2. 1 Introduction to Bioinformatics

3. 2 Introduction to Bioinformatics. LECTURE 3: SEQUENCE ALIGNMENT * Chapter 3: All in the family

4. 3 Introduction to Bioinformatics LECTURE 3: SEQUENCE ALIGNMENT 3.1 Eye of the tiger * In 1994 Walter Gehring et alum (Un. Basel) turn the gene “eyeless” on in various places on Drosophila melanogaster * Result: on multiple places eyes are formed * ‘eyeless’ is a master regulatory gene that controls +/- 2000 other genes * ‘eyeless’ on induces formation of an eye

5. 4 Eyeless Drosophila

6. 5 Mutant Drosophila melanogaster: gene ‘EYELESS’ turned on

7. 6 LECTURE 3: SEQUENCE ALIGNMENT Homeoboxes and Master regulatory genes

8. 7 Introduction to Bioinformatics LECTURE 3: SEQUENCE ALIGNMENT HOMEO BOX A homeobox is a DNA sequence found within genes that are involved in the regulation of development (morphogenesis) of animals, fungi and plants.

9. 8 LECTURE 3: SEQUENCE ALIGNMENT Drosophila melanogaster: HOX homeoboxes

10. 9 LECTURE 3: SEQUENCE ALIGNMENT Drosophila melanogaster: PAX homeoboxes

11. 10 LECTURE 3: SEQUENCE ALIGNMENT Homeoboxes and Master regulatory genes

12. 11 Introduction to Bioinformatics LECTURE 3: SEQUENCE ALIGNMENT 3.2 On sequence alignment Sequence alignment is the most important task in bioinformatics!

13. 12 Introduction to Bioinformatics LECTURE 3: SEQUENCE ALIGNMENT 3.2 On sequence alignment Sequence alignment is important for: * prediction of function * database searching * gene finding * sequence divergence * sequence assembly

14. 13 Introduction to Bioinformatics LECTURE 3: SEQUENCE ALIGNMENT 3.3 On sequence similarity Homology: genes that derive from a common ancestor-gene are called homologs Orthologous genes are homologous genes in different organisms Paralogous genes are homologous genes in one organism that derive from gene duplication Gene duplication: one gene is duplicated in multiple copies that therefore free to evolve and assume new functions

15. 14 LECTURE 3: SEQUENCE ALIGNMENT HOMOLOGOUS and PARALOGOUS

16. 15 LECTURE 3: SEQUENCE ALIGNMENT HOMOLOGOUS and PARALOGOUS

17. 16 LECTURE 3: SEQUENCE ALIGNMENT HOMOLOGOUS and PARALOGOUS versus ANALOGOUS

18. 17 Introduction to Bioinformatics LECTURE 3: SEQUENCE ALIGNMENT: sequence similarity Causes for sequence (dis)similarity mutation: a nucleotide at a certain location is replaced by another nucleotide (e.g.: A T A → A G A) insertion: at a certain location one new nucleotide is inserted inbetween two existing nucleotides (e.g.: AA → A G A) deletion: at a certain location one existing nucleotide is deleted (e.g.: AC T G → AC - G) indel: an in sertion or a del etion

19. 18 Introduction to Bioinformatics LECTURE 3: SEQUENCE ALIGNMENT 3.4 Sequence alignment: global and local Find the similarity between two (or more) DNA-sequences by finding a good alignment between them.

20. 19 The biological problem of sequence alignment DNA-sequence-1 tcctctgcctctgccatcat---caaccccaaagt |||| ||| ||||| ||||| |||||||||||| tcctgtgcatctgcaatcatgggcaaccccaaagt DNA-sequence-2 Alignment

21. 20 Sequence alignment - definition Sequence alignment is an arrangement of two or more sequences, highlighting their similarity. The sequences are padded with gaps (dashes) so that wherever possible, columns contain identical characters from the sequences involved tcctctgcctctgccatcat---caaccccaaagt |||| ||| ||||| ||||| |||||||||||| tcctgtgcatctgcaatcatgggcaaccccaaagt

22. 21 Algorithms Needleman-Wunsch Pairwise global alignment only. Smith-Waterman Pairwise, local (or global) alignment. BLAST Pairwise heuristic local alignment

23. 22 Pairwise alignment Pairwise sequence alignment methods are concerned with finding the best- matching piecewise local or global alignments of protein (amino acid) or DNA (nucleic acid) sequences. Typically, the purpose of this is to find homologues (relatives) of a gene or gene- product in a database of known examples. This information is useful for answering a variety of biological questions: 1. The identification of sequences of unknown structure or function. 2. The study of molecular evolution.

