Case Studies in Functional Genomics

Lesson 1 • Key Omics Layers Defined

Defines genomics, transcriptomics, proteomics, metabolomics, and epigenomics. Prepares students to integrate multi-omics data in later chapters.

Lesson 2 • The Central Dogma Revisited

Connects DNA, RNA, and protein relationships to functional outcomes. This grounds all downstream case study interpretation in molecular logic.

Lesson 3 • Experimental Design Principles

Introduces controls, replication, and statistical power in genomic experiments. Prevents common design flaws encountered throughout the course.

Lesson 4 • Genome Organisation and Function

Covers chromatin structure, gene density, and non-coding regions. It links physical genome architecture to regulatory capacity.

Lesson 1 • Single-Cell Sequencing Approaches

Introduces droplet-based and plate-based single-cell RNA-seq workflows. This establishes cell-level resolution as a key advantage over bulk methods.

Lesson 2 • Long-Read and Single-Molecule Sequencing

Covers nanopore and SMRT sequencing principles and error characteristics. It positions long reads as complements to short-read data in structural analyses.

Lesson 3 • Short-Read Sequencing Platforms

Explains sequencing-by-synthesis chemistry and cluster generation. It connects platform throughput and error profiles to downstream analysis choices.

Lesson 4 • Epigenomic Sequencing Methods

Covers ChIP-seq, ATAC-seq, and bisulfite sequencing for chromatin and methylation profiling. It links each method to specific regulatory questions.

Lesson 1 • Transcript Quantification Methods

Compares alignment-based and pseudo-alignment quantification strategies. This establishes count matrices as inputs for differential expression analysis.

Lesson 2 • Pipeline Automation and Reproducibility

Introduces workflow managers and containerisation for reproducible analyses. It connects automation to scalability across large case study datasets.

Lesson 3 • Variant Calling and Annotation

Introduces SNP, indel, and structural variant detection from aligned reads. It links variant annotation to functional impact prediction.

Lesson 4 • Read Alignment and Mapping

Covers reference genome selection, alignment algorithms, and mapping statistics. It connects alignment quality to downstream quantification accuracy.

Lesson 1 • Gene Ontology Framework

Explains the three GO namespaces and DAG structure. This grounds enrichment analysis in a controlled vocabulary that students shall apply throughout the course.

Lesson 2 • Gene Set Enrichment Analysis

Introduces ranked-list GSEA and its advantages over threshold-based ORA. It prepares students to interpret enrichment scores and leading-edge genes.

Lesson 3 • Over-Representation Analysis

Covers Fisher's exact test and hypergeometric models for gene set enrichment. It connects statistical thresholds to biological interpretability.

Lesson 4 • Functional Annotation in Case Studies

Applies GO and pathway tools to published functional genomics datasets. This develops critical evaluation of annotation choices and result interpretation.

Lesson 1 • CRISPR-Cas9 Mechanism and Variants

Reviews Cas9 cleavage, guide RNA design, and key Cas variants. This establishes mechanistic grounding for interpreting screen outcomes.

Lesson 2 • Interpreting and Validating Screen Hits

Covers orthogonal validation strategies and biological contextualisation of hits. It prepares students to move from screen output to mechanistic hypotheses.

Lesson 3 • Screen Execution and Quality Control

Details cell transduction, selection, and sampling at each screen timepoint. It connects QC metrics to confidence in downstream hit calling.

Lesson 4 • Pooled Library Screen Design

Covers genome-wide and focused library construction and coverage requirements. It links library design decisions to statistical power of hit detection.

Lesson 1 • Epigenomic Reprogramming Case Studies

Examines chromatin remodelling during iPSC reprogramming and cancer epigenome remodelling. This develops the ability to interpret dynamic epigenomic changes.

Lesson 2 • DNA Methylation in Gene Regulation

Analyses CpG methylation patterns and their roles in silencing and imprinting. It links methylation changes to disease and developmental case studies.

Lesson 3 • Three-Dimensional Genome Organisation

Examines TADs, compartments, and enhancer-promoter loops from Hi-C data. It connects 3D structure to gene regulation and disease-associated variants.

Lesson 4 • Histone Modification Landscapes

Covers active, repressive, and bivalent histone marks and their genomic distributions. It connects mark patterns to transcriptional states in case studies.

Lesson 1 • Bulk RNA-Seq in Disease Models

Analyses differential expression in cancer, neurodegeneration, and infection models. It connects transcriptomic signatures to disease mechanisms.

Lesson 2 • Splicing and Isoform Analysis

Covers alternative splicing detection and functional consequences in case studies. It extends transcriptomic analysis beyond gene-level to isoform resolution.

Lesson 3 • Integrating Transcriptomics with Other Omics

Combines RNA-seq with proteomics and epigenomics to strengthen mechanistic conclusions. This demonstrates multi-omics integration as a standard analytical practice.

Lesson 4 • Single-Cell RNA-Seq Case Studies

Applies clustering, trajectory, and cell-type annotation to scRNA-seq datasets. This develops cell-resolution interpretation skills beyond bulk analysis.

Lesson 1 • Gene Regulatory Network Reconstruction

Covers inference of transcription factor networks from expression and binding data. It connects network topology to regulatory logic and disease vulnerabilities.

Lesson 2 • Genome-Wide Association and Functional Mapping

Links GWAS loci to functional elements using eQTL, chromatin, and annotation data. This develops skills for translating statistical associations into biological mechanisms.

Lesson 3 • Translational Systems Genomics Case Studies

Applies integrative approaches to drug target identification and biomarker discovery. This culminates the course with end-to-end translational case study analysis.

Lesson 4 • Multi-Omics Data Integration Strategies

Compares early, intermediate, and late integration frameworks for multi-omics data. It equips students to select integration strategies matched to biological questions.