Your path into bioinformatics

A clear, jargon-free explanation of the field — what problems it solves and what a bioinformatician actually does.

Revisit DNA → RNA → protein, but now ask: what does each step look like as data? This reframe is the foundation of everything.

Rosalind's backstory — the problems that drove the field into existence, told through the story of the human genome project.

DNA replication, transcription, translation. Free, video-based, exactly the right level. Work through these before anything else.

Mendel, alleles, mutations, gene regulation. You'll encounter these terms constantly in datasets — know them cold.

A plain-language overview of the -omics landscape: what each one measures, and why you'd choose one over another.

Navigate directories, manipulate files, pipe commands. This is the foundation for every bioinformatics pipeline you'll ever run.

Python is the dominant language in bioinformatics. Variables, loops, functions, file parsing — all taught through biological examples.

R is still dominant in statistics and genomics (DESeq2, Seurat). If you're leaning towards transcriptomics or stats, start here instead.

Not optional. Track changes, collaborate, and never lose your work. Every analysis you run should live in a Git repository.

Take any DNA or protein sequence and find its relatives across all sequenced life. The first tool every bioinformatician learns.

GenBank, PubMed, SRA, RefSeq — NCBI hosts most of the world's public biological data. Learn to find what you need.

Run RNA-seq, variant calling, and genome assembly pipelines through a web interface. Excellent for understanding workflows before scripting them.

Explore annotated genomes visually. Navigate to any gene, see its structure, expression data, and conservation across species.

Pairwise alignment, scoring matrices (BLOSUM62, PAM), global vs. local alignment (Needleman-Wunsch, Smith-Waterman). Know why these choices matter.

A conceptual walk through the entire RNA-seq pipeline — what each step does and what can go wrong. Read this before running your first analysis.

p-values, multiple testing correction, PCA, clustering. These concepts appear in every analysis. Josh Starmer explains them better than anyone.

Reproducibility, workflow managers (Nextflow, Snakemake), containerization. Why you should never run analysis steps manually once you know better.

Nikolay Oskolkov · 2022

How modern biology generates data at scales that require computational thinking. A great orientation to the field's scope.

A broad overview of HT data types, preprocessing strategies, and common analytical pitfalls — an excellent first look at how data flows through a real lab.

Dr. Ömer Ali Bayraktar · 2023

A window into cutting-edge research. Don't worry about understanding every detail — focus on the biological questions being asked.

Machine learning applied to a real clinical problem. A clear example of how computational tools change medicine.

RNA-seq, single-cell, spatial transcriptomics. If gene expression and sequencing data interest you — start here.

Protein language models, structure prediction, deep learning on sequences. If AI applications in biology excite you — start here.

Molecular docking, protein structure, the AlphaFold revolution. If you are drawn to 3D molecular structures — this is your direction.

Sequencing entire microbial communities — gut, soil, ocean. If environmental or clinical microbiology calls you — start here.

The Gene Expression Omnibus has thousands of published datasets. Find one related to a paper you found interesting and download it.

Pick any methods paper with a public dataset. Reproduce one figure. You will learn more from this than any course.

Document your analysis in a README. Push your code. This is the beginning of your scientific portfolio.