Plant Bioinformatics Course

Lesson 1 • Central Dogma in Plant Systems

Traces DNA replication, transcription, and translation with plant-specific regulatory features. Links molecular processes to downstream bioinformatic data types.

Lesson 2 • Plant Genome Organisation and Complexity

Examines ploidy levels, repetitive elements, and genome size variation across plant species. Prepares students for challenges in plant genome assembly and annotation.

Lesson 3 • Key Plant Model Organisms

Introduces Arabidopsis, rice, maize, and tomato as reference systems for bioinformatic studies. Highlights genome resources and community databases for each species.

Lesson 1 • Biological Databases and Data Retrieval

Surveys major plant genomic databases and programmatic data access methods. Students can retrieve sequences, annotations, and expression data efficiently.

Lesson 2 • Linux Command Line for Bioinformatics

Teaches file navigation, text processing, and shell scripting essential for bioinformatic workflows. Directly enables students to run plant genomics pipelines on servers.

Lesson 3 • Python and R for Biological Data

Introduces Python scripting and R statistical computing for parsing and visualising biological data. Provides programming foundations used throughout all subsequent analyses.

Lesson 1 • BLAST and Heuristic Search Methods

Teaches BLAST variants, parameter tuning, and output interpretation for plant sequence queries. Enables rapid identification of homologs across large plant genome databases.

Lesson 2 • Pairwise Alignment Algorithms

Explains Needleman-Wunsch and Smith-Waterman algorithms with scoring matrices. Provides the algorithmic foundation for all downstream alignment-based analyses.

Lesson 3 • Profile and Domain-Based Searches

Introduces hidden Markov models and Pfam for detecting conserved plant protein domains. Extends search sensitivity beyond pairwise methods for divergent sequences.

Lesson 1 • Sequencing Technologies for Plant Genomes

Compares short-read, long-read, and Hi-C sequencing platforms for plant genome projects. Informs technology selection based on genome complexity and project goals.

Lesson 2 • Comparative Genomics and Synteny Analysis

Applies MCScan and SynMap to detect conserved syntenic blocks across plant genomes. Reveals evolutionary relationships and supports gene family studies.

Lesson 3 • Structural and Functional Gene Annotation

Teaches ab initio prediction, evidence-based annotation, and functional assignment of plant genes. Produces gene models with biological meaning for downstream analyses.

Lesson 4 • Repeat Identification and Masking

Addresses transposable element identification and repeat masking critical for plant genome annotation. Unmasked repeats cause false gene predictions and must be handled first.

Lesson 1 • Differential Expression Analysis

Applies DESeq2 and edgeR to identify statistically significant expression changes between plant conditions. Results are normalised, filtered, and visualised for biological interpretation.

Lesson 2 • Read Mapping and Quantification

Uses STAR and HISAT2 for splice-aware mapping and featureCounts or Salmon for quantification. Generates count matrices that serve as input for differential expression analysis.

Lesson 3 • RNA-seq Experimental Design

Covers biological replication, tissue selection, and library preparation strategies for plant RNA-seq. Sound experimental design prevents confounding and maximises statistical power.

Lesson 4 • Functional Enrichment and Pathway Analysis

Performs GO term enrichment and KEGG pathway analysis on differentially expressed plant gene sets. Translates statistical results into mechanistic biological hypotheses.

Lesson 1 • Short-Read Mapping for Variant Calling

Aligns resequencing reads to plant reference genomes using BWA-MEM and marks duplicates. Produces analysis-ready BAM files as input for variant detection.

Lesson 2 • Variant Annotation and Functional Impact

Annotates variants with SnpEff and VEP to predict functional consequences in plant genes. Links sequence variants to potential phenotypic effects and candidate genes.

Lesson 3 • SNP and Indel Variant Calling

Applies GATK HaplotypeCaller and FreeBayes to detect SNPs and indels in plant genomes. Variant filtering ensures high-confidence calls for downstream population analyses.

Lesson 4 • Population Structure and Diversity

Uses PLINK, ADMIXTURE, and PCA to characterise genetic structure in plant populations. Reveals domestication history, breeding groups, and adaptive variation.

Lesson 1 • Transcription Factor Binding and Motif Analysis

Identifies transcription factor binding sites using DAP-seq data and de novo motif discovery. Connects regulatory proteins to their target genes in plant gene networks.

Lesson 2 • Chromatin Accessibility and ATAC-seq

Processes ATAC-seq data to identify open chromatin regions and regulatory elements in plant genomes. Open chromatin maps reveal active promoters and enhancers controlling plant gene expression.

Lesson 3 • ChIP-seq for Histone Modifications

Analyses ChIP-seq data to map histone marks associated with active and repressed plant chromatin states. Histone modification profiles contextualise gene regulation and epigenetic memory.

Lesson 1 • Molecular Clock and Divergence Dating

Estimates divergence times using fossil calibrations and relaxed molecular clock models. Temporal context transforms phylogenies into evolutionary timelines for plant lineages.

Lesson 2 • Positive Selection and Molecular Evolution

Detects genes under positive selection using dN/dS ratios and branch-site models. Identifies plant genes driving adaptation to environmental and biotic stresses.

Lesson 3 • Ortholog Identification and Gene Family Analysis

Uses OrthoFinder to cluster plant proteins into orthogroups and analyse gene family evolution. Orthogroup assignments underpin all comparative and phylogenomic analyses.

Lesson 4 • Phylogenetic Tree Reconstruction

Applies maximum likelihood and Bayesian methods to reconstruct plant species and gene trees. Accurate trees are essential for interpreting evolutionary patterns and trait origins.