Hands-on Workshops & Training
Practical, hands-on workshops introducing data wrangling in R, Bash, and Python — and diving into advanced themes in evolutionary and ecological genomics.
Learn by doing — explore evolution, adaptation, and biodiversity through data, code, and discovery. ️ —
About These Workshops
These interactive computational workshops are designed to turn theoretical concepts into practical skills.
Through Python (JupyterLab), R, and Bash/Linux, participants gain hands-on experience in analysing genomic and ecological data — from allele frequency dynamics to species distribution modelling.
Each session blends: - Conceptual introductions
Guided coding exercises
Data exploration and visualisation
Collaborative problem-solving and discussion
Workshop Categories
Workshop Themes
Explore how advanced tools reveal patterns of evolution, hybridization, and adaptation — from genes to ecosystems:
| Topic | Core Tools | Focus |
|---|---|---|
| Linux & Bash for Bioinformatics | bash · awk · sed · Slurm · HPC |
Automate genomic workflows and manage high-performance computing tasks |
| Introduction to R | R · RStudio · tidyverse · tidymodels · ggplot2 |
Build a foundation in R programming, visualization, and data manipulation |
| Temporal Genomics | R · Python · dadi · SFS · snpEff · GERP · phyloP · GPN |
Track allele frequency shifts and detect selection through time |
| Hybridization Genomics | ANGSD · GATK · bcftools · vcftools · plink |
Quantify introgression and genomic ancestry in hybrid zones |
| Trait & Genotype-based Distribution Models | R · caret · SDMtoolbox · GAM · GLM |
Integrate phenotypic and genomic predictors into distribution models |
| Phylogeography & Population Structure | STRUCTURE · ADMIXTURE · DAPC · PCAngsd · poppr |
Infer connectivity, admixture, and population differentiation |
| GWAS & Functional Genomics | PLINK · GEMMA · GAPIT · R |
Identify adaptive loci and genotype–phenotype associations |
| Gene Ontology & Selection Tests | topGO · clusterProfiler · scikit-bio |
Interpret biological functions underlying signals of selection |
| Species Distribution Models (SDMs) | R · Python · biomod2 · maxent · scikit-learn |
Predict species ranges and responses to climate change |
Aim
Empower students, researchers, and educators to bridge evolutionary theory, genomic data, and computational methods — building the skills needed to analyze, visualize, and interpret complex biological data.