1. Introduction to sequence alignment Mike Hallett (David Walsh)
WEEK 2
Mike Hallett (David Walsh)
BIOL510: Bioinformatics
Alignment of dog genome to the human genome
Like to start with some general comments before we begin

2. Outline: pairwise alignment
The importance of pairwise alignment
The important steps in comparing two sequences (Sections )
Performing pairwise alignments using BLAST
(Section ): WILL COVER IN LAB CLASS ON TUESDAY

3. 4.1 Principles of sequence alignment
Sequences (DNA and protein) vary as a result of evolutionary processes acting at the molecular level:
Point mutations: nucleotides or amino acids
Insertions and deletions (length variation)
Fusion of two genes into a single gene
Evolution in gene sequences can effectively mask any underlying sequence similarity
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4. Similarity and Homology
Similarity is quantitative measure of how related two sequences are:
Usually based on pairwise alignment of two sequences
By aligning sequences we can count the number of residues that line up and be expressed in terms of percent identity
High degrees of sequence similarity may imply a common evolutionary history or a possible commonality in biological function
Homology refers specifically to similarity in sequence or structure due to decent from a common ancestor
The concept of homology implies an evolutionary relationship

5. Definition: homology Homology
Similarity attributed to descent from a common ancestor.
Morphological homology
Molecular homology
fly GAKKVIISAP SAD.APM..F
human GAKRVIISAP SAD.APM..F
plant GAKKVIISAP SAD.APM..F
bacterium GAKKVVMTGP SKDNTPM..F
yeast GAKKVVITAP SS.TAPM..F
archaeon GADKVLISAP PKGDEPVKQL

6. Definitions: identity, similarity, conservation
Identity The extent to which two (nucleotide or amino acid) sequences are invariant.
Similarity
The extent to which nucleotide or protein sequences are related. It is based upon identity plus conservation.
Conservation
Changes at a specific position of an amino acid or (less commonly, DNA) sequence that preserve the physico-chemical properties of the original residue.

7. Pairwise sequence alignment is the most
fundamental operation of bioinformatics
• It is fundamental to characterizing genome sequences
To identify genes within a genome
To identify related proteins, predict protein structure and function
To construct phylogenetic trees and compare evolutionary relationships
P. 72

8. Definition: pairwise alignment
Pairwise alignment: The process of lining up two sequences to achieve maximal levels of similarity
Query: 181 catcaactacaactccaaagacacccttacacccactaggatatcaacaaacctacccac 240
|||||||| |||| |||||| ||||| | |||||||||||||||||||||||||||||||
Sbjct: 189 catcaactgcaaccccaaagccacccct-cacccactaggatatcaacaaacctacccac 247

9. 4.5 Types of alignment
Global alignments: an alignment that covers the full length of a gene or protein sequence
 for aligning closely related sequences that are similar over their whole length

10. 4.5 Types of alignment
Global alignments: an alignment that covers the full length of a gene or protein sequence
 for aligning closely related sequences that are similar over their whole length
Local alignments: an alignment that only covers a certain region (e.g. domain) of a gene or protein sequences
 for aligning proteins that are only partly related (e.g multidomain proteins)
 for identifying conserved regions in very divergent sequences

11. 4.5 Types of alignment

12. 4.5 Types of alignment

13. General approach to pairwise alignment
Choose two sequences
Select an alignment algorithm that generates a score
Score reflects degree of similarity
Allow gaps (insertions, deletions)
Estimate probability that the alignment
occurred by chance
Many possible alignments

14. 4.1 Principles of sequence alignment
When sequences are derived from a common ancestor, we want to align bases/amino acids derived from the same ancestral position
b-corticotropin (sheep)
Corticotropin A (pig)
ala gly glu asp asp glu
asp gly ala glu asp glu
Oxytocin
Vasopressin
CYIQNCPLG
CYFQNCPRG
(Nueromodulators)
(Peptide Hormones)

15. 4.1 Principles of sequence alignment
When sequences are derived from a common ancestor, we want to align bases/amino acids derived from the same ancestral position
T H I S S E Q E N C E
T H A T S E Q E N C E
Two amino acid point mutations
Identical matches
Mismatches
P. 73

16. 4.1 Principles of sequence alignment
Often sequences we wish to align will differ in length, obscuring the similarity that exists:
T H I S I S A S E Q E N C E
T H A T S E Q E N C E
How many amino acid point mutations?
 8 point mutations?
Identical matches
Mismatches
P. 73

17. 4.1 Principles of sequence alignment
Insertion/deletion mutations result in gaps in an alignment
T H I S I S A - S E Q E N C E
T H A T S E Q E N C E
How many amino acid point mutations?
 0 point mutations?
 but two indel mutations!
Identical matches
Mismatches
The best pairwise alignment is not obvious, hence we have algorithms for testing different alignments quantitatively
P. 74

18. Matches do not have to be identical
Certain amino acids resemble each other in their physical and chemical characteristics, and can substitute functionally for each other
T H I S I S A S E Q E N C E
isoleucine - alanine
serine - threonine
T H A T S E Q E N C E

19. Charged amino acids
Polar uncharged amino acids
Hydrophobic amino acids

20. Pairwise alignment: protein versus DNA sequences
Synonymous mutations alter DNA but not amino acid sequences
Nonsynonymous mutations alter amino acid sequence
• Protein sequences offer a longer “look-back” time
DNA sequences can be translated into protein,
and then used in pairwise alignments

21. Codons are degenerate: changes in the third position
often do not alter the amino acid that is specified

22. DNA alignments
Many times, DNA alignments are appropriate (or necessary):
-- to identify promoters and regulatory elements
-- to identify gene sequences
-- to study noncoding regions of DNA
-- to study DNA polymorphisms (SNPs)
Query: 181 catcaactacaactccaaagacacccttacacccactaggatatcaacaaacctacccac 240
|||||||| |||| |||||| ||||| | |||||||||||||||||||||||||||||||
Sbjct: 189 catcaactgcaaccccaaagccacccct-cacccactaggatatcaacaaacctacccac 247

23. 4.2 Scoring alignments
How do we objectively determine which is the best possible alignment for a pair of sequences?

24. 4.2 Scoring alignments
How do we objectively determine which is the best possible alignment for a pair of sequences?
 Generate all possible alignments
(not possible: 1075 possibilities for an alignment of 100 positions!!!)
 Calculate a score for each alignment
Optimal alignment: the alignment with the best score
Suboptimal alignments: alignments with slightly poorer scores

25. 4.2 Scoring alignments: Percent identity
The simplest way to quantify similarity is to sum the number of bases/amino acid matches and divide by length of the alignment
T H I S I S A - S E Q E N C E
T H A T S E Q E N C E
(10 matches/15 positions)*100 = 66% identity

26. 4.2 Scoring alignments: dot plots
Dot-plots are a simple way to visualize pairwise sequence similarity
Fig 4.1

27. Matches do not have to be identical
Do all amino acid substitutions occur with the same probability?

28. Matches do not have to be identical
Do all amino acid substitutions occur with the same probability? NO!!!!
T H I S I S A S E Q E N C E
T H A T S E Q E N C E
serine – threonine : highly conservative
isoleucine – alanine : poorly conservative

29. Substitution Matrix
A substitution matrix contains the likelihood that a particular pair of amino acids will occupy the same position due to decent from a common ancestor (i.e. homology)
 20 x 20 substitution matrix

30. The BLOSUM62 substitution matrix
+5 for Arg to Arg
-2 for Arg to Asp
Fig 4.4

31. The BLOSUM62 substitution matrix
+ 1 for Ser to Thr
+5 for Arg to Arg
-2 for Arg to Asp
Fig 4.4

32. Scoring a pairwise alignment using the BLOSUM62 matrix
T H I S S E Q E N C E
T H A T S E Q E N C E 5 8 -1 1 4 5 5 5 6 9 5
The overall alignment score (S) = 52

33. Generation of substitution scoring matrices
Based on the observed amino acid substitution frequencies in alignments of homologous protein sequences
Use real data to model the evolutionary processes
PAM substitution matrices are calculated from global protein alignments
BLOSUM substitution matrices are calculated from local protein alignments

34. Point-accepted mutations
PAM matrices:
Point-accepted mutations
Dayoff (1960’s) calculated substitution probabilities from alignments of highly similar protein families
All the PAM data come from closely related proteins
(>85% amino acid identity).

