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About This Course

Hello from Olli

Hello! I am Olli the Octopus. I will be your guide in this course. With eight arms I can reach across sequences, structures, networks, and knowledge graphs all at once — and I love helping you connect the dots between them! I pop up at key checkpoints in each chapter to welcome you to new topics, give friendly reminders, highlight common pitfalls, guide your learning path, and help you celebrate your progress.

Why Bioinformatics Matters Now More Than Ever

Biological data is growing faster than any other domain of science. A single human genome sequencing run produces roughly 200 GB of raw data, and public databases like NCBI GenBank now hold trillions of nucleotide bases. Yet the real challenge is not storage — it is making sense of the relationships hidden in that data: which proteins interact, which genes regulate each other, which metabolic pathways are disrupted in disease, and which drug targets are worth pursuing.

Bioinformatics sits at the intersection of biology, computer science, and statistics, and demand for practitioners has never been higher:

In the United States (2025):

  • Bioinformatics scientist roles have grown 35% over the past five years, driven by precision medicine and pharmaceutical R&D1
  • The National Institutes of Health invests more than $2 billion annually in computational biology and data science infrastructure2
  • Median salaries for bioinformatics scientists exceed $90,000, with senior roles in pharma and biotech surpassing $150,0003

Worldwide:

  • Global genomics market revenue is projected to exceed $94 billion by 2030, creating sustained demand for computational talent4
  • The European Bioinformatics Institute (EMBL-EBI) serves over 7 million unique IP addresses per month, reflecting massive global reliance on open bioinformatics tools5

By studying with this open textbook you are joining a global community of learners preparing to decode genomes, model biological networks, discover drug targets, and advance precision medicine.

Olli Can Help!

Olli welcomes you

Traditional bioinformatics textbooks often cost two hundred to three hundred dollars and rely on static diagrams that cannot convey the dynamic, interconnected nature of biological networks. This course is free, interactive, and continuously updated.

Olli believes curiosity and a laptop should be the only prerequisites. Every chapter, simulation, and self-assessment is open, remixable, and designed for hands-on learning. Let's connect the dots together!

Learning Through Interactive Visualization

Instead of memorizing disconnected facts, you will build intuition with browser-based MicroSims that let you explore real biological data and models:

  • Translate mRNA sequences through an interactive codon table and discover reading frames
  • Compare prokaryotic and eukaryotic cell structures with drag-to-explore overlays
  • Traverse protein interaction networks, compute centrality measures, and identify hub proteins
  • Build phylogenetic trees from sequence alignments and visualize evolutionary relationships as graphs
  • Query biomedical knowledge graphs to find drug-gene-disease connections for drug repurposing
  • Explore metabolic pathway graphs and run flux balance analysis on genome-scale models

These simulations become laboratories you can reopen any time — no wet lab required — so conceptual understanding is paired with authentic computational experimentation.

What You Will Learn

Module Weeks Topic
1 1–3 Foundations — molecular biology, biological databases, data formats, graph theory, graph databases
2 4–5 Sequence Analysis — alignment algorithms, BLAST, phylogenetics, evolutionary graphs
3 6–7 Structural Bioinformatics — protein structure, contact maps, protein interaction networks
4 8–9 Genomics and Transcriptomics — genome assembly, variation graphs, regulatory networks
5 10–11 Pathway and Systems Biology — metabolic modeling, signaling networks, disease modules
6 12–13 Advanced Graph Applications — knowledge graphs, ontologies, GNNs, multi-omics integration
7 14 Capstone — student projects, presentations, and course synthesis

You Will Have Fun

Bioinformatics is full of wonder — from the elegance of a de Bruijn graph assembling a genome to the detective work of tracing drug targets through a knowledge graph. This course keeps that sense of discovery alive. Olli's mascot admonitions provide context-sensitive nudges, the case studies tie each module to real-world breakthroughs (SARS-CoV-2 variant tracking, cancer driver gene identification, drug repurposing), and the MicroSims encourage playful exploration. Whether you aspire to work in pharma, academic research, clinical genomics, or data science, the goal is the same: help you enjoy thinking computationally about biology while mastering practical skills.

Background

This textbook was produced with Claude Code using Skills, with an emphasis on interactive MicroSims, graph data models in every module, and alignment to Bloom's Taxonomy learning objectives at all six cognitive levels. You can customize this textbook and create your own version using a tool such as Claude Code Pro ($20). See the Claude Skills Textbook for a detailed tutorial on how to create your own textbook.

About Dan McCreary

Dan McCreary is a semi-retired AI researcher, solution architect, and educator who has spent more than three decades helping Fortune 100 organizations reason over massive datasets. At Optum he founded the Generative AI Center of Excellence and led the team that built one of the world's largest healthcare knowledge graphs — spanning over 25 billion vertices — to unify member, provider, and patient insights. During his tenure at Optum Dan taught over 3,000 engineers how to model healthcare data using graph databases. Dan's deep background in knowledge representation and systems thinking underpins the precise learning graphs and intelligent textbook workflows used throughout this course.

He is the co-author of Making Sense of NoSQL (Manning Publications), the founding chair of the NoSQL Now! conference, and a frequent keynote speaker on semantic search, ontology strategy, and AI hardware. Beyond industry, Dan has mentored students as a STEM volunteer since 2014 and now applies the same rigor to building open educational resources. You can visit the Intelligent Textbooks Case Studies to see over 70 textbooks that Dan has created or co-created with other authors.

Selected Credentials

  • B.A. in Physics and Computer Science from Carleton College
  • M.S.E.E. from the University of Minnesota
  • MBA coursework at the University of St. Thomas (33 of 36 credits complete)
  • Patent holder in semantic search and ontology management techniques
  • Advocate for large-scale Enterprise Knowledge Graph adoption across healthcare and education
  • Long-time promoter of accessible, low-cost AI-powered learning experiences

References

  1. Burning Glass Technologies / Lightcast. Labor market analysis for bioinformatics scientist roles (2020–2025). ↩

  2. National Institutes of Health. NIH Data Science Strategic Plan (2023), citing $2+ billion in computational biology investments. ↩

  3. U.S. Bureau of Labor Statistics. Occupational Outlook Handbook: Biological Scientists (2024) and Glassdoor salary data for bioinformatics scientists. ↩

  4. Grand View Research. Genomics Market Size, Share & Trends Analysis Report (2023), projecting $94.2 billion by 2030. ↩

  5. EMBL-EBI. Annual Report 2023, reporting 7+ million unique IP addresses per month accessing EBI services. ↩