Bioinformatics Engineering 100 Labs Genomics & Nextflow

What you'll learn

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Architect complete production-grade genomic data pipelines from raw sequencing data to validated biological insights.
Master Linux, Git, Bash, Python, and Biopython for computational biology and bioinformatics engineering.
Build reproducible NGS workflows using BWA, Samtools, GATK, SnpEff, Docker, Apptainer, and Nextflow DSL2.
Design scalable RNA-Seq, single-cell, and multi-omics processing systems suitable for research and enterprise environments.
Develop computer-aided drug discovery workflows using RDKit, AutoDock Vina, PyMOL, and AlphaFold-generated protein structures.
Implement data quality validation, observability, governance, compliance, and audit logging for sensitive genomic datasets.
Deploy distributed cloud-ready genomic workflows while understanding reproducibility, infrastructure automation, and cost optimization.
Engineer secure, air-gapped, sovereign bioinformatics platforms that eliminate vendor lock-in while maintaining reproducibility.
Troubleshoot real-world sequencing, alignment, workflow, infrastructure, and containerization failures with confidence.
Complete a PhD-level capstone by designing and deploying an end-to-end sovereign genomics and drug discovery platform from scratch.
Requirements

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You should have
1. Basic computer literacy
2. Windows (using WSL2) or macOS also work
3. Python 3.12+
4. Docker Desktop or Docker Engine
5. Visual Studio Code
Recommended hardware
1. 16 GB RAM minimum (32 GB recommended)
2. Quad-core CPU or better
3. 100 GB+ free SSD storage (recommended for genomics datasets)
Description

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This course contains the use of artificial intelligence.
I only charge a fee solely for the time invested in building this comprehensive curriculum.
Stop Building Demo Pipelines. Start Engineering Production Bioinformatics Systems.
The rise of AI has made it easier than ever to generate scripts, notebooks, and proof-of-concept workflows. Many people can now produce code that appears to solve a bioinformatics problem. But production genomics demands much more than generated code.
Real pharmaceutical and genomics organizations require reproducibility, traceability, scalability, compliance, security, observability, and scientific correctness. A workflow that works once on a laptop is not the same as a workflow that processes thousands of sequencing samples, survives infrastructure failures, satisfies regulatory Requirements

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, and can be reproduced years later.
That difference is the difference betweencoding andengineering .
This course is built around engineering.
Rather than teaching isolated tools, you'll complete100 carefully structured hands-on labs that progressively build the skills required to design, deploy, validate, secure, and scale modern bioinformatics platforms.
Every lab builds on previous knowledge.
Every milestone produces something tangible.
Every module moves you closer to thinking like a production bioinformatics engineer.
Your Journey Through 100 Production Labs
We begin by establishing the engineering foundation. You'll build a professional Linux environment, learn Git, automate repetitive tasks with Bash, understand biological file standards such as FASTA, FASTQ, BAM, CRAM, and VCF, and prepare your workstation exactly as modern computational biology teams do.
Next, you'll master one of the core workflows in genomics: quality control, sequence alignment, and variant discovery. Using FastQC, MultiQC, BWA, Samtools, Picard, GATK, BCFtools, and SnpEff, you'll transform raw sequencing reads into validated variant datasets while learning why each processing stage matters.
Reproducibility becomes the next focus as you package complete toolchains with Docker and Apptainer. You'll create portable environments that eliminate "it works on my machine" problems and prepare workflows for HPC environments.
You will then move into one of the industry's most valuable technologies: Nextflow DSL2. Rather than simply running pipelines, you'll engineer modular, reusable, fault-tolerant workflows with execution profiles, containers, retry strategies, reporting, and monitoring.
From there, the course expands into RNA-Seq, transcriptomics, single-cell analysis, dimensionality reduction, clustering, and multi-omics integration. These labs expose you to modern biological data processing techniques that are increasingly common in pharmaceutical research.
The journey continues into computer-aided drug discovery. You'll process molecular datasets with RDKit, prepare docking experiments, perform virtual screening using AutoDock Vina, visualize protein-ligand interactions, and incorporate AlphaFold structures into practical workflows.
Engineering doesn't stop at scientific computation. You'll also learn how to validate data quality using Great Expectations, implement lineage tracking, enforce governance policies, anonymize sensitive genomic information, design audit trails, and understand how GDPR and HIPAA influence modern genomic platforms.
Finally, you'll explore cloud-scale execution, distributed infrastructure, infrastructure automation, object storage, Spark-based big-data processing, cost optimization, and secure deployment strategies before culminating in sovereign, air-gapped production systems.
Throughout the course, the emphasis remains consistent: build systems that are reproducible, observable, secure, and maintainable.
Lab 100: The Production Sovereign Genomics System
The final capstone is not a toy project.
It is a comprehensive engineering challenge that combines everything you've learned across the previous 99 labs.
Starting with raw FASTQ sequencing data, you'll construct a fully automated genomic processing platform capable of quality control, alignment, variant calling, transcriptomic quantification, molecular docking, data validation, governance, audit logging, and secure execution inside a completely air-gapped environment.
You'll orchestrate containerized workflows with Nextflow and Apptainer, manage metadata, enforce reproducibility, validate outputs automatically, implement resilient execution strategies, and package the entire platform for repeatable deployment.
Completing Lab 100 demonstrates your ability to integrate multiple disciplines-software engineering, computational biology, infrastructure, security, and workflow orchestration-into a cohesive production-grade system.
Why This Course Is Different
This is not a collection of disconnected tutorials.
It is a structured engineering curriculum.
You won't just learn commands-you'll understand why production systems are designed the way they are. You'll build habits around reproducibility, automation, validation, and operational excellence that transfer across research labs, biotech startups, pharmaceutical companies, and enterprise bioinformatics teams.
If your goal is to move beyond notebooks and isolated scripts toward building robust genomic platforms that can stand up to real-world demands, this course provides a practical path.
The bioinformatics industry continues to grow, and organizations increasingly value engineers who can bridge biology with modern software and infrastructure practices. The best time to begin building those skills is while the ecosystem is still evolving.
Enroll today, complete the labs in order, and by the time you finish Lab 100, you'll have engineered a portfolio-worthy sovereign genomics platform that demonstrates production-grade thinking-not just tool familiarity.
Who this course is for

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1. The Future Bioinformatics Engineer
You want to transition into computational biology, genomics, or pharmaceutical software engineering and need practical production experience-not just theory.
2. The Life Science Researcher
You understand biology but want to build reproducible, scalable pipelines using modern software engineering practices without relying on proprietary platforms.
3. The Senior Software or DevOps Engineer
You already build distributed systems and want to expand into biotech, genomics, precision medicine, and pharmaceutical infrastructure using modern open-source technologies.
Homepage

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