24. 23 Global alignment A global alignment between two sequences is an alignment in which all the characters in both sequences participate in the alignment. Global alignments are useful mostly for finding closely-related sequences. As these sequences are also easily identified by local alignment methods global alignment is now somewhat deprecated as a technique. Further, there are several complications to molecular evolution (such as domain shuffling) which prevent these methods from being useful.

25. 24 Global Alignment Find the global best fit between two sequences Example: the sequences s = VIVALASVEGAS and t = VIVADAVIS align like: A(s,t) = V I V A L A S V E G A S | | | | | | | V I V A D A - V - - I S indels

26. 25 The Needleman-Wunsch algorithm The Needleman-Wunsch algorithm (1970, J Mol Biol. 48(3):443-53) performs a global alignment on two sequences (s and t) and is applied to align protein or nucleotide sequences. The Needleman-Wunsch algorithm is an example of dynamic programming, and is guaranteed to find the alignment with the maximum score.

27. 26 The Needleman-Wunsch algorithm Of course this works for both DNA-sequences as for protein-sequences.

28. 27 Alignment scoring function The cost of aligning two symbols x i and y j is the scoring function σ(x i,y j )

29. 28 Alignment cost The cost of the entire alignment:

30. 29 A simple scoring function σ(-,a) = σ(a,-) = -1 σ(a,b) = -1 if a ≠ b σ(a,b) = 1 if a = b

31. 30 The substitution matrix A more realistic scoring function is given by the biologically inspired substitution matrix : - A G C T A 10 -1 -3 -4 G -1 7 -5 -3 C -3 -5 9 0 T -4 -3 0 8 Examples: * PAM (Point Accepted Mutation) (Margaret Dayhoff) * BLOSUM (BLOck SUbstitution Matrix) (Henikoff and Henikoff)

32. 31 Scoring function The cost for aligning the two sequences s = VIVALASVEGAS and t = VIVADAVIS : A(s,t) = is: M(A)= 7 matches + 2 mismatches + 3 gaps = 7 – 2 – 3 = 2 V I V A L A S V E G A S | | | | | | | V I V A D A - V - - I S

33. 32 Optimal global alignment The optimal global alignment A* between two sequences s and t is the alignment A(s,t) that maximizes the total alignment score M(A) over all possible alignments. A* = argmax M(A) Finding the optimal alignment A* looks a combinatorial optimization problem: i. generate all possible allignments ii. compute the score M iii. select the alignment A* with the maximum score M*

34. 33 Local alignment Local alignment methods find related regions within sequences - they can consist of a subset of the characters within each sequence. For example, positions 20-40 of sequence A might be aligned with positions 50-70 of sequence B. This is a more flexible technique than global alignment and has the advantage that related regions which appear in a different order in the two proteins (which is known as domain shuffling) can be identified as being related. This is not possible with global alignment methods.

35. 34 The Smith Waterman algorithm The Smith-Waterman algorithm (1981) is for determining similar regions between two nucleotide or protein sequences. Smith-Waterman is also a dynamic programming algorithm and improves on Needleman-Wunsch. As such, it has the desirable property that it is guaranteed to find the optimal local alignment with respect to the scoring system being used (which includes the substitution matrix and the gap- scoring scheme). However, the Smith-Waterman algorithm is demanding of time and memory resources: in order to align two sequences of lengths m and n, O(mn) time and space are required. As a result, it has largely been replaced in practical use by the BLAST algorithm; although not guaranteed to find optimal alignments, BLAST is much more efficient.

36. 35 Optimal local alignment The optimal local alignment A* between two sequences s and t is the optimal global alignment A(s(i 1 :i 2 ), t(j 1 :j 2 ) ) of the sub-sequences s(i 1 :i 2 ) and t(j 1 :j 2 ) for some optimal choice of i 1, i 2, j 1 and j 2.

37. 36 Sequence alignment - meaning Sequence alignment is used to study the evolution of the sequences from a common ancestor such as protein sequences or DNA sequences. Mismatches in the alignment correspond to mutations, and gaps correspond to insertions or deletions. Sequence alignment also refers to the process of constructing significant alignments in a database of potentially unrelated sequences.

38. 37 Introduction to Bioinformatics LECTURE 3: SEQUENCE ALIGNMENT 3.5 Statistical analysis of alignments This works identical to gene finding: * Generate randomized sequences based on the second string * Determine the optimal alignments of the first sequence with these randomized sequences * Compute a histogram and rank the observed score in this histogram * The relative position defines the p-value.

39. 38 Introduction to Bioinformatics LECTURE 3: SEQUENCE ALIGNMENT: statistical analysis Histogram of scores of randomly generated strings using permutation of original sequence t original sequence s

40. 39 Introduction to Bioinformatics LECTURE 3: SEQUENCE ALIGNMENT 3.6 BLAST: fast approximate alignment Fast but heuristic Most used algorithm in bioinformatics Verb: to blast

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43. 42 Introduction to Bioinformatics LECTURE 3: SEQUENCE ALIGNMENT 3.7 Multiple sequence alignment: Determine the best alignment between multiple (more than two) DNA-sequences.

