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 |
| Spatial Ecology & Ecological Modeling | sf, terra, ggplot2, dplyr, spatstat, spdep, tmap, dismo, randomForest, blockCV, mlr3 |
Spatial sampling theory, spatial point-pattern analysis, BioClim climate predictors, multivariate analysis, and machine learning approaches for species distribution modeling (SDM) with spatial cross-validation |
| Species Distribution Models (SDMs) | R · Python · biomod2 · maxent · scikit-learn |
Predict species ranges and responses to climate change |
| 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 |
Aim
Empower students, researchers, and educators to bridge evolutionary theory, genomic data, spatial ecology & modeling and computational methods — building the skills needed to analyze, visualize, and interpret complex biological data.