Lecture Bioinformatics Algorithms


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Autumn/Winter 2008/2009

Introduction


Algorithms have become an essential tool for understanding biological data. Starting with the Needleman-Wunsch and Smith-Waterman alignment algorithms, putting biological problems into algorithmic models has become essential in many other applications, ranging from whole genome sequencing and gene finding up to structure prediction for biomolecules and the reconstruction of phylogenies.
This lecture introduces some of the most important algorithms used in today’s bioinformatics tools. The course will be accompanied by excercises and tutorials for both theory and practice.

Schedule

  1. BASICS: Algorithms and Complexity [1]
  2. Pairwise Sequence Comparison and Dynamic Programming [1]
  3. Multiple Sequence Alignments and Motif Finding [1]
  4. Hierarchical Clustering; Phylogenetic Trees and Networks [4]
  5. Advanced Methods of Phylogeny Reconstruction [1,2,4]
  6. RNA structure prediction and comparison [2]
  7. Whole Genome Sequencing [1]
  8. Suffix Trees [4]
  9. BASICS: Probability [2]
  10. Hidden Markov Models [2]
  11. Protein Gene Finding and Threading [3]
  12. Covariance Models [2]
  13. A Snapshot of Microarray Data Analysis [4,5]

Literature


Books:
[1] Bioinformatics Algorithms by Pavel Pevzner
[2] Biological Sequence Analysis by Sean Eddy and Richard Durbin
[3] Computational Molecular Biology by Pavel Pevzner

External Lecture Notes:
[4] Algorithms in Molecular Biology by Ron Shamir
[5] Analysis of Gene Expression, DNA Chips and Gene Networks by Ron Shamir