Bioinformatics
Lab Curriculum.

BUG FOUND: learners who can run a bioinformatics tool once, but cannot trust a pipeline result.

$ step-01 brushup // weeks 1–2 — Topics taught:

1. Genomics basics: DNA, genes, variants, and sequence formats (FASTA/FASTQ concepts).
2. Python for bioinformatics: parsing, filtering, and summarising sequence data.
3. Quality control basics: read quality, adapters, and contamination checks.
4. Alignment and variant concepts: what a tool does, and what can go wrong.
5. Reproducibility basics: tool versions, parameters, and logs.
6. Reading a pipeline result and spotting a suspicious output.

$ step-02 debug // weeks 3–4 — Production Debugging. Tools: Python, sample sequence data, QC tools, pipeline logs.

• BUG #1: Pipeline failure: a run crashes — read the log, find the input / parameter / version cause, fix and rerun.
• BUG #2: Genomic data error: mislabelled / contaminated samples — detect it with QC, correct or exclude with reasons.
• BUG #3: Wrong parameters produce a misleading result — find the parameter error, rerun correctly, compare outputs.
• BUG #4: Silent failure: a pipeline finishes but with empty / truncated output — find the silent cause, add a QC check so it cannot pass silently again.

$ step-03 navigate // weeks 5–7 — Codebase / System Navigation:

• Navigate pipelines and datasets: trace a result to input, tool version, and parameters.
• Fix a pipeline step without breaking downstream steps; verify with a small test dataset.
• Audit a pipeline for reproducibility: versions, parameters, and data provenance, documented.
• Review a peer's analysis for QC gaps, parameter choices, and over-claimed conclusions.

$ step-04 ship // weeks 8–9 — Shipping Features: capstone — a cleaned analysis pipeline + report with methods and limitations stated, presented as if to a scientific reviewer.

$ ship → PROOF — exact output: cleaned pipeline code, analysis report with methods and limitations, and a QC summary.

$ run --lab bioinformatics → ✓ SHIPPED, job-ready