🌱 TRACK 20 • 10 MODULAR CHAPTERS PLANT BIOTECHNOLOGY • CROP GENOMICS SOLANUM TUBEROSUM • DROUGHT TOLERANCE

End-to-End Plant Stress Transcriptomics • Hands-on R Programming

RNA-Seq Data Analysis for Plant Biotechnology using R Programming

An End-to-End RNA-Seq Workflow for Plant Stress Research: From Experimental Design & Genome Alignment to Factor Regression, Volcano Plots & Pathway Enrichment.

This comprehensive curriculum guides participants through an end-to-end RNA-Seq analysis workflow for plant stress transcriptomics using R programming. Participants will learn experimental design, raw sequencing data processing, read alignment, gene expression quantification, normalization, and differential expression analysis using tools such as DESeq2 and edgeR. The workshop also covers statistical modeling of multiple experimental factors, transcriptomic data visualization through volcano plots and heatmaps, and functional interpretation using pathway enrichment and gene ontology analysis to identify stress-responsive biological processes.

📦 Production R & Bioconductor Setup CRAN + Bioconductor 3.19+

# Install all required R packages for the complete plant RNA-Seq pipeline
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install(c("DESeq2", "edgeR", "preprocessCore", "EnhancedVolcano",
                       "pheatmap", "clusterProfiler", "enrichplot", "RColorBrewer",
                       "tximport", "GenomicAlignments", "openxlsx", "tidyverse"))

Start Chapter 01 → Explore 10 Modules ↓ Workshop Schedule (4 Days) ↓

Official Training Flyer

Click flyer to view full high-resolution image

👥

Who Should Attend

🎓

PhD and Master's Students Specializing in Bioinformatics, Plant Genomics, Molecular Biology, or Life Sciences.

🌱

Plant & Crop Researchers Exploring RNA-Seq, plant abiotic/biotic stress transcriptomics, and breeding genetics.

🌾

Agricultural Scientists Investigating plant stress responses, drought resilience, and differential gene expression.

🔬

Wet-Lab Scientists & PIs Transitioning from bench experiments to independent, script-driven RNA-Seq analysis in R.

🎯

Learning Outcomes

Understand the core fundamentals of RNA-Seq and plant stress transcriptomics.
Perform read alignment, quantification, and normalization of plant RNA-Seq data.
Conduct differential expression analysis using production packages DESeq2 and edgeR.
Apply statistical modeling of multiple experimental factors (Genotype × Treatment) in R.
Generate publication-quality visualizations including volcano plots, heatmaps, and MA plots.
Perform pathway enrichment and functional interpretation of stress-responsive genes via clusterProfiler.

📅 INTENSIVE 4-DAY CURRICULUM BLUEPRINT

Daily Workshop Agenda & Practical Benchmarks

Live sessions held at 7:30 PM IST | 8:00 AM CST with full hands-on R scripts and real potato drought datasets.

DAY 1

Experimental Design & Data Prep

November 30, 2026 • 7:30 PM IST / 8:00 AM CST

RNA-Seq concepts & potato drought experiment
Selecting resistant vs non-resistant strains
Raw FASTQ QC with FastQC & fastp
Reference mapping with HISAT2 & TopHat2
Hands-on R & RStudio alignment exploration

DAY 2

Quantification & Normalization

December 1, 2026 • 7:30 PM IST / 8:00 AM CST

Pipeline A: Bowtie2 + RSEMExpTable
Pipeline B: TopHat2 / HISAT2 + HTSeq
Raw counts vs FPKM vs TPM
Addressing RuBisCO compositional distortion
DESeq2 Size Factors & Quantile Normalization

DAY 3

Differential Expression & Regression

December 2, 2026 • 7:30 PM IST / 8:00 AM CST

DESeq2, edgeR, and cuffdiff methods
Factor regression: Genotype × Drought Duration
Design matrix construction & F-test / LRT
Execution of complete DEG pipeline in R
Automated significance flagging table

DAY 4

Visuals, Pathways & Wrap-Up

December 3, 2026 • 7:30 PM IST / 8:00 AM CST

MA plots, Volcano plots, Heatmaps (pheatmap)
Pathway & Gene Ontology analysis (clusterProfiler)
Biological insights: ABA signaling, LEA, Osmolytes
Troubleshooting plant extraction artifacts
End-to-end reproducible script integration

COURSE CURRICULUM

10 Comprehensive Modular Chapters

All modules include executable R / Bash scripts and sample datasets

MODULE 01 Day 1 • Foundations & Study Design

Plant Stress Transcriptomics & Experimental Design in Solanum tuberosum

Principles of plant RNA-Seq, selecting potato cultivars (drought-tolerant vs susceptible), temporal sampling under water-deficit regimes, eliminating batch effects, and defining biological replication.

Key Deliverables:

Understanding plant transcriptional dynamics under abiotic stress
Design matrix formulation: Genotype (tolerant vs susceptible) × Drought Timepoints

RStudio tidyverse pwr DesignMatrix

Read Chapter 01 →

MODULE 02 Day 1 • Genomic Resources & QC

Plant Genome Resources, Spud DB, GFF3/GTF Annotations & FASTQ QC

Navigating plant reference genomes (Spud DB, Phytozome, Ensembl Plants), polyploidy considerations, filtering plastid/mitochondrial rRNA contamination, and running FastQC + fastp.

