1. Sequence Alignment and Phylogenetic Analysis

2. Evolution

3. Sequence Alignment AGGCTATCACCTGACCTCCAGGCCGATGCCC
TAGCTATCACGACCGCGGTCGATTTGCCCGAC
-AGGCTATCACCTGACCTCCAGGCCGA--TGCCC---
TAG-CTATCAC--GACCGC--GGTCGATTTGCCCGAC
Definition
Given two strings x = x1x2...xM, y = y1y2…yN,
an alignment is an assignment of gaps to positions
0,…, N in x, and 0,…, N in y, so as to line up each letter in one sequence with either a letter, or a gap
in the other sequence

4. What is a good alignment?
AGGCTAGTT, AGCGAAGTTT
AGGCTAGTT- 6 matches, 3 mismatches, 1 gap
AGCGAAGTTT
AGGCTA-GTT- 7 matches, 1 mismatch, 3 gaps
AG-CGAAGTTT
AGGC-TA-GTT- 7 matches, 0 mismatches, 5 gaps
AG-CG-AAGTTT

5. Scoring Function Sequence edits: AGGCCTC Scoring Function: Match: +m
Mutations AGGACTC
Insertions AGGGCCTC
Deletions AGG . CTC
Scoring Function:
Match: +m
Mismatch: -s
Gap: -d
Score F = (# matches)  m - (# mismatches)  s – (#gaps)  d

6. Example F(i,j) i = 0 1 2 3 4 F(1, 1) = max{F(0,0) + s(A, A),
x = AGTA m = 1
y = ATA s = -1
d = -1
F(i,j) i =
F(1, 1) =
max{F(0,0) + s(A, A),
F(0, 1) – d,
F(1, 0) – d} =
max{0 + 1,
-1 – 1,
-1 – 1} = 1 A G T -1 -2 -3 -4 1 2
j = 0 1 2 3 A G - T A

7. The Needleman-Wunsch Matrix
x1 ……………………………… xM
Every nondecreasing path
from (0,0) to (M, N)
corresponds to
an alignment
of the two sequences
y1 ……………………………… yN
An optimal alignment is composed of optimal subalignments

8. Example H E A G W -8 -16 -24 -32 -40 -48 -56 -64 -72 -80 P -2 -9 -17-2 -3 6 10 P -4 15
Example H E A G W -8
-16
-24
-32
-40
-48
-56
-64
-72
-80 P -2 -9
-17
-25
-33
-42
-49
-57
-65
-73
Exercise fill in the rest of the table

9. H E A G W -8 -16 -24 -32 -40 -48 -56 -64 -72 -80 P -2 -9 -17 -25 -33-2 -3 6 10 P -4 15 H E A G W -8
-16
-24
-32
-40
-48
-56
-64
-72
-80 P -2 -9
-17
-25
-33
-42
-49
-57
-65
-73
-10 -3 -4
-12
-20
-28
-36
-44
-52
-60
-18
-11 -6 -7
-15 -5
-13
-21
-29
-37
-14
-19
-22 3
-30 2
-38 1

10. PAMX PAMx = PAM1x PAM250 is a widely used scoring matrix:
PAM250 = PAM1250
PAM250 is a widely used scoring matrix:
Ala Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys ...
A R N D C Q E G H I L K ...
Ala A
Arg R
Asn N
Asp D
Cys C
Gln Q
...
Trp W
Tyr Y
Val V

11. The Blosum50 Scoring Matrix

12. Affine Gap Penalties ATA__GC ATATTGC ATAG_GC AT_GTGC
In nature, a series of k indels often come as a single event rather than a series of k single nucleotide events:
ATA__GC
ATATTGC
ATAG_GC
AT_GTGC
This is more likely.
This is less likely.
Normal scoring would give the same score for both alignments

13. Affine gaps e d (n) (n) = d + (n – 1)e | | gap gap open extend
| |
gap gap
open extend
To compute optimal alignment,
F(i, j): score of alignment x1…xi to y1…yj
if xi aligns to yj
G(i, j): score if xi aligns to a gap after yj
H(i, j): score if yj aligns to a gap after xi
V(i, j) = best score of alignment x1…xi to y1…yj d

14. Needleman-Wunsch with affine gaps
Initialization: V(i, 0) = d + (i – 1)e
V(0, j) = d + (j – 1)e
Iteration:
V(i, j) = max{ F(i, j), G(i, j), H(i, j) }
F(i, j) = V(i – 1, j – 1) + s(xi, yj)
V(i, j – 1) – d
G(i, j) = max
G(i, j – 1) – e
V(i – 1, j) – d
H(i, j) = max
H(i – 1, j) – e
Termination: similar

15. Pairwise Alignment Tools

16. Some Typical Dot-plot Comparisons
Divergent sequences where only a segment is homologous
Long insertions and deletions
Tandem repeats
The square shape of the pattern is characteristic of these repeats

