About Bioinformatics
Bioinformatics is where biology meets computing: using code and statistics to turn
raw sequencing data into answers about genes, diseases, and evolution. This course
walks through it in the order most people actually learn it. It starts with
Molecular Biology Foundations and
Biological Databases & File Formats, then moves to
Python & R for Bioinformatics and
Command Line & Linux so you can work with real data.
Next comes Sequence Alignment & Search, covering pairwise and
multiple alignment and BLAST. From there, Sequencing Data & Quality
Control covers how NGS reads are produced, checked, and mapped, and
Genomics & Variant Calling covers assembly, SNP and indel
calling, and variant annotation.
Transcriptomics & RNA-seq covers measuring gene expression and
running differential expression analysis, and
Protein Structure & Phylogenetics covers PDB, AlphaFold, and
evolutionary trees.
Finally, Workflows & Reproducibility covers Nextflow,
Snakemake, Conda, and Docker: the skills that separate a one-off analysis from one
other researchers can trust and rerun.
Content
10 modules
01
Molecular Biology Foundations
DNA, RNA, and proteins, the central dogma, and how genes and genomes are organised: the biology you need to make sense of the data.
Central DogmaGenes & GenomesProteins
02
Biological Databases & File Formats
Finding and downloading data from NCBI, Ensembl, and UniProt, and reading the formats it comes in.
NCBIFASTA / FASTQGFF / VCF
03
Python & R for Bioinformatics
Writing scripts to parse sequences, wrangle tables, and plot results with Biopython, pandas, and R.
PythonBiopythonR / Bioconductor
04
Command Line & Linux
Working on a remote server: navigating the shell, chaining tools with pipes, and processing large text files.
Bashgrep / awk / sedSSH
05
Sequence Alignment & Search
Pairwise and multiple sequence alignment, scoring matrices, and searching databases with BLAST.
BLASTNeedleman-WunschMultiple Alignment
06
Sequencing Data & Quality Control
How next-generation sequencing works, checking read quality, trimming, and mapping reads to a reference genome.
IlluminaFastQCSAM / BAM
07
Genomics & Variant Calling
Assembling genomes, calling SNPs and indels, and annotating variants to see what they might do.
Genome AssemblyGATKVCF Annotation
08
Transcriptomics & RNA-seq
Quantifying gene expression, running differential expression analysis, and interpreting pathways and gene sets.
RNA-seqDESeq2Gene Ontology
09
Protein Structure & Phylogenetics
Exploring 3D protein structures and AlphaFold predictions, and building evolutionary trees from sequences.
PDBAlphaFoldPhylogenetic Trees
10
Workflows & Reproducibility
Packaging analyses into pipelines that anyone can rerun, with workflow managers, environments, and containers.
NextflowSnakemakeConda / Docker
What You Can Do After This Course
By the end of the curriculum you'll have worked through 5 hands-on projects and
covered the core of what a junior bioinformatics or computational biology role expects. Concretely,
you'll be able to:
Read the biology behind the data: explain the central dogma and what genes, transcripts, and proteins actually are.
Find and handle biological data: pull sequences from NCBI, Ensembl, and UniProt, and read FASTA, FASTQ, GFF, and VCF files.
Script your analyses: write Python and R code, and use the command line to process large files on a server.
Compare sequences: run pairwise and multiple alignments and search databases with BLAST.
Process sequencing reads: check quality, trim, map reads to a reference, and work with SAM/BAM files.
Find variants and expression changes: call and annotate variants, and run differential expression analysis on RNA-seq data.
Make it reproducible: explore protein structures and phylogenetic trees, then package your work into a pipeline with Nextflow or Snakemake, Conda, and Docker.