Bioinformatics - Michaelmas 2015
Lecturer: Dr P. Lio'
Lecture Theatre 2, WGB
News
Content
Introduction to biological data: Bioinformatics as an interesting field in computer science.
Dynamic programming. Longest common subsequence,
DNA, RNA, protein structure alignment, linear space alignment,
heuristics for multiple alignment.
Sequence database search. Blast, Patternhunter.
Next Generation Sequencing. De Bruijn graph, Burrows–Wheeler transform.
Phylogeny - parsimony-based. Fitch, Wagner, Sankoff parsimony.
Phylogeny - distance-based. UPGMA, Neighbour Joining.
Clustering. K-means, Markov Clustering algorithm.
Applications of Hidden Markov Models.
Searching motifs in sequence alignment. Gibbs sampling.
Biological networks: reverse engineering algorithms and dynamics; Wagner, Gillespie.
Example Questions and answers
Examinability
What is examinable:
- The exam questions will not focus on biological terms and concepts.
- Knowledge of applications of algorithms, complexity and assumptions are required.
- Details/steps of computation/formulas are not examinable material (just the meaning of the final formula).
What isn't examinable
- Slides 1-27, 30, 151,152, 155,156, 167,168 (only concept of gene, exon, intron, protein, gene expression)
- Slide 78 – only the concept
- Slides 179, 189, 190 – not details
- Slides 199
- Slides 227-229
- Slides 234-242
- Slides 249-258
Lectures
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Reading
Jones, Pevzner Introduction to Bioinformatics algorithms, MIT Press
Material/links
Programming: Matlab: http://www.mathworks.com/products/bioinfo/
Past exam papers