workshop and what is the workload?

The workshop runs for 30 days across 6 structured modules. Learners typically dedicate 1 to 2 flexible hours per day to work through pipeline commands, code exercises, and the mini-project.

Who is this workshop designed for?

It is built for B.Sc., M.Sc., and Ph.D. students, postdoctoral researchers, faculty members, and industry professionals in biotechnology, biochemistry, microbiology, and medicine who need to analyze NGS datasets independently.

What will I learn in this workshop?

You will master raw read QC, splice-aware mapping with STAR and HISAT2, gene quantification with featureCounts, statistical differential expression with DESeq2 and edgeR, and pathway analysis using GO and KEGG.

What are the fees, dates, and enrollment details?

The workshop fee is MRP ₹5,000, inclusive of GST. Batch dates are shared on enquiry. Use the Enrol now button to reserve your place or receive the upcoming schedule.

Fees, dates and batches

The workshop fee is MRP ₹5,000 (incl. GST). Batch dates and onboarding materials are shared upon enquiry.

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