Who, Where, When

Course Outline

• A primer in biology; computational problems arising in biology (week 1)
• Sequence alignment for DNA and protein sequences (weeks 2 – 4)
  • Concepts: homology, sequence similarity and sequence alignment; dynamic programming algorithms
  • Pairwise alignment
  • Global and local alignment using dynamic programming
  • Heuristic alignment methods: BLAST/FASTA and the statistics of local alignments
  • Multiple sequence alignment
    • Definition, scoring, techniques
    • Aligners for proteins sequences
    • Spliced alignment
• Motif finding in DNA and proteins (week 5)
• Hidden Markov models (HMMs) (week 6 – 8)
  • The basic HMM algorithms: forward, backward, Viterbi, Baum-Welch
  • Applications: CpG islands, gene finding, profile HMMs, pair HMMs
• Genome assembly (week 9)
• Analysis of high-throughput sequencing data (week 10)
• Phylogenetic analysis (week 11 – 12)
  • Why phylogeny?
  • Neighbor joining, parsimony, and maximum likelihood methods
• Comparative genomics: gene regulation, gene finding, genome rearrangements (week 13)
• High throughput biological data: microarrays, mass spectrometry, and protein-protein interactions (week 14)
• Student project presentations and class summary (week 15)

Prerequisites:

Some background in statistics and ability to program (R and matlab are good enough)

Textbook:

Richard Durbin, Sean R. Eddy, Anders. Krogh and Graeme Mitchison. Biological Sequence Analysis. Cambridge UP, 1998.

Also useful:

Neil C. Jones and Pavel A. Pevzner
An introduction to bioinformatics algorithms
MIT Press, 2004

Grading: