1. Multiple Sequence Alignment
Alexei Drummond

2. Week 3 Learning Outcomes
Be able to compute the Smith-Waterman (local) pairwise alignment of two sequences given a score matrix and gap penalty
Be able to compute the Needleman-Wunsch (global) pairwise alignment of two sequences given a score matrix and gap penalty
Understand the principle of log-odds scoring. CS

3. Week 4 Learning Outcomes
Be able to recognize simple problems that are amenable to dynamic programming (DP) and design a DP algorithm to solve such problems.
Understand the principle of linear space optimal pairwise alignment
Understand the principle of quadratic-time pairwise alignment with affine gap penalties. CS

4. Computational Biology
Pairwise sequence alignment (global and local)
Multiple sequence alignment
Substitution matrices
Database searching
BLAST
Sequence statistics
Global
Local
Evolutionary tree reconstruction CS
Adapted from slide by Dannie Durant

5. Multiple sequence alignment
Definition: Given sequences X(1)…X(N) of lengths n1…nN, seek A(1)…A(N) of length n  max{ni} such that
Obtain X(i) from A(i) by removing gap characters
No columns contains all gaps
He score of the alignment is optimal CS

6. Definitions Sequence i Row i in alignment Column j in alignmentCS

7. Multiple sequence alignment
The first 55 amino acids of the albumin protein in 4 vertebrate animals unaligned and aligned. CS

8. Multiple sequence alignment
Align N sequences, so that residues in each column share a property of interest
A common ancestor / evolutionary history
A structural or functional role CS

9. Multiple sequence alignmentH T A L H N V L H H V F Y H V
Characters in the same column share evolutionary history CS

10. Structure-based alignmentCS
Adapted from slide by Dannie Durant

11. Scoring function: sum of pairs
Column Score
A-CTCCAT
A-GTCC-T
ACGTCA-T CS

12. Scoring function: sum of pairs
Column Score
A-CTCCAT
A-GTCC-T
ACGTCA-T CS

13. Scoring function: tree-based
(1) A-CTCCAT
(2) A-GTCC-T
(3) ACGTCA-T G C G G
(1)
(2)
(3)
Assumptions
Sequences (in particular the characters in a column) evolved from a common ancestor
Evolution is parsimonious - mutations are rare CS

14. Scoring function: tree-based1 G
(1) A-CTCCAT
(2) A-GTCC-T
(3) ACGTCA-T C G G
(1)
(2)
(3) C 1 G
The score is the minimum number of substitutions needed to explain the data, considering all possible internal labels.
Here are 3 of the 16 possible internal labelings of two internal nodes, and the corresponding number of substitutions implied. C G G
(1)
(2)
(3) C C 2 C G G
(1)
(2)
(3) CS

15. Sum of pairs versus tree-basedG
SP_Score = 6
Tree_Score = 1 A A A A A A G G G G CS

16. Tree-based scores Thought to be the “most biological” but
We don’t know the tree
We need to infer the characters on internal nodes (more on that in later lectures)
There may be different trees for different parts of the alignment (if recombination has occurred)
Not always relevant for structural alignments
Sum of pairs is almost always used in practice. CS

17. Linear gap scores & SP scoring1 - - -
Treat gap as separate symbol
s(a,-) = s(-,a) = gap score
s(-,-) = 0
“Sum of Pairs” (SP) scoring function - - - - - - - - - j - - - - - - - - k - - - - - - - N -
Column CS

18. Multidimensional dynamic programming
Define 1 i
= max score of an alignment up
to the sequences ending with 1 - - - - - - - - - - - - - - - - - - N -
All
ways of placing
gaps in this column
time,
space CS

19. Dynamic programming for multiple sequence alignment
Traceback
Optimal score CS

20. MSA Carrillo and Lipman (1988),
Lipman, Altschul and Kececioglu (1989).
Can optimally align up to 8-10 protein sequences of up to 500 residues. CS

