1. Recent Progress in Multiple Sequence Alignments: A Survey
Cédric Notredame

2. Our Scope What are The existing Methods? How Do They Work:
-Assemby Algorithms
-Weighting Schemes.
When Do They Work ?
Which Future?

3. Outline -Introduction -A taxonomy of the existing Packages
-A few algorithms…
-Performance Comparison using BaliBase

4. Introduction

5. What Is A Multiple Sequence Alignment?
A MSA is a MODEL
It Indicates the RELATIONSHIP between residues of different sequences.
LIKE ANY MODEL
It REVEALS
-Similarities
-Inconsistencies

6. How Can I Use A Multiple Sequence Alignment?
chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD
wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE
trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP
mouse KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: : * . *: *
chite AATAKQNYIRALQEYERNGG-
wheat ANKLKGEYNKAIAAYNKGESA
trybr AEKDKERYKREM
mouse AKDDRIRYDNEMKSWEEQMAE
* : .* . :
Extrapolation
Motifs/Patterns
Multiple Alignments Are CENTRAL to MOST Bioinformatics Techniques.
Profiles
Phylogeny
Struc. Prediction

7. How Can I Use A Multiple Sequence Alignment?
Multiple Alignments Is the most INTEGRATIVE Method Available Today.
We Need MSA to INCORPORATE existing DATA

8. Why Is It Difficult To Compute A multiple Sequence Alignment?
A CROSSROAD PROBLEM
BIOLOGY: What is A Good Alignment
COMPUTATION What is THE Good Alignment
chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD
wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE
trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP
mouse KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP
***. ::: .: : * . *: *

9. Why Is It Difficult To Compute A multiple Sequence Alignment ?
BIOLOGY
COMPUTATION
CIRCULAR PROBLEM....
Good
Good
Sequences
Alignment

10. A Taxonomy of Multiple Sequence Alignment Methods

11. Grouping According to the assembly Algorithm

12. Simultaneous As opposed to Progressive
[Simultaneous: they simultaneously use all the information]
Exact As opposed to Heursistic
[Heuristics: cut corners like Blast Vs SW]
[Heuristics: do not guarranty an optimal solution]
Stochastic As opposed to Determinist
[Stochastic: contain an element of randomness]
[Stochastic: Example of a Monte Carlo Surface estimation ]
Iterative As opposed to Non Iterative
[Iterative: run the same algorithm many times]
[Iterative: Most stochastic methods are iterative]

13. Simultaneous
Clustal
Dialign
T-Coffee
Progressive
MSA
POA
DCA
Combalign
Non tree based
Iterative
Iteralign
Prrp
SAM
HMMer
SAGA GA
OMA
Praline MAFFT
GAs
HMMs

14. Iterative
Iteralign
Prrp
SAM
HMMer GA
Clustal
Dialign
T-Coffee
Progressive
Simultaneous
MSA
POA
OMA
Praline MAFFT
DCA
Combalign
SAGA
Stochastic

15. NEARLY EVERY OPTIMISATION ALGORITHM HAS BEEN APPLIED TO THE MSA PROBLEM!!!

16. Grouping According to the Objective Function

17. Scoring an Alignment: Evolutionary based methods
BIOLOGY
How many events separate my sequences?
Such an evaluation relies on a biological model.
COMPUTATION
Every position musd be independant

18. Model: ALL the sequences evolved from the same ancestor
REAL Tree
Model: ALL the sequences evolved from the same ancestor
A A A C A A C
Tree: Cost=1 A A C A C
PROBLEM: We do not know the true tree

19. A A A C C Star Tree: Cost=2 C
Model: ALL the sequences have the same ancestor
A A A C A C
Star Tree: Cost=2 A A C A
PROBLEM: the tree star is phylogenetically wrong

20. C Sums of Pairs: Cost=6 A A A C
Model=Every sequence is the ancestor of every sequence A C
Sums of Pairs: Cost=6
A A A C
[s(a,b): matrix]
[i: column i]
[k, l: seq index]
PROBLEM: -over-estimation of the mutation costs
-Requires a weighting scheme

21. Some of itslimitations (Durbin, p140)
Sums of Pairs:
Some of itslimitations (Durbin, p140)
L L L
Cost= 5*N*(N-1)/2
[5: Leucine Vs Leucine with Blosum50]
Cost=5*N*(N-1)/2-(5)*(N-1) - (-4)*(N-1)
[glycine effect]
Cost=5*N*(N-1)/2-(9)*(N-1) G

22. Some of its limitations (Durbin, p140)
Sums of Pairs:
Some of its limitations (Durbin, p140)
L L L G
Delta=
2*(9)*(N-1)
5*N*(N-1) =
(9)
5*N N
Delta
Conclusion: The more Leucine, the less expensive it gets to add a Glycin to the column...

