Course Code: BME355
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Synopsis

This course covers a core subject in computational biology where revolution in molecular biology and computer science has enabled high throughput analysis of tons of the genomic sequences generated from sequencing projects. Topics include the computational methods and algorithms for analysing and disseminating genomic information.
Level: 3
Credit Units: 5
Presentation Pattern: EVERY JULY

Topics

  • Introduction to Molecular Genetics
  • Molecular Genetics and Databases
  • Pairwise Sequence Alignment
  • Database Searching with BLAST and FASTA
  • Advanced BLAST Searching
  • Multiple Sequence Alignment
  • Introduction to AI and Machine Learning in Bioinformatics
  • AI for Gene Function Prediction (domain-based + AI-enhanced)
  • AI-based Protein Structure Prediction (ESMFold / AlphaFold)
  • AI for Functional Genomics & Variant Effect Prediction
  • AI-Driven Multi-Omics Integration (Conceptual)
  • Ethical, Practical and Future Applications of AI in Genomics

Learning Outcome

  • Apply core concepts of molecular genetics and computational biology to interpret biological sequence data.
  • Analyse genomic sequence organisation and retrieve relevant data from open -access databases such as GenBank and Ensembl.
  • Examine protein and DNA sequence alignment methods, including scoring matrices and global vs. local alignment tools, in an evolutionary context.
  • Evaluate homology-based search tools, multiple sequence alignment algorithms & genome-wide analysis tools to formulate solutions to biological research problems.
  • Solve complex research questions using multiple online bioinformatics tools introduced in this course.
  • Discuss and evaluate the application of AI and machine learning approaches in genomics, including gene function prediction, protein structure modelling, variant analysis, and the ethical implications of their use.
Part-time Programmes / CET Courses