35. Point-accepted mutations
PAM matrices:
Point-accepted mutations
Dayoff (1960’s) calculated substitution probabilities from alignments of highly similar protein families
All the PAM data come from closely related proteins
(>85% amino acid identity).
The PAM1 is the matrix calculated from comparisons
of sequences with no more than 1% divergence.
Other PAM matrices are extrapolated from PAM1. For PAM250, 250 changes have occurred for two proteins over a length of 100 amino acids.

36. A PAM250 scoring matrix that assigns scores and is forgiving of mismatches…
(such as +17 for W to W
or -5 for W to T) 36

37. …compared to a scoring matrices such as PAM10 that are strict and do not tolerate mismatches
(such as +13 for W to W
or -19 for W to T) 37

38. BLOSUM Matrices BLOSUM matrices are based on local alignments.
The BLOCKS database contains thousands of groups of
multiple sequence alignments.
BLOSUM stands for blocks substitution matrix.
All BLOSUM matrices are based on observed alignments;
they are not extrapolated from comparisons of
closely related proteins. 38

39. BLOSUM Matrices BLOSUM62 is a matrix calculated from comparisons of
sequences with no more than 62% similarity.
BLOSUM62 is the default matrix in BLAST2.0.
Though it is tailored for comparisons of moderately distant
proteins, it performs well in detecting closer relationships.
A search for distant relatives may be more sensitive
with a different matrix. 39

40. Selecting an appropriate scoring matrix
More conserved
Less conserved
Rat versus
mouse globin
Rat versus
bacterial
globin

41. 4.4 Inserting Gaps
Homologous sequences are often different in length as a result of insertions and deletions (indels)
The alignment of indels involves inserting gaps into the alignment
Gap penalty: each time a gap is introduced, a gap penalty is subtracted from the score
A gap opening penalty is usually high
A gap extension penalty is usually low

42. Scoring a pairwise alignment using the BLOSUM62 matrix and gap penalty
T H I S I S A S E Q E N C E
T H A T S E Q E N C E 5 8 -1 1 4 5 5 5 6 9 5

43. Scoring a pairwise alignment using the BLOSUM62 matrix and gap penalty
T H I S I S A S E Q E N C E
T H A T S E Q E N C E 5 8 -1 1 4 5 5 5 6 9 5
Gap opening penalty = -11
Gap extension penalty = -1
The overall score (S) = 52 + ( ) = 40

44. 4.4 Inserting Gaps Alignment with a high gap penalty
Alignment with a low gap penalty
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45. Next in the course... Ch 5.
We have learned how to score an alignment, but how do you generate the alignment in the first place?
Here are two approaches:
 Dynamic Programming Algorithms
 Heuristic Search Algorithms

46. Sequence alignments continued
David Walsh
BIOL510: Bioinformatics
Alignment of dog genome to the human genome
Like to start with some general comments before we begin
Rasko et al. Nucleic Acids Res. 2004; 32(3): 977–988

47. Outline: sequence alignments (Ch 5)
Dynamic Programming Algorithms (Ch 5.2)
Global alignment: Needleman-Wunsch
Local alignments: Smith-Waterman
Heuristic Search Algorithms (Ch 5.3)
 BLAST
Alignment Score Significance (Ch 5.4)
WE WILL COVER THIS ON TUESDAY DURING THE LAB

48. Scoring an alignment using the BLOSUM62 substitution matrix and gap penalty
T H I S I S A S E Q E N C E
T H A T S E Q E N C E 5 8 -1 1 4 5 5 5 6 9 5
Gap opening penalty = -11
Gap extension penalty = -1
The overall score (S) = 52 + ( ) = 40

49. Dynamic Programming Algorithms
For any given pair of sequences, if gaps are allowed there is a large number of possible alignments. 49

50. Dynamic Programming Algorithms
For any given pair of sequences, if gaps are allowed there is a large number of possible alignments.
Dynamic programming algorithms: can explore the full range of alignments using a variety of different constraints, by dividing the problem of alignment into many smaller parts
Needleman and Wunsch published the original program in the 1970’s and there have been many modifications and improvements since. 50