23. Enthropy based Functions
Model: Minimize the enthropy (variety) in each Column
[number of Alanine (a) in column i]
A A A C
[Score of column i]
[a: alphabet]
[P can incorporate pseudocounts]
S=0 if the column is conserved
PROBLEM: -requires a simultaneous alignment
-assumes independant sequences

24. Consistency based Functions
Model: Maximise the consistency (agreement) with a list of constraints (alignments)
[kand l are sequences, i is a column]
A A A C
[the two residues are found aligned in the list of constraints]
PROBLEM: -requires a list of constraints

25. Prrp
Clustal POA
MSA
MAFFT OMA
DCA
SAGA
Weighted
Sums of
Pairs
Concistency Based
Iteralign
Dialign
T-Coffee
Praline
Combalign
Enthropy
SAM
HMMer
GIBBS

26. A few Multiple Sequence Alignment Algorithms

27. MSA and DCA POA ClustalW MAFFT Dialign II Prrp SAGA GIBBS Sampler
A Few Algorithms
MSA and DCA
POA
ClustalW
MAFFT
Dialign II
Prrp
SAGA
GIBBS Sampler

28. Simultaneous: MSA and DCA

29. Simultaneous Alignments : MSA
1) Set Bounds on each pair of sequences (Carillo and Lipman)
2) Compute the Maln within the Hyperspace
-Few Small Closely Related Sequence.
-Memory and CPU hungry
-Do Well When They Can Run.

30. MSA: the carillo and Lipman bounds
chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD
wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE
trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP
mouse KPKRPRSAYNIYVSESFQ----EAKDDS-AQGKLKLVNEAWKNLSP ( ) S = ( ) S
chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD
wheat --DPNKPKRAPSAFFVFMGEFREEFKQKNPKNKSVAAVGKAAGERWKSLSE + ) (
chite ---ADKPKRPLSAYMLWLNSARESIKRENPDFK-VTEVAKKGGELWRGLKD
trybr KKDSNAPKRAMTSFMFFSSDFRS----KHSDLS-IVEMSKAAGAAWKELGP S …
[Pairwise projection of sequences k and l]

31. MSA: the carillo and Lipman bounds
a(k,l)=score of the projection k l in the optimal MSA
S(a(x,y))=score of the complete multiple alignment
â(k,l)=score of the optimal alignment of k l
Upper
Lower
a(k,m)
â(k,m)
â(k,l) ?
a(k,l)

32. MSA: the carillo and Lipman bounds
LM: a lower bound for the complete MSA
LM<=S(â(x,y)) - (â(k,l)-a(k,l))
a(k,l)>=LM +â(k,l)-S(â(x,y))
a(k,l)
â(k,l)
LM+ â(k,l)-S(â(x,y)) ?

33. MSA: the carillo and Lipman bounds
â(k,l)
LM+ â(k,l)-S(â(x,y))
a(k,l)
ä(k,l)
â(k,l)
LM: can be measured on ANY heuristic alignment
LM = S(ä(x,y))
The better LM, the tighter the bounds…

34. MSA: the carillo and Lipman bounds
Best( M-i, N-j)
Best( 0-i, 0-j) + M M
Forward
backward

35. Simultaneous Alignments : MSA
1) Set Bounds on each pair of sequences (Carillo and Lipman)
2) Compute the Maln within the Hyperspace
-Few Small Closely Related Sequence.
-Memory and CPU hungry
-Do Well When They Can Run.

36. Simultaneous Alignments : DCA
-Few Small Closely Related Sequence, but less limited than MSA
-Do Well When Can Run.
-Memory and CPU hungry, but less than MSA

37. Simultaneous With a New Sequence Representaion:
POA-Partial Ordered Graph

40. POA
POA makes it possible to represent complex relationships:
-domain deletion
-domain inversions

41. Progressive: ClustalW

42. Progressive Alignment: ClustalW Feng and Dolittle, 1988; Taylor 198ç
Clustering

43. Progressive Alignment: ClustalW
Dynamic Programming Using A Substitution Matrix

44. Tree based Alignment : Recursive Algorithm
Align ( Node N) {
if ( N->left_child is a Node)
A1=Align ( N->left_child)
else if ( N->left_child is a Sequence)
A1=N->left_child
if (N->right_child is a node)
A2=Align (N->right_child)
else if ( N->right_child is a Sequence)
A2=N->right_child
Return dp_alignment (A1, A2) } A B C D E F G

45. Progressive Alignment : ClustalW
-Depends on the CHOICE of the sequences.
-Depends on the ORDER of the sequences (Tree).
-Depends on the PARAMETERS:
Substitution Matrix.
Penalties (Gop, Gep).
Sequence Weight.
Tree making Algorithm.

