School of Bioinformatics · Genomics & transcriptomics
RNA-seq Data Analysis
RNA-seq is the most requested analysis in molecular biology labs. You run a complete bulk RNA-seq analysis from raw reads to biological interpretation, learn the statistics behind differential expression and learn to spot the problems that make results unreliable.
Course overview
RNA-seq Data Analysis takes a bulk RNA-seq experiment from raw reads to a defensible list of differentially expressed genes and the pathways behind them. It covers both alignment-based quantification (STAR with featureCounts) and pseudo-alignment (Salmon).
The statistical core is the negative binomial model in DESeq2: how size factors normalise library depth, why dispersion estimates are shrunk, how a design formula encodes the experiment and what an adjusted p-value controls. Exploratory analysis comes first, because batch effects and outlier samples are far easier to catch in a PCA than in a finished gene list.
Learning outcomes
By the end of the course you will be able to:
- Design an RNA-seq experiment with adequate replication and depth
- Quantify expression with STAR + featureCounts or Salmon
- Run, check and interpret DESeq2 differential expression
- Detect batch effects and model them in the design
- Interpret results with GO, Reactome and preranked GSEA
Skills you will practise
Eligibility and prerequisites
Who can apply
- Students and graduates of B.Sc., M.Sc., B.Tech., M.Tech., B.Pharm., M.Pharm., MBBS, BDS, B.V.Sc. or a PhD programme in the life sciences, biotechnology, bioinformatics, pharmacy, medicine, agriculture, chemistry, statistics or computer science
- Researchers, faculty and industry professionals moving into data-driven biology
- Final-year students may apply before their results are declared
Prerequisites
- Bioinformatics Foundations or equivalent
- Basic R or Python
Best suited to
- Students and researchers with transcriptomics projects
- Wet-lab scientists who want to analyse their own RNA-seq data
- Anyone preparing for single-cell analysis
Module-wise syllabus
6 modules. Each pairs instructor-led sessions with a hands-on lab on public data.
Topics
- Replicates, depth and statistical power
- Library types and strandedness
- Confounding and batch effects
Topics
- QC and trimming
- STAR or HISAT2 alignment versus Salmon pseudo-alignment
- featureCounts and tximport
Topics
- Normalisation and variance-stabilising transforms
- PCA and sample clustering
- Recognising outliers and batch
Topics
- The negative binomial model in DESeq2
- Design formulas and contrasts
- MA and volcano plots; fold-change shrinkage
Topics
- Over-representation analysis
- Preranked GSEA
- Reporting enrichment honestly
Topics
- Download from GEO and SRA
- Full analysis
- Figures and methods for a manuscript
Practical sessions and projects
Capstone project
Re-analyse a GEO study
Download a public study, run the full workflow, compare your gene list with the authors’ and explain every difference you find.
Assessed on the correctness of the analysis, the quality of the code and the reasoning in the write-up. A pass is required for the certificate.
- Experimental design
Audit three published study designs for replication, confounding and depth, and propose a better design for one of them.
- From reads to counts
Quantify airway samples with Salmon and with STAR + featureCounts, and compare the resulting count tables.
- Exploratory analysis
Apply the variance-stabilising transform, draw PCA and sample-distance plots and identify the batch structure in the data.
- Differential expression
Fit DESeq2 models with and without a batch term, extract contrasts and draw MA and volcano plots with shrunken fold changes.
- Functional interpretation
Run over-representation analysis and preranked GSEA on Reactome and GO, and write a summary that states what the enrichment does and does not show.
- Project: a published dataset from GEO
Re-analyse a GEO study end to end and compare your results with those reported by the authors.
Software, tools and datasets
Software and tools
All software is free and open source or free for academic use. Recommended computer: 8 GB RAM, 30 GB free disk, Linux, macOS or Windows with WSL2.
Datasets
- airway: Dexamethasone-treated airway smooth-muscle cells: the main teaching dataset.GEO GSE52778 (Himes et al. 2014)
- GENCODE human annotation: Transcriptome index and gene models.GENCODE
- Reactome and Gene Ontology: Pathway and functional enrichment.reactome.org; geneontology.org
Core reading
- Love MI, Huber W, Anders S (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15:550.
- Patro R et al. (2017). Salmon provides fast and bias-aware quantification of transcript expression. Nature Methods 14:417–419.
- Conesa A et al. (2016). A survey of best practices for RNA-seq data analysis. Genome Biology 17:13.
Duration, format and fees
- Next cohort
- Dates are sent with your admission offer
- Timetable
- Three evening or weekend sessions a week (IST), plus a lab clinic; intensive and weekend-only schedules on request
- Payment
- Secure online payment link with your admission offer (UPI, cards, net banking). Instalments on request for fees above ₹15,000.
- Refunds
- See the refund and cancellation policy.
Instructor
The instructor for each cohort is named in your admission offer and on this page when the cohort is announced. Vedicgen instructors are selected for hands-on research or industry experience in the subject they teach, and every course is reviewed by a second subject expert.
Questions about who teaches a cohort? [email protected]
Certificate
Learners who complete the course and pass its capstone receive a Vedicgen certificate of completion for RNA-seq Data Analysis, issued by Vivofold, stating the guided hours and skills assessed. Each certificate has a unique ID and QR code that anyone can check.
Apply for RNA-seq Data Analysis
Apply
Send the form below. It takes about five minutes.
Review
An adviser confirms your fit and format within two working days.
Offer
You receive dates, timetable and a secure payment link.
Onboard
Set-up guide, pre-reading and joining links.
Frequently asked questions
Read processing uses command-line tools. Differential expression and enrichment use R and Bioconductor, because DESeq2 and edgeR are the reference implementations.
No. Single-cell analysis has its own course, which builds on this one.
In the live cohort, modules are taught in instructor-led sessions with a weekly lab clinic where you work on real public data with help at hand; every session is recorded. The self-paced format uses the same lessons, labs and datasets with mentor review of your capstone. Both formats end with an assessed project reviewed for the code and the science.
A laptop with at least 8 GB of RAM and about 30 GB of free disk space, running Linux, macOS, or Windows with WSL2. Installation instructions are sent before the first session, and lab clinics help with any set-up problems.
Yes. Learners who complete the course and pass its assessment receive a Vedicgen certificate of completion, issued by Vivofold. Every certificate carries a unique ID and a QR code that anyone, including an employer or admissions office, can check on the certificate verification page.
Apply with the form on this page. Live and in-person cohorts run through the year; the dates, timetable and joining details of the next cohort are sent with your admission offer and listed on this page once announced. Self-paced enrolment opens as soon as your application is accepted.
Yes. No course runs longer than four weeks, and the length is customisable: choose the standard schedule, an intensive one- to two-week schedule, weekends only, or a custom timetable. Pick your preference on the application form; colleges and teams can book their own dates.
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