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Introduction to

Introduction to. Bioinformatics. Introduction to Bioinformatics. LECTURE 3: SEQUENCE ALIGNMENT * Chapter 3: All in the family. Introduction to Bioinformatics LECTURE 3: SEQUENCE ALIGNMENT. 3.1 Eye of the tiger

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Introduction to

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  1. Introduction to Bioinformatics

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

  3. Introduction to BioinformaticsLECTURE 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

  4. Eyeless Drosophila

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

  6. LECTURE 3: SEQUENCE ALIGNMENTHomeoboxes and Master regulatory genes

  7. Introduction to BioinformaticsLECTURE 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.

  8. LECTURE 3: SEQUENCE ALIGNMENTDrosophila melanogaster: HOX homeoboxes

  9. LECTURE 3: SEQUENCE ALIGNMENTDrosophila melanogaster: PAX homeoboxes

  10. LECTURE 3: SEQUENCE ALIGNMENTHomeoboxes and Master regulatory genes

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

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

  13. 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 Introduction to BioinformaticsLECTURE 3: SEQUENCE ALIGNMENT

  14. LECTURE 3: SEQUENCE ALIGNMENTHOMOLOGOUS and PARALOGOUS

  15. LECTURE 3: SEQUENCE ALIGNMENTHOMOLOGOUS and PARALOGOUS

  16. LECTURE 3: SEQUENCE ALIGNMENTHOMOLOGOUS and PARALOGOUS versus ANALOGOUS

  17. Causes for sequence (dis)similarity • mutation: a nucleotide at a certain location is replaced by • another nucleotide (e.g.: ATA → AGA) • insertion: at a certain location one new nucleotide is inserted inbetween two existing nucleotides (e.g.: AA → AGA) • deletion: at a certain location one existing nucleotide is deleted (e.g.: ACTG → AC-G) • indel: an insertion or a deletion Introduction to BioinformaticsLECTURE 3: SEQUENCE ALIGNMENT: sequence similarity

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

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

  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

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

  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.

  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.

  24. indels 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

  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.

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

  27. Alignment scoring function The cost of aligning two symbolsxiandyjis thescoring functionσ(xi,yj)

  28. Alignment cost The cost of the entire alignment:

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

  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)

  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

  32. Optimal global alignment The optimal global alignmentA* 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*

  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.

  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.

  35. Optimal local alignment The optimal local alignmentA* between two sequences s and t is the optimal global alignmentA(s(i1:i2), t(j1:j2)) of the sub-sequences s(i1:i2)and t(j1:j2) for some optimal choice of i1, i2,j1 and j2.

  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.

  37. 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. Introduction to BioinformaticsLECTURE 3: SEQUENCE ALIGNMENT

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

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

  40. Introduction to BioinformaticsLECTURE 3: SEQUENCE ALIGNMENT • 3.7 Multiple sequence alignment: • Determine the best alignment between multiple (more than two) DNA-sequences.

  41. 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.

  42. Multiple alignment

  43. Introduction to BioinformaticsLECTURE 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

  44. Dynamic Programming Approach to Sequence Alignment The dynamic programming approach tosequence 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 withmaximum matchesso 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)

  45. 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 charactersiand j.

  46. 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

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

  48. 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.

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