1. Tutorial 2: Some problems in bioinformatics 1. Alignment pairs of sequences Database searching for sequences Multiple sequence alignment Protein classification 2. Phylogeny prediction (tree construction) Sources: 1) "Bioinformatics: Sequence and Genome Analysis" by David W. Mount. 2001. Cold Spring Harbor Press 2) NCBI tutorial http://www.ncbi.nlm.nih.gov/Education/ and http://www.ncbi.nih.gov/BLAST/tutorial/Altschul-1.html 3) Brian Fristensky. Univ. of Manitoba http://www.umanitoba.ca/faculties/afs/plant_science/COURSES/bioinformatics

2. Alignment: pairs of sequences DNA: A, G, C, T protein: A, C, D, E, F, G, H, I, K, L, M, N, P, Q, R, S, T, V, W, Y KQTGKG | ||| KSAGKG TCGCA || TC-CA

4. DNA to RNA to protein to phenotype

6. Alignment: pairs of sequences Concepts: Similarity Identity Homology Orthology Paralog KQTGKGV | |||: KSAGKGL 4/7 identical 5/7 similar

7. Homology is based on evolutionary history

9. Figure 45 Lineage-specific expansions of domains and architectures of transcription factors. Top, specific families of transcription factors that have been expanded in each of the proteomes. Approximate numbers of domains identified in each of the (nearly) complete proteomes representing the lineages are shown next to the domains, and some of the most common architectures are shown. Some are shared by different animal lineages; others are lineage-specific.

11. A partial alignment of globin sequences. Proteins with very little identity (10% or less) can be recognized as sharing a common domain if they match a pattern.

14. - Fitch, W.M. 2001. Homology: A personal view of some of the problems. Trends Genet. 16: 227-231. Homology, orthology and paralogy orthologs diverged at a speciation event paralogs diverged at a gene duplication event

15. Alignment: pairs of sequences Scoring schemes Score = matches - mismatches - gaps GKG-RRWDAKR ||| || GKGAKRWESAP What is the best way to evaluate the contribution of each?

16. A partial alignment of globin sequences from Pfam. Proteins with very little identity (10% or less) can be recognized as sharing a common domain if they match a pattern.

17. Alignment: pairs of sequences Global vs. local alignment. (end gaps are ignored in local alignment)

18. Brian Fristensky. Univ. of Manitoba http://www.umanitoba.ca/faculties/afs/plant_science/COURSES/bioinformatics/lec04/lec04.2.html Dynamic programming TCGCA || TC-CA

19. Dynamic programming Brian Fristensky. Univ. of Manitoba http://www.umanitoba.ca/faculties/afs/plant_science/COURSES/bioinformatics/lec04/lec04.2.html

20. Dynamic programming Brian Fristensky. Univ. of Manitoba http://www.umanitoba.ca/faculties/afs/plant_science/COURSES/bioinformatics/lec04/lec04.2.html

21. Dynamic programming Brian Fristensky. Univ. of Manitoba http://www.umanitoba.ca/faculties/afs/plant_science/COURSES/bioinformatics/lec04/lec04.2.html

22. Alignment: pairs of sequences Scoring schemes Score = matches - mismatches - gaps GKG-RRWDAKR ||| || GKGAKRWESAP "The dynamic programming algorithm was improved in performance by Gotoh (1982) by using the linear relationship for a gap weight wx = g + rx, where the weight for a gap of length x is the sum of a gap opening penalty (g) and a gap extension penalty (r) times the gap length (x), and by simplifying the dynamic programming algorithm." D. W. Mount KQTGKG-RRWDAKR | ||| ||| KSAGKG-----AKR VS.

23. Alignment: amino acid substitution matrices Scoring schemes "Any [scoring] matrix has an implicit amino acid pair frequency distribution that characterizes the alignments it is optimized for finding. More precisely, let p i be the frequency with which amino acid i occurs in protein sequences and let q ij be the freqeuncy with which amino acids i and j are aligned within the class of alignments sought. Then, the scores that best distinguish these alignments from chance are given by the formula: S ij = log (q ij / p i p j ) The base of the logarithm is arbitrary, affecting only the scale of the scores. Any set of scores useful for local alignment can be written in this form, so a choice of substitution matrices can be viewed as an implicit choice of 'target frequencies'" - Altschul et al. 1994 (Nature Genetics 6:119) Those frequencies are characteristic of the sequences being aligned, and are primarily a function of their degree of divergence.

24. Alignment: amino acid substitution matrices Substitution matrices -- BLOSUM 62 Henikoff and Henikoff. 1992. Amino acid substitution matrices from protein blocks. PNAS 89: 10915-10919.

25. Alignment: amino acid substitution matrices Substitution matrices -- BLOSUM 62

26. Alignment: implementations Fasta Introduces the concept of k-tuple perfects alignment to seed longer global alignments. BLAST -- Basic Local Alignment Search Tool Initiates an alignment locally and then extends that alignment. GKG ||| GKG GKG-RRW ||| || GKGAKRW

27. Alignment: Searching databases for sequences

28. There are many modifications of BLAST for specific purposes.

29. The NCBI BLAST interface

32. Extreme value distribution the expected distribution of the maximum of many independent random variables, generally Y = exp [-x -e -x ] K and lambda are statistical parameters dependent upon the scoring system and the background amino acid frequencies of the sequences being compared. While FASTA estimates these parameters from the scores generated by actual database searches, BLAST estimates them beforehand for specific scoring schemes by comparing many random sequences generated using a standard protein amino acid composition [12].

34. Fasta can be run at EMBL. The software is also available for download.

37. Alignment: Multiple sequence alignment

40. Alignment: Protein classification

44. Phylogeny prediction (tree construction)

45. root

47. Phylogeny prediction (tree construction) Character-based Methods Parsimony Maximum Likelihood tree that maximizes the likelihood of seeing the data Bayesian Analysis trees with greatest likelihoods given the data Distance Methods Unweighted Gap-pair method with Arithmetic Means Neighbor joining

48. a,The interspecies relationships of five chromosome regions to corresponding DNA sequences in a chimpanzee and a gorilla. Most regions show humans to be most closely related to chimpanzees (red) whereas a few regions show other relationships (green and blue). b, The among-human relationships of the same regions are illustrated schematically for five individual chromosomes. Within- and between-species variation along a single chromosome.

49. Tutorial III: Open problems in bioinformatics Tentatively: Detection of subtle signals promoter elements exon splicing enhancers noncoding RNAs weak protein similarities Microarrays Protein folding and homology modeling Thursday, June 10, 2:00 - 3:45

50. Microarray expression data Statistical analysis -- what has changed Clustering -- which genes change together Clustering -- promoter recognition Clustering -- database integration Phenotype determination (e.g. cancer prognosis)

51. Tutorial 2: Some problems in bioinformatics 1. Alignment pairs of sequences Multiple sequence alignment Database searching for sequences Protein classification 2. Phylogeny prediction (tree construction) 3. microarray expression data 4. Protein structure Protein folding Structure prediction Homology modeling Sources: 1) "Bioinformatics: Sequence and Genome Analysis" by David W. Mount. 2001. Cold Spring Harbor Press 2) NCBI tutorial http://www.ncbi.nlm.nih.gov/Education/ 3) Cold Spring Harbor course in Computational Genomics (1999) Pearson