Computational Microbiology Study Path

A reading order through the computational layer of this vault. Hubs: MOC - Bioinformatics in Microbiology · MOC - AI in Microbiology

Stage 1 — Orientation (understand the shape of the field)

1. Microbial Genomics
2. Sequencing Technologies → Figure - Sequencing Platform Comparison
3. Sequencing Data Formats
4. Bioinformatics and AI Glossary (keep open while reading)

Stage 2 — One isolate, end to end

5. Read QC and Preprocessing
6. Genome Assembly
7. Genome Annotation
8. Sequence Alignment and BLAST
9. WGS Bioinformatics Pipeline → Figure - WGS Bioinformatics Pipeline
10. Practice: Genomics Command-Line Cheatsheet

Stage 3 — Clinical interpretation

11. AMR Gene Databases
12. Virulence Factor Databases
13. Variant Calling in Bacteria
14. Genotype to Phenotype Prediction
15. Reality check: Antimicrobial Susceptibility Testing

Stage 4 — Many isolates (populations and outbreaks)

16. Comparative Genomics
17. Pangenome Analysis
18. Plasmid and Mobile Element Analysis
19. MLST and cgMLST
20. Phylogenetic Tree Building → Phylogenomics and Outbreak Typing
21. Phylodynamics · Viral Genomics and Surveillance

Stage 5 — Beyond isolates

22. Metagenomics · 16S Amplicon Analysis
23. Metagenome-Assembled Genomes
24. Microbiome Statistics
25. Microbial Transcriptomics · Proteomics and MALDI Bioinformatics → Figure - Omics Layers in Microbiology

Stage 6 — Machine learning & algorithms

26. Machine Learning Basics for Microbiology → Figure - Machine Learning Workflow in Microbiology
27. AI Algorithms in Microbiology → Feature Representation for Microbial ML
28. Classical: Supervised Learning Algorithms in Microbiology · Linear and Kernel Models in Microbiology · Tree Ensembles in Microbiology
29. Unsupervised Learning in Microbiology · Transfer Learning in Microbiology
30. Deep: Deep Learning in Microbiology · Convolutional Neural Networks in Microbiology · Transformers and Attention in Microbiology · Graph Neural Networks in Microbiology · Generative Models in Microbiology
31. Applications: Machine Learning for AMR Prediction · AI Diagnostics in Microbiology · Digital Microscopy and Image AI
32. Model Evaluation in Clinical Microbiology · Population Structure Confounding in Microbial ML

Stage 7 — Frontier and responsibility

33. Structural Bioinformatics → AlphaFold in Microbiology → Protein Design for Antimicrobials
34. Protein Language Models · DNA and Genome Language Models → Foundation Models and LLMs in Microbiology
35. AI for Antibiotic Discovery · AI for Vaccine Design
36. AI in Antimicrobial Stewardship · AI for Outbreak Detection
37. AI Ethics in Clinical Microbiology · FAIR Data and Genomic Surveillance

Stage 8 — Working like a bioinformatician

38. Bioinformatics Toolkit for Microbiology
39. Reproducible Bioinformatics Workflows
40. Public Sequence Databases

Self-test checkpoints

• After Stage 2: can you explain what each file in a pipeline contains?
• After Stage 3: can you defend “gene present, MIC susceptible” to a clinician?
• After Stage 4: can you distinguish a clonal from a plasmid outbreak?
• After Stage 6: can you name three ways a published model could be inflated?
• After Stage 6 algorithms: can you match data type → algorithm family (genes→GBM, images→CNN, molecules→GNN)?