1. Introduction to bioinformatics (I617) Haixu Tang School of Informatics Email: hatang@indiana.eduhatang@indiana.edu Office: EIG 1008 Tel: 812-856-1859

2. Textbook A Primer of Genome Science (2nd Edition) by Greg Gibson, Spencer V. Muse, Sinauer Associates, 2004 Suggested reading materials will be posted on the class wiki page: http://cheminfo.informatics.indiana.edu/djwild/I61 7_2006_wiki/index.php/Main_Page http://cheminfo.informatics.indiana.edu/djwild/I61 7_2006_wiki/index.php/Main_Page Office Hour: MW 11:00-12:00, EIG 1008 or appointment

3. Grading Class project: selected from one of four covered areas (bioinformatics, Chemical informatics, Laboratory informatics and Health informatics) 25% –Suggested Bioinformatics topics will be posted on the class wiki page Homework: 25% in Bioinformatics –4, each 6.25%

4. Bioinformatics = BIOlogy + informatics? Not really: it is a term (somehow arbitrarily chosen) to define a multi-disciplinary area that combines life sciences, physical sciences and computer science / informatics; It addresses biological problems using theoretical informatics approaches, not vice versa; It is transforming classical Biology into a Information Science.

5. The birth of bioinformatics A revolution in biology research: the emergence of Genome Science Technology advancement in both biology and information science

6. Genome science: a revolution of biology Classical BiologyGenome Science Hypothesis Data Knowledge Hypothesis driven approach Hypothesis Knowledge Data Data driven approach

7. Bioinformatics: from data analysis to data mining Hypothesis Data Classical Biology Low throughput data Genome Science Hypothesis Data High throughput data Hypothesis confirmation / rejection Hypothesis generation 1 2 3 …

8. Bioinformatics: in the driver’s seat Classical Biology Hypothesis Data Knowledge Genome Science Hypothesis Knowledge Data Data analysis Data mining

9. Key technology advancements High throughput biotechnologies –Genome sequencing techniques –DNA microarray –Mass spectrometry Large-scale experiments –HGP, HapMap –Omics / Systems Biology Massive data generation, storage, exchange and analysis –CPU, storage, etc. –High speed network (Internet) –Bioinformatics

10. Bioinformatics: mutually beneficial For biologists –Fragment assembly in genome sequencing –Genome comparison –Gene clustering in DNA microarray analysis –Protein identification in proteomics For computer scientists –String algorithms / Tree algorithms –Alternative Eulerian path (BEST theorem) –Reversal distances –Probabilistic graphic models (HMMs, BNs, etc.)

11. Two origins of bioinformatics Combinatorial pattern matching in theoretical computer science –DNA and protein sequence analysis Physical and analytical chemistry of Biomolecules –Protein structure analysis  Structural bioinformatics –Bio-analytical chemistry  Proteomics

12. Bioinformatics addresses computational challenges in life and medical sciences New computational problems for automatic data analysis Reformulation of old problems using new high throughput data Formulating new problems using high throughput data

13. Bioinformatics addresses computational challenges in life and medical sciences New computational problems for automatic data analysis Genome sequencing Proteomics Transcriptomics Data representation and visualization Genome Browser Solving biological problems by in silico approaches –Reformulation of old problems using new high throughput data Gene finding Protein structure and function –Formulating new problems using high throughput data Comparative genomics Polymorphisms / Population genetics Systems Biology

14. Bioinformatics resources Databases –Nucleic Acid Research (NAR) annual database issue Organization –ISCB (International Society in Computational Biology) Conferences –ISMB –RECOMB –Many other smaller or regional conferences, e.g. ECCB, CSB, PSB, etc, including local Indiana Bioinformatics conference

15. A case study How bioinformatics help and transform classical biological topics? Molecular evolutionary studies: from anatomical features to molecular evidences Genome evolution: comparison of gene orders

16. Early Evolutionary Studies Anatomical features were the dominant criteria used to derive evolutionary relationships between species since Darwin till early 1960s

17. Early Evolutionary Studies Anatomical features were the dominant criteria used to derive evolutionary relationships between species since Darwin till early 1960s The evolutionary relationships derived from these relatively subjective observations were often inconclusive. Some of them were later proved incorrect

18. Evolution and DNA Analysis: the Giant Panda Riddle For roughly 100 years scientists were unable to figure out which family the giant panda belongs to Giant pandas look like bears but have features that are unusual for bears and typical for raccoons, e.g., they do not hibernate

19. Evolution and DNA Analysis: the Giant Panda Riddle In 1985, Steven O’Brien and colleagues solved the giant panda classification problem using DNA sequences and bioinformatics algorithms

20. Evolutionary Tree of Bears and Raccoons

21. Evolutionary Trees: DNA-based Approach 40 years ago: Emile Zuckerkandl and Linus Pauling brought reconstructing evolutionary relationships with DNA into the spotlight In the first few years after Zuckerkandl and Pauling proposed using DNA for evolutionary studies, the possibility of reconstructing evolutionary trees by DNA analysis was hotly debated Now it is a dominant approach to study evolution.

22. Evolutionary Trees How are these trees built from DNA sequences?

23. Evolutionary Trees How are these trees built from DNA sequences? –leaves represent existing species –internal vertices represent ancestors –root represents the common evolutionary ancestor

24. Rooted and Unrooted Trees In the unrooted tree the position of the root (“common ancestor”) is unknown. Otherwise, they are like rooted trees

25. Distances in Trees Edges may have weights reflecting: –Number of mutations on evolutionary path from one species to another –Time estimate for evolution of one species into another In a tree T, we often compute d ij (T) - the length of a path between leaves i and j d ij (T) – tree distance between i and j

26. Distance in Trees: an Exampe d 1,4 = 12 + 13 + 14 + 17 + 12 = 68 i j

27. Distance Matrix Given n species, we can compute the n x n distance matrix D ij D ij may be defined as the edit distance between a gene in species i and species j, where the gene of interest is sequenced for all n species. D ij – edit distance between i and j

28. Fitting Distance Matrix Given n species, we can compute the n x n distance matrix D ij Evolution of these genes is described by a tree that we don’t know. We need an algorithm to construct a tree that best fits the distance matrix D ij

29. Reconstructing a 3 Leaved Tree Tree reconstruction for any 3x3 matrix is straightforward We have 3 leaves i, j, k and a center vertex c Observe: d ic + d jc = D ij d ic + d kc = D ik d jc + d kc = D jk

30. Turnip vs Cabbage: Look and Taste Different Although cabbages and turnips share a recent common ancestor, they look and taste different

31. Turnip vs Cabbage: Comparing Gene Sequences Yields No Evolutionary Information

32. Turnip vs Cabbage: Almost Identical mtDNA gene sequences In 1980s Jeffrey Palmer studied evolution of plant organelles by comparing mitochondrial genomes of the cabbage and turnip 99% similarity between genes These surprisingly identical gene sequences differed in gene order This study helped pave the way to analyzing genome rearrangements in molecular evolution

33. Turnip vs Cabbage: Different mtDNA Gene Order Gene order comparison: Before After Evolution is manifested as the divergence in gene order

34. Turnip vs Cabbage: Different mtDNA Gene Order Gene order comparison:

35. Turnip vs Cabbage: Different mtDNA Gene Order Gene order comparison:

36. Turnip vs Cabbage: Different mtDNA Gene Order Gene order comparison:

37. Turnip vs Cabbage: Different mtDNA Gene Order Gene order comparison:

38. Transforming Cabbage into Turnip Reversal distance

39. History of Chromosome X Rat Consortium, Nature, 2004