CSE 527 Computational Biology

Lecture notes

Lecture 1: Course logistics, short intro to molecular biology, example project topics [PPT] [PDF]

Lecture 2: Introduction to Bayesian networks for computational biology [PPT] [PDF]

Lecture 3: Maximum Likelihood Estimation, Expectation Maximization [PPT] [PDF]

Lecture 4: Genetic basics, QTL mapping, Association studies [PPT] [PDF]

Lecture 5: QTL mapping, haplotypes [PPT] [PDF]

Lecture 6: Haplotype reconstruction [PPT] [PDF]

Lecture 7: Disease association studies [PPT] [PDF]

Lecture 8: Linkage analysis [PPT] [PDF]

Lecture 9: Inferring transcriptional regulatory networks I [PDF] [PPT]

Lecture 10: Inferring transcriptional regulatory networks II [PDF] [PPT]

Lecture 11: Advanced topics in inferring regulatory networks [PDF]

Lecture 12: Regulatory motif finding I [PDF]

Lecture 13: Regulatory motif finding II [PDF]

Lecture 14: Inferring the signaling networks I [PDF]

Lecture 15: Inferring the signaling networks II [PDF]

Lecture 16: Sequence alignment [PDF]

Lecture 17: Scoring alignments [PDF]

Lecture 18: Local sequence alignment and heuristic local aligners [PDF]

Lecture 19: Multiple sequence alignment I [PDF]

Lecture 20: Multiple sequence alignment II [PDF]

Reading materials

Lecture 1: Course logistics, short intro to molecular biology, example project topics

Lecture 2-3: Machine learning basics

Lecture 4-5: QTL mapping

Lecture 6: Haplotype reconstruction

Lecture 7: Disease association studies

Lecture 8: Linkage analysis

  • The Elston-Stewart algorithm for continuous genotypes and environmental factors. Elston et al. Human Heredity 42, 16-27 (1992).

Lecture 9-11: Inferring transcriptional regulatory networks

Lecture 12-13: Regulatory motif finding

Lecture 14-15: Inferring protein-signaling networks

Lecture 16-18: Pairwise sequence alignment

  • Biological sequence analysis: probabilistic models of proteins and nucleic acids. Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison.

Lecture 19-20: Multiple sequence alignment