21. Multiple alignment software
Really need approximation methods.
Different techniques
Progressive global alignment of sequences starting with an alignment of the most similar sequences and then building a full alignment by adding more sequences
Iterative methods that make an initial alignment of groups of sequences and then refine the alignment to achieve a better result (Barton-Sternberg, Simulated annealing, stochastic hill climbing, genetic algorithms)
Use of probabilistic models of the indel and substitution process to do statistical inference of alignment. (“Statistical alignment”) CS

22. Progressive alignment
Align sequences
(pairwise) in some
(greedy) order
Decisions
(1) Order of alignments
(2) Alignment of sequence to group (only), or allow group to group
Method of alignment, and scoring function CS

23. Guide tree A
this ? B C D E A
or this ? B C D E F CS

24. Feng & Doolittle (1987) Overview
Calculate diagonal matrix of N(N-1)/2 distances between all pairs of N sequences by standard pairwise alignment, converting raw alignment scores to approximate pairwise “distances” (either p-distance or a genetic distance based on a Markov model).
Construct guide tree from the distance matrix by using appropriate clustering algorithm.
Starting from first node added to the tree, align the child nodes (which may be two sequences, a sequence and an alignment, or two alignments). Repeat for all other nodes in the order that they were added to tree, until all sequences have been aligned. CS

25. Feng & Doolittle (1987) X X X XX sequence-to-group Best pairwise
alignment
determines
alignment to group X X X XX CS

26. Feng & Doolittle (1987) X sequence-to-group Best pairwise alignment
determines
alignment to group X CS

27. Feng & Doolittle (1987) – – – – – X
sequence-to-group
Best pairwise
alignment
determines
alignment to group
– – – – – X
This column is encouraged because it has no cost CS

28. Feng & Doolittle (1987) – – – – – X X X XX sequence-to-group
Best pairwise
alignment
determines
alignment to group
– – – – – X X X XX CS

29. Feng & Doolittle (1987) X X X X X X X X XX sequence-to-group
Best pairwise
alignment
determines
alignment to group X X X X X X X X XX CS

30. Feng & Doolittle (1987) group-to-group X XX X X X XX Best pairwise
alignment
determines
alignment of
groups X X X XX CS

31. Feng & Doolittle (1987) group-to-group XX X Best pairwise alignment
determines
alignment of
groups X CS

32. Feng & Doolittle (1987) group-to-group – – – – – – XX X –
Best pairwise
alignment
determines
alignment of
groups X – CS

33. Feng & Doolittle (1987) group-to-group – – – – – – X – – – – – –XX
– – – – – –
Best pairwise
alignment
determines
alignment of
groups X X
––––––– X XX CS

34. Feng & Doolittle (1987) – – – – – – X – – – – – – – – – – – – XX
group-to-group
– – – – – – X
– – – – – –
– – – – – – XX
Best pairwise
alignment
determines
alignment of
groups
– – – – – – X X
––––––– X XX CS

35. Feng & Doolittle (1987) X X X X X X X XX X X XXXXXXX X XX
group-to-group X X X X X X X XX
Best pairwise
alignment
determines
alignment of
groups X X
XXXXXXX X XX CS

36. Feng & Doolittle (1987)
After alignment is completed gap symbols replaced by “X”.
“Once a gap, always a gap”.
Encourages gaps to occur in same columns in subsequent alignments.
Implemented by PILEUP (from GCG package). CS

37. Profile alignment X X X group-to-group A B
Total alignment score = score (A) + score (B) + score (A*B) CS

38. CLUSTALW Thompson, Higgins and Gibson (1994).
Widely used implementation of profile-based progressive multiple alignment.
Similar to Feng-Doolittle method, except for use of profile alignment methods.
Overview:
Calculate diagonal matrix of N(N-1)/2 distances between all pairs of N sequences by standard pairwise alignment, converting raw alignment scores to approximate pairwise “distances”.
Construct guide tree from distance matrix by using an appropriate neighbour-joining clustering algorithm.
Progressively align at nodes in order of decreasing similarity, using sequence-sequence, sequence-profile, and profile-profile alignment.
Plus many other heuristics. CS

