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1. to learn about some basic problems and algorithms behind common bioinformatics applications (alignment, sequence similarity, phylogenetics), and 2. to get an idea of some basic computational issues (complexity, efficiency, limitations).
Introduction to algorithm analysis: time and storage space analysis; notation for complexity analysis: Big-Oh-notation, growth of functions; formalism on strings/sequences; basic combinatorics on strings;
Applications; Pairwise sequence alignment: Exhaustive search, Dynamic programming (DP) algorithm of Needleman-Wunsch (global alignment), DP algorithm of Smith-Waterman (local alignment), other variants; multiple sequence alignment; Scoring matrices: PAM (computation, application); Heuristics for sequence alignment and database search: dotplots, q-grams, FASTA, BLAST;
Introduction to graphs and trees; number of phylogenetic trees; distance-based data: UPGMA; character-based data: Perfect Phylogeny (PP), Small parsimony: Fitch' algorithm; Large parsimony: heuristics.
Written exam and presentation of a project.
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