Bioinformatics Course

Lesson 1 • File Formats in Bioinformatics

Explains FASTA, FASTQ, GenBank, PDB, and GFF formats and their use cases. Prepares students to handle raw data files in subsequent chapters.

Lesson 2 • Biomolecular Data Types

Introduces nucleotide sequences, amino acid sequences, and structural coordinates as distinct data classes. Connects data type to appropriate analytical tools.

Lesson 3 • The Central Dogma Explained

Covers DNA-to-RNA-to-protein information flow and its exceptions. Anchors all downstream bioinformatics concepts in molecular biology reality.

Lesson 1 • Heuristic Database Search Methods

Introduces BLAST and FASTA heuristics for rapid database searching. Students learn to configure searches and evaluate statistical significance.

Lesson 2 • Multiple Sequence Alignment

Covers progressive, iterative, and consistency-based MSA methods. Students evaluate alignment quality and prepare alignments for downstream analyses.

Lesson 3 • Alignment Visualisation and Interpretation

Teaches tools for rendering and annotating alignments for biological insight. Bridges computational output to biological conclusions.

Lesson 4 • Scoring Matrices and Gap Penalties

Explains substitution matrices and gap penalty models that underpin alignment scoring. Connects mathematical choices to biological interpretability.

Lesson 1 • Raw Read Quality Control

Covers quality metrics, adapter trimming, and read filtering for sequencing data. Ensures clean input for assembly and downstream analyses.

Lesson 2 • Next-Generation Sequencing Technologies

Compares short-read and long-read sequencing platforms and their error profiles. Informs technology selection for specific genomic projects.

Lesson 3 • Genome Assembly Strategies

Explains overlap-layout-consensus and de Bruijn graph assembly approaches. Students select assembly strategies based on data type and genome complexity.

Lesson 4 • Genome Annotation Basics

Covers structural and functional annotation pipelines for assembled genomes. Connects raw assembly to biologically interpretable gene models.

Lesson 1 • Read Mapping and Quantification

Explains splice-aware alignment and pseudoalignment for transcript quantification. Students generate count matrices from raw RNA-seq reads.

Lesson 2 • RNA-seq Experimental Design

Covers replication, randomisation, and confounding factor control in RNA-seq studies. Proper design prevents analytical artefacts in expression data.

Lesson 3 • Functional Enrichment Analysis

Teaches gene ontology and pathway enrichment methods for interpreting expression results. Connects gene lists to biological processes and molecular functions.

Lesson 4 • Normalisation Methods

Compares RPKM, TPM, and count-based normalisation strategies. Correct normalisation enables valid cross-sample expression comparisons.

Lesson 1 • Mass Spectrometry Data Analysis

Introduces peptide identification, protein quantification, and post-translational modification detection from MS data. Connects proteomics data to biological insight.

Lesson 2 • Structural Comparison and Classification

Covers structural alignment, RMSD calculation, and protein fold classification. Students identify structural relationships beyond sequence similarity.

Lesson 3 • Protein Structure Prediction

Explains homology modelling, threading, and deep learning structure prediction methods. Students generate and evaluate predicted protein models.

Lesson 4 • Molecular Docking Fundamentals

Covers ligand-receptor docking principles, scoring functions, and result interpretation. Students assess protein-ligand interactions computationally.

Lesson 1 • Population Structure and Diversity

Covers principal component analysis, admixture modelling, and population differentiation statistics. Students characterise genetic diversity across populations.

Lesson 2 • Variant Annotation and Prioritisation

Introduces functional annotation, pathogenicity prediction, and variant prioritisation strategies. Connects raw variants to biological and clinical relevance.

Lesson 3 • Variant Filtering and Quality Control

Covers hard filtering, variant quality score recalibration, and genotype-level filters. Removes false positives before biological interpretation.

Lesson 4 • Variant Calling Methods

Explains SNP, indel, and structural variant calling algorithms and their assumptions. Students apply appropriate callers for germline and somatic contexts.

Lesson 1 • Community Detection and Modules

Covers clustering algorithms for identifying functional modules in biological networks. Students link network modules to biological pathways and processes.

Lesson 2 • Protein-Protein Interaction Networks

Explains PPI data sources, confidence scoring, and network construction. Students build and filter interaction networks for downstream analysis.

Lesson 3 • Network-Based Functional Prediction

Teaches guilt-by-association and network propagation for gene function prediction. Students draw on network topology to infer unknown gene functions.

Lesson 4 • Network Topology and Metrics

Covers degree distribution, clustering coefficient, and centrality measures. Students identify hubs, bottlenecks, and modular structures in networks.

Lesson 1 • Biological Interpretation and Reporting

Covers pathway-level synthesis, hypothesis generation, and scientific communication of multi-omics findings. Students translate computational results into actionable biological conclusions.

Lesson 2 • Machine Learning in Multi-Omics

Introduces supervised and unsupervised learning for biomarker discovery and sample classification. Students apply regularised models to high-dimensional omics data.

Lesson 3 • Dimensionality Reduction for Multi-Omics

Covers PCA, UMAP, and multi-omics factor analysis for high-dimensional data. Students reduce complexity whilst preserving biologically meaningful variation.

Lesson 4 • Multi-Omics Data Harmonisation

Covers sample matching, batch correction, and data normalisation across omics layers. Harmonised data is the prerequisite for valid integration.