1. Introduction to Bioinformatics Biostatistics & Medical Informatics 576 Computer Sciences 576 Fall 2008 Colin Dewey Dept. of Biostatistics & Medical Informatics Dept. of Computer Sciences cdewey@biostat.wisc.edu www.biostat.wisc.edu/bmi576/

2. BMI/CS 576: Bioinformatics instructor: Prof. Colin Dewey –cdewey@biostat.wisc.edu office hours: TBA Room 2120 Genetics/Biotech Room 6720, Medical Sciences Center course home page: www.biostat.wisc.edu/bmi576/

3. Finding My Office

4. Course TA Bo Li –bli25@wisc.edubli25@wisc.edu –???? Computer Sciences (1210 W. Dayton St.) –Office hours: TBA

5. Expected Background CS 367 (Intro to Data Structures) or equivalent statistics: good if you’ve had at least one course, but not required molecular biology: no knowledge assumed, but an interest in learning some basic molecular biology is mandatory

6. Course Emphases understanding the types and sources of data available for computational biology understanding the important computational problems in molecular biology  understanding the most significant & interesting algorithms

7. Course Requirements 5 or so homework assignments: ~40% –mostly programming (in Java) –computational experiments (e.g. measure the effect of varying parameter x in algorithm y) –some written exercises midterm exam: ~25% final exam: ~ 35%

8. Computing Resources for the Class UNIX workstations in Dept. of Biostatistics & Medical Informatics –no “lab”, must log in remotely –accounts will be created later this week –two machines mi1.biostat.wisc.edu mi2.biostat.wisc.edu CS department offers UNIX orientation sessions on September 3 rd, 4 th, & 8 th at 4pm in Computer Sciences 1221 the “CS 1000” UNIX tutorial http://www.cs.wisc.edu/csl/cs1000/

9. Course Readings Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. R. Durbin, S. Eddy, A. Krogh, and G. Mitchison. Cambridge University Press, 1998. articles from the primary literature (scientific journals, etc.)

10. The Short-term Plan Friday (9/7) and Monday (9/10) –optional “Molecular Biology 101” lectures Wednesday (9/12) –start on “Pairwise Sequence Alignment”

11. Reading Assignment Life and Its Molecules: A Brief Introduction. L. Hunter (available from web page) Sections 2.1-2.4 in Durbin et al.

12. What is Bioinformatics representation/storage/retrieval/analysis of biological data concerning –sequences –structures –functions –activity levels –networks of interactions of/among biomolecules sometimes used synonymously with computational biology or computational molecular biology

13. What do two sequences/genomes have in common?

14. Sequence Alignment Topics pairwise and multiple sequence alignment dynamic programming methods for global and local alignments linear and affine gap penalty functions the BLAST algorithm dynamic programming and heuristic methods for multiple sequence alignment alignment statistics and substitution matrices

15. Where are the genes in this genome?

16. Probabilistic Sequence Model Topics Markov chains high-order Markov models inhomogeneous Markov models hidden Markov models Forward/Backward/Viterbi algorithms applications to gene finding and motif modeling

17. How are these species related?

18. Phylogenetic Tree Inference Topics distance-based approaches parsimony-based approaches branch-and-bound search multiple sequence alignment

19. Can diseases be characterized by patterns of gene activity?

20. Topics in Data Analysis from High-Throughput Experiments clustering algorithms hierarchical clustering k-means clustering EM-based clustering classification algorithms (simple methods for supervised learning) multiple hypothesis testing and the false discovery rate

21. Can we induce models of cellular processes from high-throughput experiments? cellmodel

22. Network Modeling Topics Bayesian network representations algorithms for exact inference in BNs algorithms for structure and parameter learning in BNs Module networks

23. What does the protein encoded by a given gene look like? What does it do?

24. Protein Structure Prediction Topics classification methods revisited threading algorithms the Rosetta system