This course studies discrete algorithms for solving computational biology problems and algorithmic principles driving advances in bioinformatics. The content of the course is introduced with emphasizing the ideas underlying algorithms instead of offering a collection of unrelated bioinformatics problems. In particular, the following algorithm design techniques are covered: exhaustive search, branch-and-bound algorithms, greedy algorithms, dynamic programming, divide-and-conquer algorithms, combinatorial and graph algorithms, machine learning, and randomized algorithms with respective applications in restricted mapping, regulatory motif finding, genome rearrangement, sequence alignment, multiple alignment, gene prediction, DNA sequencing, peptide sequencing, repeat finding, and phylogeney reconstruction.

• CSCI 4470/6470 Algorithms or permission of the department
  

TextBook:

An Introduction to Bioinformatics Algorithms. Jones and Pevzner. MIT Press 2004

Programming/research projects/reports: 55%
Midterm exam: 20% Final exam: 25%

Tentative schedule:

1. Chapters 1 and 2: Introduction to algorithms (1-2 weeks).
2. Chapters 4, 5, and 6: Basic algorithmic techniques) (5-6 weeks).
3. Midterm exam.
4. Chapters 7, 8, 9, and 10: Advanced algorithms (5-6 weeks).
5. Chapters 11 and 12: Machine learning and probabilistic algorithms (2-3 weeks).
6. Final exam.

It is expected that the work you submit is your own. Plagiarism and other forms of academic dishonesty will be handled within the guidelines of the Student Handbook. The usual penalty for academic dishonesty is loss of credit for the assignment in question; however, stronger measures may be taken when conditions warrant.