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23 courses. Python and R are free in full; every other course opens its first two chapters as a preview and unlocks entirely with a subscription.
Linux, Git, workflow managers, HPC, cloud and the desktop and web tools every computational lab runs on.
Command-line foundations for computational biology
The shell skills every bioinformatics workflow assumes you already have: installation, the filesystem, file manipulation, pipes and redirection, text processing with grep/sed/awk, permissions, processes, SSH, package management and shell scripting.
Version control, collaboration and reproducibility
Git for scientific work: installation and configuration, the commit model, branching and merging, remotes, pull requests, resolving conflicts, releases and DOIs, GitHub Actions, and the reproducibility practices reviewers increasingly expect.
Reproducible bioinformatics workflow engineering
Build pipelines that survive contact with real data. Linux and Bash foundations, DAGs and reproducibility, Nextflow DSL2 channels/processes/modules/profiles/containers, cloud and cluster execution, a Snakemake deep dive, nf-core standards, benchmarking, CI/CD and capstone pipelines.
Clusters, schedulers and parallel genomics at scale
High-performance computing for biology: why HPC is needed, hardware architecture, networking and parallel storage, Linux administration, SLURM and PBS job scheduling, parallel programming, containers on clusters, monitoring, security and cloud HPC.
AWS, GCP and Azure for genomics workloads
Running biological analysis in the cloud: core service models, compute and object storage, cost control, security and data governance, Docker and Kubernetes, managed workflow services, and building reproducible cloud genomics platforms.
BLAST, alignment, structure, docking and genome browsers
A practical, click-by-click guide to the web tools used daily in research: every BLAST variant, multiple sequence alignment and phylogeny, SWISS-MODEL and structure prediction, docking servers, primer design, genome browsers, variant analysis and visualisation.
PyMOL, Chimera, MEGA, Cytoscape, IGV, SnapGene and more
Deep tutorials for the desktop software of computational biology: molecular visualisation and docking with PyMOL, RasMol, Discovery Studio and AutoDock Vina; PyRx, Chimera/ChimeraX and IGV; MEGA, Jalview, Cytoscape and UGENE; plus Geneious, ApE, SnapGene, Tablet, Artemis and CLC.