39. CLUSTAL W heuristics
Closely related sequences are aligned with hard matrices (BLOSUM80) and distant sequences are aligned with soft matrices (BLOSUM50).
Hydrophobic residues (which are more likely to be buried) are given higher gap penalties than hydrophilic residues (which are more likely to be surface-accessible).
Gap-open penalties are also decreased if the position is spanned by 5 or more consecutive hydrophilic residues. CS

40. CLUSTAL W heuristics
Both gap-open penalties and gap-extend penalties are increased if there are no gaps in a column but gaps occur nearby in the alignment. This rule tries to force all gaps to occur in the same places in an alignment.
In the progressive alignment stage, if the score of an alignment is low, the guide tree may be adjusted on the fly to defer the low scoring alignment until later in the progressive alignment phase when more profile information has been accumulated. CS

41. Iterative refinement
i.e. “hill climbing”. Slightly change solution to improve score. Converge to local optimum.
e.g. Barton-Sternberg (1987) multiple alignment
Find the two sequences with the highest pairwise similarity and align them
using standard dynamic programming alignment.
Find sequence most similar to a profile of the alignment of the first two, and align it to first two by profile-sequence alignment.
Repeat until all sequences have been included in the multiple alignment.
Remove sequence X(1) and realign it to a profile of the other aligned
sequences X(2)…X(N) by profile-sequence alignment. Repeat for
sequences X(2)…X(N).
Repeat the previous alignment step a fixed number of times, or until the alignment score converges. CS

42. Clustal X CS

43. Clustal X CS

44. CLUSTALX CS

45. CLUSTALX CS

46. C_aminophilum AGCT.YCGCA TGRAGCAGTG TGAAAA.... ............ACTCCGGT GGTACAGGAT
C_colinum AGTA..GGCA TCTACAAGTT GGAAAA ACTGAGGT GGTATAGGAG
C_lentocellum GGTATTCGCT TGATTATNAT AGTAAA GATTTATC GCCATAGGAT
C_botulinum_D TTTA.TGGCA TCATACATAA AATAATCAAA GGAGCAATCC GCTTTGAGAT
C_novyi_A TTTA.CGGCA T....CGTAG AATAATCAAA GGAGCAATCC GCTTTGAGAT
C_gasigenes AGTT.TCGCA TGAAACA... GC.AATTAAA GGAGAAATCC GCTATAAGAT
C_aurantibutyricum A.NT.TCGCA TGGAGCA... AC.AATCAAA GGAGCAAT.C ACTATAAGAT
C_sp_C_quinii AGTT.T.GCA TGGGACA... GC.AATTAAA GGAGCAATCC GCTATGAGAT
C_perfringens AAGA.TGGCA T.CATCA... TTCAACCAAA GGAGCAATCC GCTATGAGAT
C_cadaveris TTTT.CTGCA TGGGAAA... GTC.ATGAAA GGAGCAATCC GCTGTAAGAT
C_cellulovorans ATTC.TCGCA TGAGAGA... .TGTATCAAA GGAGCAATCC GCTATAAGAT
C_K21 TTGR.TCGCA TGATCKAAAC ATCAAAGGAT ..TTTTCTTTGGAAAATTCC ACTTTGAGAT
C_estertheticum TTGA.TCGCA TGATCTTAAC ATCAAAGGAA ..TTT..TTCGG..AATTTC ACTTTGAGAT
C_botulinum_A AGAA.TCGCA TGATTTTCTT ATCAAAGATT ..T ATT.. GCTTTGAGAT
C_sporogenes AGAA.TCGCA TGATTTTCTT ATCAAAGATT ..T ATT.. GCTTTGAGAT
C_argentinense AAGG.TCGCA TGACTTTTAT ACCAAAGGAG ..T AATCC GCTATGAGAT
C_subterminale AAGG.TCGCA TGACTTTTAT ACCAAAGGAG ..T AATCC GCTATGAGAT
C_tetanomorphum TTTT.CCGCA TGAAAAACTA ATCAAAGGAG ..T AAT.C GCTTTGAGAT
C_pasteurianum AGTT.TCACA TGGAGCTTTA ATTAAAGGAG ..T AATCC GCTTTGAGAT
C_collagenovorans TTGA.TCGCA TGGTCGAAAT ATTAAAGGAG ..T AATCC GCTTACAGAT
C_histolyticum TTTA.ATGCA TGTTAGAAAG ATTAAAGGAG CAATCC GCTTTGAGAT
C_tyrobutyricum AGTT.TCACA TGGAATTTGG ATGAAAGGAG ..T AATTC GCTTTGAGAT
C_tetani GGTT.TCGCA TGAAACTTTA ACCAAAGGAG ..T AATCT GCTTTGAGAT
C_barkeri GACA.TCGCA TGGTGTT... .TTAATGAAA ACTCCGGT GCCATGAGAT
C_thermocellum GGCA.TCGTC CTGTTAT... .CAAAGGAGA AATCCGGT ...ATGAGAT
Pep_prevotii AGTC.TCGCA TGGNGTTATC ATCAAAGA TTTATC GGTGTAAGAT
C_innocuum ACGGAGCGCA TGCTCTGTAT ATTAAAGCGC CCTTCAAGGCGTGAAC ATGGAT
S_ruminantium AGTTTCCGCA TGGGAGCTTG ATTAAAGATG GCCTCTACTTGTAAGCTATC GCTTTGCGAT

