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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.
Learn to code the way biologists actually work — every example built on sequences, expression matrices and real experimental data.
From first script to publication-ready analysis pipelines
A biology-first Python curriculum. Every concept is introduced because a biological problem needs it — dictionaries are codon tables, loops scan genomes, dataframes hold expression matrices. Covers fundamentals, pandas/NumPy, Biopython, statistics and machine learning, and interactive Streamlit and Quarto reporting.
A field guide to data — tidyverse, Bioconductor and beyond
R taught as laboratory practice rather than computer science. Starts with the six data containers and base syntax, then works through the tidyverse, Bioconductor packages (Biostrings, DESeq2, edgeR, Seurat, biomaRt), statistical modelling, survival analysis and R Markdown / Shiny reporting.
Query, model and mine biological data at scale — from a first SELECT statement to distributed big-data pipelines.
From SELECT to schema design for genomics data
Learn SQL against biological data. Covers engine choice, joins, aggregation, CTEs, window functions, normalisation, indexes, views, triggers, stored procedures, transactions, EXPLAIN-plan tuning, biological schema design and querying real genomics databases.
Every major biological database, explained and queried
A reference course on the biological database landscape: primary sequence archives, secondary and curated resources, clinical and variant databases, genome browsers, pathway and drug databases, structure repositories and how to query each programmatically.
Hadoop, Spark and data mining at genome scale
What to do when the dataset no longer fits in memory. Distributed storage and compute, association rule mining, clustering, classification, text mining of the biomedical literature, graph mining and streaming analytics on biological data.
Machine learning, deep learning and modern AI explained from zero maths, applied end-to-end to biological problems.
Zero maths assumed — to random forests, SVMs and neural nets
A full university-level ML curriculum for people with no programming or maths background. Builds the required mathematics from arithmetic upward, then covers preprocessing, every major supervised and unsupervised algorithm, deep learning, model evaluation, and applications across RNA-seq, GWAS, drug discovery and digital pathology.
Deep learning, LLMs, RAG and AI agents for life science
Modern AI explained from first principles for biologists: neural network foundations, transformers and attention, large language models, prompt engineering, retrieval-augmented generation over scientific literature, AI agents, generative protein and molecule design, and responsible AI in medicine.
Take a model from notebook to reproducible production system
The engineering discipline around scientific ML: version control, data versioning, experiment tracking, containers, Kubernetes, CI/CD, model registries, deployment, monitoring, drift detection and reproducibility standards for regulated research.
Practical AI assistants in day-to-day research work
A hands-on series on using AI tools responsibly in a working lab — where they help, where they quietly fail, ground rules for scientific use, and reproducible workflows for literature review, code assistance and data interpretation.
Complete analysis workflows for NGS, multi-omics, structural bioinformatics and computational drug discovery.
Complete clinical and research sequencing pipeline
The flagship course: sequencing chemistries, raw data and QC, trimming, alignment, germline and somatic variant calling, CNVs, fusions and structural variants, annotation with VEP/ANNOVAR/SnpEff, ACMG clinical interpretation, reporting and pipeline automation.
Genomics, transcriptomics, proteomics, metabolomics and more
Each molecular omics layer in turn — genomics, transcriptomics, proteomics, metabolomics, lipidomics and epigenomics — what it measures, the platforms behind it, the analysis workflow, and how the layers integrate for precision medicine.
Structural bioinformatics, docking, QSAR and AI-driven design
The full in-silico discovery pipeline: target identification and validation, virtual screening, QSAR, molecular docking and binding-site analysis, homology modelling, molecular dynamics, RDKit cheminformatics, AI-driven design, ADMET prediction and clinical translation.
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.