Bioinformatics Course

Lesson 1 • Python and R for Biological Analysis

Provides entry-level scripting in Python and R tailored to biological data. Students write scripts to parse sequences and generate basic plots.

Lesson 2 • Command-Line Essentials for Bioinformatics

Introduces the Unix shell as the primary environment for bioinformatics tools. Proficiency here underpins tool installation and pipeline execution throughout the course.

Lesson 3 • Core Bioinformatics Databases

Surveys major public repositories for sequences, structures, and functional annotations. Students gain hands-on retrieval skills essential for every subsequent chapter.

Lesson 4 • Reproducibility and Workflow Management

Establishes best practices for reproducible research using version control and workflow tools. Sets professional standards applied in all subsequent practical exercises.

Lesson 1 • Alignment Visualization and Interpretation

Teaches tools for visualising alignments and extracting biological meaning. Students connect alignment patterns to conserved functional regions.

Lesson 2 • Pairwise Alignment Algorithms

Explains dynamic programming behind global and local alignment. Students implement Needleman-Wunsch and Smith-Waterman to understand scoring mechanics.

Lesson 3 • Heuristic Database Search Methods

Covers BLAST and its variants for rapid similarity searching against large databases. Students interpret E-values, bit scores, and alignment statistics correctly.

Lesson 1 • Genome Assembly Algorithms

Explains overlap-layout-consensus and de Bruijn graph approaches to assembly. Students run assemblers and understand how k-mer size affects contiguity.

Lesson 2 • Genome Annotation

Covers structural and functional annotation of assembled genomes using ab initio and evidence-based tools. Annotated genomes feed directly into comparative genomics analyses.

Lesson 3 • Next-Generation Sequencing Technologies

Compares short-read and long-read platforms, their error profiles, and throughput. Technology choice directly determines assembly strategy and downstream analysis.

Lesson 4 • Assembly Quality Assessment

Introduces N50, BUSCO completeness, and reference-based evaluation metrics. Students distinguish high-quality assemblies from fragmented drafts.

Lesson 1 • Read Mapping and Quantification

Covers splice-aware alignment and pseudo-alignment for transcript quantification. Students map reads to reference genomes and transcriptomes using standard tools.

Lesson 2 • RNA-Seq Experimental Design

Addresses replication, batch effects, and library preparation choices before sequencing. Sound design prevents confounding that cannot be corrected computationally.

Lesson 3 • Differential Expression Analysis

Applies statistical models to identify genes with significant expression changes between conditions. Students use DESeq2 and edgeR and interpret results rigorously.

Lesson 4 • Visualisation of Expression Data

Produces heatmaps, volcano plots, and PCA plots to communicate expression results. Visualisation skills are applied in reports and presentations throughout the course.

Lesson 1 • Mass Spectrometry Proteomics Data Analysis

Processes shotgun proteomics data from peptide identification to protein quantification. Integrates proteomics results with transcriptomics for multi-omics interpretation.

Lesson 2 • Protein Sequence Analysis

Covers domain identification, signal peptide prediction, and physicochemical property calculation. Sequence-level features inform structural and functional hypotheses.

Lesson 3 • Protein Structure Prediction

Introduces homology modelling, threading, and deep-learning structure prediction methods. Students generate and validate 3D models for proteins lacking experimental structures.

Lesson 4 • Molecular Docking and Virtual Screening

Covers receptor preparation, ligand docking, and scoring function interpretation. Students perform virtual screening workflows relevant to drug discovery applications.

Lesson 1 • Variant Annotation and Prioritisation

Annotates variants with functional consequence, allele frequency, and clinical significance. Students prioritise candidate variants for downstream experimental validation.

Lesson 2 • Population Genetic Analysis

Applies population genetics statistics to multi-sample variant data to infer structure and selection. Builds on variant calling skills to address evolutionary and epidemiological questions.

Lesson 3 • Structural Variant Detection

Identifies large deletions, duplications, inversions, and translocations using paired-end and long-read data. Structural variants underlie many phenotypic and disease associations.

Lesson 4 • SNP and Indel Calling

Applies probabilistic variant callers to detect single nucleotide polymorphisms and small insertions and deletions. Students filter raw calls to high-confidence variant sets.

Lesson 1 • Comparative Genomics Methods

Identifies orthologs, synteny blocks, and conserved non-coding elements across genomes. Comparative analysis reveals functional constraints and evolutionary innovations.

Lesson 2 • Tree Visualisation and Interpretation

Teaches rooting, clade annotation, and comparative visualisation of phylogenetic trees. Accurate interpretation of topology and branch lengths is essential for comparative analyses.

Lesson 3 • Molecular Evolution Fundamentals

Covers substitution models, molecular clocks, and neutral theory as the statistical basis for phylogenetics. Conceptual grounding here is required for model selection in tree building.

Lesson 4 • Phylogenetic Tree Construction

Applies distance, parsimony, maximum likelihood, and Bayesian methods to build trees. Students evaluate method assumptions and select appropriate approaches for their data.

Lesson 1 • Unsupervised Learning and Clustering

Uses k-means, hierarchical clustering, and topic models to discover patterns in omics data. Unsupervised methods reveal sample subtypes and co-expression modules.

Lesson 2 • Model Interpretation and Validation

Applies SHAP values, saliency maps, and benchmarking to ensure models are interpretable and generalisable. Rigorous validation prevents overfitting and spurious biological conclusions.

Lesson 3 • Supervised Learning for Prediction

Applies random forests, SVMs, and gradient boosting to classification and regression tasks in biology. Students predict gene function, splice sites, and protein properties.

Lesson 4 • Deep Learning for Sequence Analysis

Introduces CNNs and transformers for motif discovery, variant effect prediction, and protein language models. Students fine-tune pre-trained biological sequence models.