47. TCAAAGGAG
C_aminophilum AGCT.YCGCA TGRAGCAGTG TGAAAA ACTCCGGT GGTACAGGAT
C_colinum AGTA..GGCA TCTACAAGTT GGAAAA ACTGAGGT GGTATAGGAG
C_lentocellum GGTATTCGCT TGATTATNAT AGTAAA GATTTATC GCCATAGGAT
C_botulinum_D TTTA.TGGCA TCATACATAA AATAATCAAA GGAGCAATCC GCTTTGAGAT
C_novyi_A TTTA.CGGCA T....CGTAG AATAATCAAA GGAGCAATCC GCTTTGAGAT
C_gasigenes AGTT.TCGCA TGAAACA... GC.AATTAAA GGAGAAATCC GCTATAAGAT
C_aurantibutyricum A.NT.TCGCA TGGAGCA... AC.AATCAAA GGAGCAAT.C ACTATAAGAT
C_sp_C_quinii AGTT.T.GCA TGGGACA... GC.AATTAAA GGAGCAATCC GCTATGAGAT
C_perfringens AAGA.TGGCA T.CATCA... TTCAACCAAA GGAGCAATCC GCTATGAGAT
C_cadaveris TTTT.CTGCA TGGGAAA... GTC.ATGAAA GGAGCAATCC GCTGTAAGAT
C_cellulovorans ATTC.TCGCA TGAGAGA... .TGTATCAAA GGAGCAATCC GCTATAAGAT
C_K21 TTGR.TCGCA TGATCKAAAC ATCAAAGGAT ..TTTTCTTTGGAAAATTCC ACTTTGAGAT
C_estertheticum TTGA.TCGCA TGATCTTAAC ATCAAAGGAA ..TTT..TTCGG..AATTTC ACTTTGAGAT
C_botulinum_A AGAA.TCGCA TGATTTTCTT ATCAAAGATT ..T ATT.. GCTTTGAGAT
C_sporogenes AGAA.TCGCA TGATTTTCTT ATCAAAGATT ..T ATT.. GCTTTGAGAT
C_argentinense AAGG.TCGCA TGACTTTTAT ACCAAAGGAG ..T AATCC GCTATGAGAT
C_subterminale AAGG.TCGCA TGACTTTTAT ACCAAAGGAG ..T AATCC GCTATGAGAT
C_tetanomorphum TTTT.CCGCA TGAAAAACTA ATCAAAGGAG ..T AAT.C GCTTTGAGAT
C_pasteurianum AGTT.TCACA TGGAGCTTTA ATTAAAGGAG ..T AATCC GCTTTGAGAT
C_collagenovorans TTGA.TCGCA TGGTCGAAAT ATTAAAGGAG ..T AATCC GCTTACAGAT
C_histolyticum TTTA.ATGCA TGTTAGAAAG ATTAAAGGAG CAATCC GCTTTGAGAT
C_tyrobutyricum AGTT.TCACA TGGAATTTGG ATGAAAGGAG ..T AATTC GCTTTGAGAT
C_tetani GGTT.TCGCA TGAAACTTTA ACCAAAGGAG ..T AATCT GCTTTGAGAT
C_barkeri GACA.TCGCA TGGTGTT... .TTAATGAAA ACTCCGGT GCCATGAGAT
C_thermocellum GGCA.TCGTC CTGTTAT... .CAAAGGAGA AATCCGGT ...ATGAGAT
Pep_prevotii AGTC.TCGCA TGGNGTTATC ATCAAAGA TTTATC GGTGTAAGAT
C_innocuum ACGGAGCGCA TGCTCTGTAT ATTAAAGCGC CCTTCAAGGCGTGAAC ATGGAT
S_ruminantium AGTTTCCGCA TGGGAGCTTG ATTAAAGATG GCCTCTACTTGTAAGCTATC GCTTTGCGAT
TCAAAGGAG