51. Global alignment versus local alignment
Global alignment (Needleman-Wunsch) extends
from one end of each sequence to the other.
Local alignment finds optimally matching
regions within two sequences (“subsequences”).
Local alignment is almost always used for database
searches such as BLAST. It is useful to find domains
(or limited regions of homology) within sequences.
Smith and Waterman (1981) solved the problem of
performing optimal local sequence alignment. Other
methods (BLAST, FASTA) are faster but less thorough. 51

52. Needleman-Wunsch: dynamic programming
N-W is guaranteed to find optimal alignments, although
the algorithm does not search all possible alignments.
It is an example of a dynamic programming algorithm:
an optimal path (alignment) is identified by
incrementally extending optimal subpaths.
Thus, a series of decisions is made at each step of the
alignment to find the pair of residues with the best score. 52

53. 4.2 Scoring alignments: dot plots
Dot-plots are a simple way to visualize pairwise sequence similarity
But, they are the beginning of generating optimal alignments as well.
Fig 4.1

54. Three steps to global alignment with the Needleman-Wunsch algorithm
[1] set up a matrix of two sequences
[2] score the matrix
[3] identify the optimal alignment(s) 54

55. Global alignment with the algorithm of Needleman and Wunsch (1970)
• Two sequences can be compared in a matrix
along x- and y-axes.
• If they are identical, a path along a diagonal
can be drawn
• Find the optimal subpaths, and add them up to achieve
the best score. This involves
--adding gaps when needed
--allowing for conservative substitutions
--choosing a scoring system (simple or complicated)
N-W is guaranteed to find optimal alignment(s) 55

56. Four possible outcomes in aligning two sequences1 2
[1] identity (stay along a diagonal)
[2] mismatch (stay along a diagonal)
[3] gap in one sequence (move vertically!)
[4] gap in the other sequence (move horizontally!) 56

57. 57

58. The initial stage of dynamic programming
Gap extension penalty (E) = -8
BLOSUM62 substitution matrix
Figure 5.8

59. The initial stage of dynamic programming
Figure 5.10
Gap extension penalty (E) = -8
BLOSUM62 substitution matrix
Figure 5.8

60. The initial stage of dynamic programming
-16
Figure 5.10
Gap extension penalty (E) = -8
BLOSUM62 substitution matrix
Figure 5.8

61. The initial stage of dynamic programming
-16
Figure 5.10
Gap extension penalty (E) = -8
BLOSUM62 substitution matrix
Figure 5.8

62. The initial stage of dynamic programming: filling in the matrix-1
Figure 5.10
Thr  Ile Score= -1
Gap extension penalty (E) = -8
BLOSUM62 substitution matrix
Figure 5.8

63. The initial stage of dynamic programming: filling in the matrix
Score = -4
Gap extension penalty (E) = -8
BLOSUM62 substitution matrix
Figure 5.9
The final stage of dynamic programming: traceback

64. The initial stage of dynamic programming: filling in the matrix
Score = 7
Gap extension penalty (E) = -4
BLOSUM62 substitution matrix
Figure 5.11
The final stage of dynamic programming: traceback

65. Local alignment: the Smith-Waterman (SW) algorithm
Remember: two protein sequences may not exhibit homology along their full length
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66. Local alignment: the Smith-Waterman (SW) algorithm
Remember: two protein sequences may not exhibit homology along their full length
SW is a modification of the Needleman-Wunsch algorithm
Instead of looking at each sequence in its entirety, the method compares segments of all possible lengths and chooses the segments that optimize the similarity measure
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67. Local alignment algorithm: optimal subsequence alignments less than zero (<0) are rejected
Score = 12
Gap extension penalty (E) = -8 (!!!!!)
BLOSUM62 substitution matrix
Figure 5.15

68. Outline: sequence alignments (Ch 5)
Dynamic Programming Algorithms (Ch 5.2)
Global alignment: Needleman-Wunsch
Local alignments: Smith-Waterman
Heuristic Search Algorithms (Ch 5.3)
 BLAST
Alignment Score Significance (Ch 5.4)
Will cover this topic during the lab