Bioinformatics & Computational Biology. Where biology meets data science.

The main kinds of bioinformatics work and the skills each one needs.

Topics covered

• Bioinformatics work in genomics, drug discovery support and protein structure
• The skills bioinformatics roles ask for: biology, coding and statistics
• Planning your next step: industry roles, an MSc or a PhD

You produce: a skills map for the bioinformatics role you want.

Handle biological sequences in Python with Biopython: parse files, manipulate sequences and run basic operations.

Topics covered

• Python essentials for biologists in Jupyter
• Biopython sequence objects: transcription, translation and complements
• Parsing FASTA and GenBank sequence files
• Basic sequence statistics: length, GC content and motifs

You produce: Python scripts that parse and analyse sequence files.

Compare sequences with pairwise and multiple alignment, and search databases with BLAST.

Topics covered

• Pairwise alignment: global and local
• Scoring matrices and gap penalties
• Multiple sequence alignment
• Running BLAST searches against NCBI databases and reading the results

You produce: a BLAST search with the results interpreted.

How genomes are assembled and annotated, with an introduction to variant calling.

Topics covered

• From sequencing reads to a genome assembly
• Genome annotation: finding genes and other features
• An introduction to variant calling
• Exploring genomes with NCBI tools and GenBank

You produce: a small, documented variant analysis.

Measure gene activity with RNA-seq: the workflow, differential expression and pathway analysis.

Topics covered

• How an RNA-seq workflow runs, from reads to counts
• Differential expression: which genes change, and by how much
• Pathway analysis: what the changes mean biologically
• Plotting expression results clearly

You produce: a differential expression analysis with plots.

How protein structure relates to function, and how AI-era prediction and structure databases are used.

Topics covered

• From sequence to structure to function
• Protein structure prediction in the AlphaFold era
• Finding and reading entries in protein structure databases
• Comparing predicted and experimental structures

You produce: a structure report on a protein of your choice.

Statistical thinking for biological data, so you can tell a real signal from noise.

Topics covered

• Variation, replicates and sample size in biology
• Hypothesis tests and the multiple testing problem
• Avoiding common mistakes when reading biological data

You produce: a statistical interpretation of a biological dataset.

Where machine learning and AI are being applied in drug discovery, with a candid look at hype and reality.

Topics covered

• Where machine learning is used across the drug discovery pipeline
• What published results show, and what they do not
• Separating real progress from hype

You produce: a short, evidence-based briefing on one AI drug discovery application.

Share data and code responsibly, following open data practice and the FAIR principles.

Topics covered

• Open data and data sharing in biology
• FAIR principles: findable, accessible, interoperable and reusable data
• Reproducible computation with Jupyter notebooks and documented methods

You produce: a reproducible project repository with documented methods.

Pick a real bioinformatics question and solve it end to end with documented methods.

Topics covered

• Choosing a question: sequence alignment, variant analysis or expression analysis
• Finding the data in public databases
• Running and documenting the analysis in Jupyter
• Presenting your results and methods

You produce: your capstone analysis with documented methods.