44. 43 Multiple alignment Multiple alignment is an extension of pairwise alignment to incorporate more than two sequences into an alignment. Multiple alignment methods try to align all of the sequences in a specified set. The most popular multiple alignment tool is CLUSTAL. Multiple sequence alignment is computationally difficult and is classified as an NP-Hard problem.

45. 44 Multiple alignment

46. 45 Introduction to Bioinformatics LECTURE 3: SEQUENCE ALIGNMENT 3.8 Computing the alignments * NW and SW are both based on Dynamic Programming (DP) * A recursive relation breaks down the computation

47. 46 Dynamic Programming Approach to Sequence Alignment The dynamic programming approach to sequence alignment always tries to follow the best prior-result so far. Try to align two sequences by inserting some gaps at different locations, so as to maximize the score of this alignment. Score measurement is determined by "match award", "mismatch penalty" and "gap penalty". The higher the score, the better the alignment. If both penalties are set to 0, it aims to always find an alignment with maximum matches so far. Maximum match = largest number matches can have for one sequence by allowing all possible deletion of another sequence. It is used to compare the similarity between two sequences of DNA or Protein, to predict similarity of their functionalities. Examples: Needleman-Wunsch(1970), Sellers(1974), Smith-Waterman(1981)

48. 47 The Needleman-Wunsch algorithm The Needleman-Wunsch algorithm (1970, J Mol Biol. 48(3):443-53) performs a global alignment on two sequences (A and B) and is applied to align protein or nucleotide sequences. The Needleman-Wunsch algorithm is an example of dynamic programming, and is guaranteed to find the alignment with the maximum score. Scores for aligned characters are specified by the transition matrix σ (i,j) : the similarity of characters i and j.

49. 48 The Needleman-Wunsch algorithm For example, if the substitution matrix was - A G C T A 10 -1 -3 -4 G -1 7 -5 -3 C -3 -5 9 0 T -4 -3 0 8 with a gap penalty of -5, would have the following score... then the alignment: AGACTAGTTAC CGA---GACGT

50. 49 The Needleman-Wunsch algorithm 1.Create a table of size (m+1)x(n+1) for sequences s and t of lengths m and n, 2.Fill table entries (m:1) and (1:n) with the values: 3.Starting from the top left, compute each entry using the recursive relation: 4.Perform the trace-back procedure from he bottom-right corner

51. 50 The Needleman-Wunsch algorithm Once the F matrix is computed, note that the bottom right hand corner of the matrix is the maximum score for any alignments. To compute which alignment actually gives this score, you can start from the bottom left cell, and compare the value with the three possible sources(Choice1, Choice2, and Choice3 above) to see which it came from. If it was Choice1, then A(i) and B(i) are aligned, if it was Choice2 then A(i) is aligned with a gap, and if it was Choice3, then B(i) is aligned with a gap.

52. 51 The Needleman-Wunsch algorithm

53. 52

54. 53 The Smith-Waterman algorithm 1.Create a table of size (m+1)x(n+1) for sequences s and t of lengths m and n, 2.Fill table entries (1,1:m+1) and (1:n+1,1) with zeros. 3.Starting from the top left, compute each entry using the recursive relation: 4.Perform the trace-back procedure from the maximum element in the table to the first zero element on the trace-back path.

55. 54 Introduction to Bioinformatics LECTURE 3: SEQUENCE ALIGNMENT EXAMPLE: Eyeless Gene Homeobox Compare the gene eyeless of Drosophila Melanoganster with the human gene aniridia. They are master regulatory genes producing proteins that control large cascade of other genes. Certain segments of genes eyeless of Drosophila melanogaster and human aniridia are almost identical. The most important of such segments encodes the PAX (paired-box) domain, a sequence of 128 amino acids whose function is to bind specific sequences of DNA. Another common segment is the HOX (homeobox) domain that is thougth to be part of more than 0.2% of the total nummber of vertebrate genes.

56. 55 Introduction to Bioinformatics LECTURE 3: SEQUENCE ALIGNMENT

57. 56 Introduction to Bioinformatics LECTURE 3: GLOBAL ALIGNMENT

58. 57 Introduction to Bioinformatics LECTURE 3: GLOBAL ALIGNMENT

59. 58 Introduction to Bioinformatics LECTURE 3: SEQUENCE ALIGNMENT

60. 59 END of LECTURE 3

61. 60 Introduction to Bioinformatics LECTURE 3: SEQUENCE ALIGNMENT

62. 61