Hands-on Genomics Tutorials

Welcome to the collection of hands-on bioinformatics exercises for the course.
Each exercise is designed to help you build practical skills in genomics, data analysis, and computational biology, using real data and modern tools.
You can browse, read, and copy-paste code directly from each exercise page.

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• Practical Session 1: FASTQ to VCF - Part 1
  From raw sequencing data to variant calls: learn how to access the HPC, explore FASTQ files, run quality control, trim reads, and map to a reference genome using real tools and scripts.

• Practical Session 2: FASTQ to VCF - Part 2
  From filtered BAM files to analysed variants: learn how to perform BAM filtering, call and filter variants, explore VCF files, and perform dimensionality reduction (PCA) on real datasets using R and HPC tools.

• Practical Session 3: Population Differentiation and Functional Insights
  From SNP-level differentiation to biological meaning: compute F_ST between populations using Stacks, identify defense-related genes under selection through GO enrichment analysis, and integrate climate data for downstream ecological and evolutionary interpretation.

• Sample Research Project Part 1: From population genomics to candidate gene discovery Identify defence-related genes under selection by comparing nucleotide diversity (π), Tajima’s D, and FST between defence and background genomic regions. Learn how to detect FST outliers, map them to annotated genes, and interpret evolutionary signals using real Arabidopsis thaliana data from the 1001 Genomes Project.

• Sample Research Project Part 2: GO enrichment analysis of high-FST genes Assess whether genes with elevated genetic differentiation are enriched for specific biological processes using Gene Ontology (GO) analysis. Learn how to summarize SNP-level FST at the gene level, perform enrichment testing with topGO (Fisher and KS tests), and interpret results in the context of defence-related selection using Arabidopsis thaliana data from the 1001 Genomes Project.

• STACKS + RADstackshelpR + SNPfiltR Workflow Integrate Stacks, RADstackshelpR, and SNPfiltR for optimal de novo SNP discovery. Learn parameter optimization strategies, systematic filtering approaches, and quality validation methods for non-model species RADseq analysis.