46. Progressive Alignment : ClustalW Weighting
Weighting Within ClustalW

47. Progressive Alignment : ClustalW GOP
Position Specific GOP

48. Progressive Alignment : ClustalW
ClustalW is the most Popular Method
-Greedy Heuristic (No Guarranty).
-Fast
-Scales Well: N, N L 3 2

49. Progressive Alignment With a Heuristic DP: MAFFT

51. Concistency Based Dialign II
Progressive
And
Concistency Based Dialign II

52. Dialign II
1) Identify best chain of segments on each pair of sequence. Assign a Pvalue to each Segment Pair.
2) Ré-évaluate each segment pair according to its consistency with the others
3) Assemble the alignment according to the segment pairs.

53. Dialign II -May Align Too Few Residues -No Gap Penalty
-Does well with ESTs

54. Concistency Based T-COFFEE
Progressive
And
Concistency Based T-COFFEE

55. Mixing Local and Global Alignments Multiple Sequence Alignment
Local Alignment
Global Alignment
Extension
Multiple Sequence Alignment

56. Library Based Multiple Sequence Alignment
What is a library? 3
Seq1 anotherseq
Seq2 atsecondone
Seq3 athirdone
#1 2
#1 3 …. 2
Seq1 MySeq
Seq2 MyotherSeq
#1 2 ….
Extension+T-Coffee
Library Based Multiple Sequence Alignment

57. Iterative

58. 7.16.1 Progressive Iterative Methods -HMMs, HMMER, SAM.
-Slow, Sometimes Inaccurate
-Good Profile Generators

59. Iterative Methods : Prrp
Initial Alignment
Tree and weights computation
YES
Weights converged
End
Outer Iteration NO
Realign two sub-groups
Inner Iteration
YES
Alignment converged NO

60. SAGA, The Genetic Algorithm
Iterative Sochastic:
SAGA, The Genetic Algorithm

64. Automatic scheduling of the operators

66. Weighting Schemes

67. The Problem The sequences Contain Correlated Information
Most scoring Schemes Ignore this Correlation

68. Weighting Sequence Pairs with a Tree:
Carillo and Lipman
Rationale I

69. QUESTION: Which Weight for a Pair of Sequences
E=EDGE
P=Evolutive Path from A to X
E must contribute the same weight to every path P that goes throught it.
Nk: Number of Edges meeting on Node k. A B C D E F G
All the weights using E must sum to 1: S(WP,E)=1.
Wp=
P(Nk-1) 1

70. USAGE

71. PROBLEM: Weight Depends only on the Tree topologyA C
AB: 0.5
AC: 0.5
BC: 0.5. B A C
AB: 0.5
AC: 0.5
BC: 0.5.

72. Weighting Sequences with a Tree
Clustal W
Weights

73. S QUESTION: Which Weight for Sequences ? W=Length *1/4 W=Length *1/2A B C D E F G G
W=S(W)
Number Sequences Sharing Edge
Edge Length
Wseq = S

74. USAGE

75. PROBLEM: Overweight of distant sequences
-C Will dominate the Alignment
-C Will be very Difficult to align

76. Performance Comparison Using Collections of Reference Alignments: BaliBase and Ribosomal RNA

77. What Is BaliBase
BaliBase
BaliBase is a collection of reference Multiple Alignments
The Structure of the Sequences are known and were used to assemble the MALN.
Evaluation is carried out by Comparing the Structure Based Reference Alignment With its Sequence Based Counterpart

78. What Is BaliBase
BaliBase 
DALI, Sap …
Method X
Comparison

79. What Is BaliBase BaliBase
Source: BaliBase, Thompson et al, NAR, 1999,
PROBLEM
Description
Even Phylogenic Spread.
One Outlayer Sequence
Two Distantly related Groups
Long Internal Indel
Long Terminal Indel

80. Choosing The Right Method

81. Choosing The Right Method (POA Evaluation)

82. Choosing The Right Method (POA Evaluation)

83. Choosing The Right Method (MAFFT evaluation)

84. Choosing The Right Method (MAFFT evaluation)

85. Choosing The Right Method (MAFFT evaluation)

86. Conclusion

87. What Is BaliBase Which Method ?
Source: BaliBase, Thompson et al, NAR, 1999,
PROBLEM
Strategy
Strategy
ClustalW, T-coffee, MSA, DCA
T-Coffee
PrrP,
T-Coffee
Dialign
T-Coffee
Dialign
T-Coffee

88. Methods /Situtations 1-Carillo and Lipman: 2-Segment Based:
-MSA, DCA.
-Few Small Closely Related Sequence.
-Do Well When They Can Run.
2-Segment Based:
-DIALIGN, MACAW.
-May Align Too Few Residues
-Good For Long Indels
3-Iterative:
-HMMs, HMMER, SAM.
-Slow, Sometimes Inaccurate
-Good Profile Generators
4-Progressive:
-ClustalW, Pileup, Multalign…
-Fast and Sensitive

89. Addresses MAFFT Progressive www.biophys.kyoto-u.jp/katoh
POA Progressive/Simulataneous
MUSCLE Progressive/Iterative