Key Deliverables:

Downloading Solanum tuberosum reference genomes (DM1-3 v4.04/v6.1) and GFF3/GTF annotations
Dissecting plant gene models: CDS, UTR, pseudogenes, transposable elements, and chloroplast/mitochondria

FastQC fastp MultiQC Spud DB Ensembl Plants

Read Chapter 02 →

MODULE 03 Day 1 • Spliced Alignment

Spliced Read Alignment to Complex Plant Genomes with HISAT2 & TopHat2

Building plant genome indexes, handling non-canonical plant introns and alternative splicing, executing HISAT2/TopHat2 alignments, and generating sorted, indexed BAM files with Samtools.

Key Deliverables:

Extracting splice sites and exons from plant GTF/GFF3 for index building
Running HISAT2 with plant-specific parameters (--dta, --pen-noncansplice)

HISAT2 TopHat2 Samtools Rsamtools

Read Chapter 03 →

MODULE 04 Day 2 • Expression Quantification

Gene & Isoform Quantification: featureCounts, HTSeq & RSEM

Quantifying exon and gene-level reads using featureCounts and HTSeq; executing Bowtie2 + RSEM for transcript isoform estimation; comparing raw integer counts, FPKM, and TPM.

Key Deliverables:

Running Subread featureCounts for paired-end plant RNA-Seq datasets
HTSeq-count configuration: union, intersection-strict, and strand-specific handling

featureCounts HTSeq RSEM tximport

Read Chapter 04 →

MODULE 05 Day 2 • Library Normalization

Normalization Strategies: TMM, DESeq2 Size Factors & Quantile Normalization

Overcoming compositional bias from high-abundance transcripts (e.g., RuBisCO / stress proteins); executing edgeR TMM, DESeq2 Median-of-Ratios, and preprocessCore Quantile Normalization.

Key Deliverables:

Understanding compositional distortion caused by massive RuBisCO (rbcS/rbcL) transcripts
DESeq2 Median-of-Ratios size factor calculation and mathematical derivation

DESeq2 edgeR preprocessCore ggplot2

Read Chapter 05 →

MODULE 06 Day 3 • Statistical Modeling

Differential Expression Modeling with DESeq2 and edgeR

Formulating the Negative Binomial model (Y_ij ~ NB(mu_ij, alpha_i)), empirical Bayes dispersion shrinkage, Wald tests, and log2 fold-change shrinkage using apeglm and ashr.

Key Deliverables:

Theoretical breakdown of the Negative Binomial distribution vs Poisson for RNA-Seq count overdispersion
Empirical Bayes dispersion estimation and variance stabilization (vst & rlog)

DESeq2 edgeR apeglm ashr

Read Chapter 06 →

MODULE 07 Day 3 • Factor Regression & Interactions

Multi-Factor Experimental Modeling: Genotype × Drought Time-Series

Designing complex model matrices in R (~ genotype + time + genotype:time), conducting ANOVA-like Likelihood Ratio Tests (LRT) to uncover cultivar-specific stress adaptation genes.

Key Deliverables:

Setting up factorial design matrices in R: model.matrix(~ Genotype * Time)
Distinguishing main effect genes (general drought response) from interaction genes (cultivar-specific tolerance)

DESeq2 limma-voom stats dplyr

Read Chapter 07 →

MODULE 08 Day 4 • Visualization & Clustering

Publication-Ready DEG Visualizations: Volcano Plots, MA Plots & Heatmaps

Generating customized MA plots, publication-grade Volcano plots with ggplot2 and EnhancedVolcano, and bidirectional hierarchically clustered heatmaps using pheatmap and ComplexHeatmap.

Key Deliverables:

Constructing MA plots displaying mean expression vs log2FC with shrunken estimates
Creating publication-grade Volcano plots highlighting key drought transcription factors (DREB, WRKY, NAC)

ggplot2 EnhancedVolcano pheatmap ComplexHeatmap

Read Chapter 08 →

MODULE 09 Day 4 • Functional Enrichment & Biology

Functional Enrichment: Plant GO, KEGG & Drought Biological Mechanisms

Gene Ontology and KEGG pathway enrichment using clusterProfiler; biological interpretation of ABA signaling, DREB/WRKY transcription factors, LEA proteins, and proline osmoprotection.

Key Deliverables:

Custom OrgDb preparation and GO enrichment (BP, MF, CC) using clusterProfiler
Plant KEGG pathway enrichment: Plant hormone signal transduction, Starch/Sucrose metabolism, and Glutathione

clusterProfiler enrichplot KEGGREST org.At.tair.db

Read Chapter 09 →

MODULE 10 Day 4 • Reproducible Capstone

Applied Capstone: End-to-End Plant Stress Pipeline & Quarto Reporting

Complete, reproducible R Markdown / Quarto pipeline script from raw count matrix to biological publication figures, gene tables, and cross-species validation in crop biotechnology.

Key Deliverables:

End-to-end reproducible R script executing complete multi-factor Solanum tuberosum workflow
Automated generation of publication tables (.xlsx / .csv) with annotated DEGs and functional terms

Quarto rmarkdown openxlsx BiocManager

Read Chapter 10 →

Looking for Other Bioinformatics Specializations?

Explore all 20 Flagship Curricula across Genomics, Transcriptomics, Proteomics, GWAS, Single-Cell, and Nextflow Pipelines.

Browse All 20 Curricula (252 Chapters) → Track 18: Practical NGS RNA-Seq → Track 19: Environmental Statistics in R →