📚 Official Curriculum & Master Syllabus

💻 Bioinformatics & Computational Biology: Syllabus & Course Map

Teach a biology student to interpret sequence and structure data, and the algorithms that compare them, well enough to use a result and to say when it does not support the sentence written under it.

10 Syllabus Modules

38 Mapped Topics

10 Chapter Pages

🗺️ View Course Map 📚 Chapter Pages (10)

CURRICULUM ROADMAP

Interactive Syllabus & Modular Structure

Official curriculum mapped to university degree syllabi and competitive examinations. Each module links directly to its dedicated study notes page and relevant subtopics.

10 Modules

Module 01 Notes →

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Core Learning Outcomes & Competencies

Upon mastery of this comprehensive syllabus, students and researchers are expected to:

✓ Separate bioinformatics from the wider computational biology, and match a question to genome, transcriptome, proteome, or structure data.

✓ Read a FASTA record, a FASTQ record and its Phred scores, the material fields of a GenBank flat file, a SAM alignment, and a VCF variant call, and state what an accession version does and does not certify.

✓ Distinguish homology, similarity, and identity, and compute a short global and a short local alignment under a stated match, mismatch, and gap score.

✓ Choose a substitution matrix for a close or a distant comparison, and interpret an E-value and a bit score, including the effect of database size on each.

✓ Explain why a progressive multiple alignment depends on its guide tree, and what a profile hidden Markov model represents that a regular expression does not.

✓ Compare distance, parsimony, maximum likelihood, and Bayesian trees by the quantity each optimises, and explain long-branch attraction, the limit of bootstrap support, and a gene tree that disagrees with the species tree.

✓ Describe shotgun assembly in terms of reads, contigs, scaffolds, repeats, coverage, long reads, and proximity linking, and contrast bacterial open-reading-frame finding with evidence-based eukaryotic annotation.

✓ Explain why expression counts need normalisation and a false-discovery correction, how a variant call is separated from a sequencing error, why a genome-wide association is not a cause, and which checks to apply before trusting a spectacular similarity or a predicted structure.

CHAPTER DIRECTORY

Dedicated Chapter & Lecture Pages (10)

Browse every individual chapter, experimental protocol, and specialized topic module published in this discipline.

10 Pages

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