Our sites

These are the teaching sites, practicals, course materials and code that we have made. All of them are free to use, and the code for each one is on GitHub.

Teaching sites

These are sites we use in our own teaching. They run in a web browser, so students have nothing to install.

• How aligners work

  For students and educators

  An interactive tutorial on the algorithms inside sequence aligners and read mappers, from hashing and the Burrows–Wheeler transform to BLAST, multiple alignment and hidden Markov models, with exercises in every chapter.

  It has fourteen chapters, about eight hours of work in all, and runs entirely in the browser, even offline.

  Open the site: How aligners workCode on GitHub for How aligners work

• R workbench

  For students and educators

  R running in the browser, built as a base for practicals. The R console, an RStudio-style editor (with R Markdown, Quarto and Shiny), Jupyter-style notebooks and a terminal sit next to the practical's instructions.

  It runs on webR. The optional AI assistant needs each student's own API key.

  Open the site: R workbenchCode on GitHub for R workbench

• Bioinformatics workbench

  For students and educators

  Real bioinformatics programs, such as fastp, minimap2, BWA, samtools, bcftools, Salmon and PLINK 2, compiled to run in the browser. Python and R notebooks, a genome browser, a 3D structure viewer and a network viewer sit alongside them.

  It needs an internet connection to fetch Python, R and results from online databases.

  Open the site: Bioinformatics workbenchCode on GitHub for Bioinformatics workbench

Practicals

These are complete practicals, each with the instructions on one side of the screen and a workbench on the other. Each one shows a different side of technology enhanced learning for genomics.

• Genomic variant analysis practicals

  For students and educators

  Two practicals and a coursework workbench. Students take raw exome reads to a variant that matters in the clinic, CYP2C19*2, then annotate, filter and interpret variants, and finally analyse a patient's exome for their coursework.

  Real samtools and bcftools run in the browser, and FastQC, BWA-MEM and FreeBayes replay their recorded output. Answers are saved in the student's browser.

  Open the site: Genomic variant analysis practicalsCode on GitHub for Genomic variant analysis practicals

• Pedigrees: drawing, inheritance and ethics

  For students and educators

  A three-hour practical in which students take a family history, draw it as a pedigree, work out how a condition is inherited and think through the ethical problems that follow. They interview two simulated patients and draw each family as they go.

  Pedigree Studio is built in, and each drawing can be checked against the family it should show. Answers, drawings and interviews are saved in the student's browser and can be downloaded to hand in.

  Open the site: Pedigrees: drawing, inheritance and ethicsCode on GitHub for Pedigrees: drawing, inheritance and ethics

• Variant calling practical

  For students and educators

  Students align human and yeast reads with minimap2 or Bowtie 2, call variants with bcftools or exactSNP, and compare what each tool and setting finds, from a terminal or from Galaxy-style forms that share the same files.

  The aligners and variant callers are the real programs compiled to WebAssembly, and the data come with the site, so it needs no outside services during the practical.

  Open the site: Variant calling practicalCode on GitHub for Variant calling practical

• Pipelines and reproducibility

  For students and educators

  An extension practical that turns the reads-to-variants analysis into a reproducible pipeline in three ways, with Snakemake and conda, as a Galaxy workflow and with an AI agent, and asks which one meets Claerbout's principle.

  minimap2, samtools and bcftools are real programs compiled to WebAssembly. Snakemake, conda and Galaxy are recreated in the browser around them.

  Open the site: Pipelines and reproducibilityCode on GitHub for Pipelines and reproducibility

• AI agents for bioinformatics

  For students and educators

  A two-hour practical on AI assistants and AI agents. Students answer one question, which genotype NA12878 has at CYP2C19*2, with an assistant that writes commands and with an agent that runs them, and then turn the agent's run into an analysis they can repeat.

  The programs are the real ones, compiled to WebAssembly, and the AI is a real model. Each student enters their own API key, and Google's Gemini API has a free tier.