48. Alignment - considerations
The programs simply try to maximize the number of matches
The “best” alignment may not be the correct biological one
Multiple alignments are done progressively
Such alignments get progressively worse as you add sequences
Mistakes that occur during alignment process are frozen in.
Unless the sequences are very similar you will almost certainly have to correct manually CS

49. Manual Alignment- software
Geneious- cross-platform -
CINEMA- Java applet available from:
Seqapp/Seqpup- Mac/PC/UNIX available from:
Se-Al for Macintosh, available from:
BioEdit for PC, available from: CS

50. CS

51. CS

52. CS

53. CS

54. Extra T
Missing G CS

55. CS

56. CS

57. Hang on, what makes a good alignment?CS

58. What makes a good alignment?CS

59. What makes a good alignment?
Sequence Alignment
Structural Alignment CS

60. What makes a good alignment?CS

61. I hate ad hoc algorithms and manual sequence alignment
I hate ad hoc algorithms and manual sequence alignment! Is there an alternative? CS

62. An evolutionary hypothesis
Hypothesis/Model AG
Knowing the rates of different events (substitutions, insertions and deletions) provides a method of assessing the probability of these observations, given this hypothesis: Pr{D|T,Q}
T: the evolutionary tree
Q: parameters of the evolutionary process
G->A
Insert CC
Insert T
G->C
T->C
Delete G
AAT
AAC AC
ACCG
ACC
Observations CS

63. Statistics: fitting versus modeling
Statistical fitting of sequence variation
Count frequencies of changes in real data sets
Build empirical statistical descriptions of the data (Blosum62)
Compare observed frequencies to well defined null hypothesis for testing (log-odds ratio and scores)
Use scores in ad hoc algorithms for search and alignment (BLAST and ClustalX)
Probabilistic models of sequence evolution
Describe a probabilistic model in terms of a process of evolution, rates of substitution, insertion and deletion
Estimate parameters of the models and compare models using model comparison (likelihood ratios, Bayes factors)
Use maximum likelihood and Bayesian inference to co-estimate (uncertainty in) alignment and evolutionary history. CS

64. Probabilistic models and biology
3D structure of myoglobin, showing six alpha-helices. CS

65. State of the art CS

66. Bali-Phy
Source: CS

67. What does the future hold?
No single “true” alignment
In most situations there are a set of alignments that are consistent with the observations
Understanding this uncertainty is as important as understanding the “best” alignment
Explicit evolutionary model-based methods
Methods that co-estimate alignment and phylogeny are beginning to appear
Co-estimation of protein structure and alignment using evolutionary models may be on horizon
Death of manual sequence alignment? CS