  Open the site: AI agents for bioinformaticsCode on GitHub for AI agents for bioinformatics

• Yeast, data and AI

  For students and educators

  Which genes does baker's yeast need to live? Students answer with a table of 5,481 genes, a Python notebook, machine learning, a protein language model and AlphaFold structures, helped by an AI coding assistant. Some of its answers contain deliberate mistakes for students to catch.

  Python runs in the page (Pyodide). The assistant gives prepared answers, or connects to a live model with the student's own API key. About 2½ to 3 hours.

  Open the site: Yeast, data and AICode on GitHub for Yeast, data and AI

• Proteins in 3D

  For students and educators

  Students study the immune receptor TLR4, its co-receptor MD-2 and bacterial LPS with UniProt, the Protein Data Bank, a 3D viewer and AlphaFold, then make a figure of their own, without leaving the page.

  Progress and answers are saved in the student's browser, and can be downloaded to keep or hand in.

  Open the site: Proteins in 3DCode on GitHub for Proteins in 3D

• Bioinformatics algorithms in Python

  For students and educators

  A three-hour practical in which students write Python in the browser and use it to look inside alignment, BLAST, trees and networks, with exercises that check themselves.

  Python runs in the page (Pyodide). Work can be saved as a Jupyter notebook.

  Open the site: Bioinformatics algorithms in PythonCode on GitHub for Bioinformatics algorithms in Python

• Introduction to R

  For anyone new to R

  Two short courses, R basics and then the tidyverse, with real R running in an RStudio-style workbench beside the instructions. Each course ends with a certificate to download.

  Adapted from Babraham Bioinformatics' Introduction to R (with Tidyverse) by Simon Andrews and Laura Biggins. Built on the R workbench.

  Open the site: Introduction to RCode on GitHub for Introduction to R

Learning through games

This is an example of gamified learning, in which the course becomes a game, with tasks to choose, experience points to earn and levels to reach.

• Datascape: Cambridge

  For data science apprentices and their tutors

  A workplace simulator set in Cambridge. Apprentices take on scenario tasks in SQL, data cleaning, machine learning, statistics and data ethics. Each task earns experience points towards the next level, and finishing one unlocks harder ones. The tasks are mapped to the Level 6 Data Scientist apprenticeship standard.

  Progress is saved in the browser.

  Open the site: Datascape: CambridgeCode on GitHub for Datascape: Cambridge

Data and science communication

An interactive chart that we use in lectures and outreach.

• Sequencing cost chart

  For educators, students and researchers

  How the cost of DNA sequencing per gigabase has fallen since 2001, by platform, with figures you can export for slides and papers.

  The figures come from the NHGRI and from manufacturers' specifications. Check them against the sources before you publish them.

  Open the site: Sequencing cost chartCode on GitHub for Sequencing cost chart

Teaching materials

Notebooks that we use in our courses. They live on GitHub rather than as websites.

• Computational biology notebooks

  For students

  Jupyter notebooks for teaching computational biology, including differential equations, SIR models, bootstrapping, Monte Carlo methods and mixed models.

  Open on GitHub for Computational biology notebooks

• Learn maths with Python

  For students

  Notebooks for learning mathematics with Python, from equations to calculus.

  Open on GitHub for Learn maths with Python

• Principles of Data Science notebooks

  For students

  Weekly notebooks to go with the textbook Principles of Data Science.

  Open on GitHub for Principles of Data Science notebooks

Code

Software that other developers can build into their own teaching sites.

• Genomics WebAssembly tools

  For developers

  Bioinformatics programs compiled to WebAssembly for web browsers and Node.js, 25 in all, among them BWA, minimap2, HISAT2, Salmon, kallisto, StringTie, FreeBayes, Kraken2, MACS3, Primer3 and PLINK 2, with DESeq2 through webR. Each is a separate package with a JavaScript API, tested against the original program.

  The tools run the real algorithms rather than replaying saved results. The Bioinformatics workbench is built on them.

  Open on GitHub for Genomics WebAssembly tools