17. Using Dotlet Dotlet is one of the handiest tools for making dot plots
Dotlet is a Java applet
Open and download the applet at the following site:
Use Firefox or IE

18. Window size
Threshold window for fine tuning
Dot plot window
Alignment window

19. Window size
Threshold window for fine tuning
Dot plot window
Alignment window

20. Window size
Threshold window for fine tuning
Dot plot window
Alignment window

21. Looking at Repeated Domains with Dotlet
The square shape is typical of tandem repeats
The repeats are not perfect because the sequences have diverged after their duplication

22. Comparing a Gene and Its Product
Eukaryotic genes are transcribed into RNA
The RNA is then spliced to remove the introns’ sequences
It may be necessary to compare the gene and its product
Dotlet makes this comparative analysis easy

23. Lalign and BLAST Lalign is like a very precise BLAST
It works on only two sequences at a time
You must provide both sequences

24. LaLign

26. Lalign Output
Lalign produces an output similar to the alignment section of BLAST
The E-value indicates the significance of each alignment
Low E-value  good alignment

27. Multiple Alignment

28. Example

29. 4 Ways of Using MSAs . . .

30. 4 More Ways of Using MSAs

31. Generalizing the Notion of Pairwise Alignment
Alignment of 2 sequences is represented as a
2-row matrix
In a similar way, we represent alignment of 3 sequences as a 3-row matrix
A T _ G C G _
A _ C G T _ A
A T C A C _ A
Score: more conserved columns, better alignment

32. Aligning Three Sequences
source
Same strategy as aligning two sequences
Use a 3-D “”, with each axis representing a sequence to align
For global alignments, go from source to sink
sink

33. Architecture of 3-D Alignment Cell
(i-1,j,k-1)
(i-1,j-1,k-1)
(i-1,j-1,k)
(i-1,j,k)
(i,j,k-1)
(i,j-1,k-1)
(i,j,k)
(i,j-1,k)

34. Multiple Alignment: Dynamic Programming
cube diagonal: no indels
si,j,k = max
(x, y, z) is an entry in the 3-D scoring matrix
si-1,j-1,k-1 + (vi, wj, uk)
si-1,j-1,k +  (vi, wj, _ )
si-1,j,k  (vi, _, uk)
si,j-1,k  (_, wj, uk)
si-1,j,k +  (vi, _ , _)
si,j-1,k +  (_, wj, _)
si,j,k  (_, _, uk)
face diagonal: one indel
edge diagonal: two indels

35. Multiple Alignment: Running Time
For 3 sequences of length n, the run time is 7n3; O(n3)
For k sequences, build a k-dimensional Manhattan, with run time (2k-1)(nk); O(2knk)
Conclusion: dynamic programming approach for alignment between two sequences is easily extended to k sequences but it is impractical due to exponential running time

36. Sum of Pairs Score(SP-Score)
Consider pairwise alignment of sequences
ai and aj
imposed by a multiple alignment of k sequences
Denote the score of this suboptimal (not necessarily optimal) pairwise alignment as
s*(ai, aj)
Sum up the pairwise scores for a multiple alignment:
s(a1,…,ak) = Σi,j s*(ai, aj)

37. SP-Score: Example a1 ATG-C-AAT . A-G-CATAT ak ATCCCATTT
To calculate each column: s
s*(
Pairs of Sequences A G 1
Score=3 1 -m 1
Score = 1 – 2m A A C G 1 -m
Column 1
Column 3

38. Multiple Alignment Induces Pairwise Alignments
Every multiple alignment induces pairwise alignments
x: AC-GCGG-C
y: AC-GC-GAG
z: GCCGC-GAG
Induces:
x: ACGCGG-C; x: AC-GCGG-C; y: AC-GCGAG
y: ACGC-GAC; z: GCCGC-GAG; z: GCCGCGAG

39. Reverse Problem: Constructing Multiple Alignment from Pairwise Alignments
Given 3 arbitrary pairwise alignments:
x: ACGCTGG-C; x: AC-GCTGG-C; y: AC-GC-GAG
y: ACGC--GAC; z: GCCGCA-GAG; z: GCCGCAGAG
can we construct a multiple alignment that induces
them?

40. Reverse Problem: Constructing Multiple Alignment from Pairwise Alignments
Given 3 arbitrary pairwise alignments:
x: ACGCTGG-C; x: AC-GCTGG-C; y: AC-GC-GAG
y: ACGC--GAC; z: GCCGCA-GAG; z: GCCGCAGAG
can we construct a multiple alignment that induces
them?
NOT ALWAYS
Pairwise alignments may be inconsistent

41. Profile Representation of Multiple Alignment
- A G G C T A T C A C C T G
T A G – C T A C C A G
C A G – C T A C C A G
C A G – C T A T C A C – G G
C A G – C T A T C G C – G G A C G T

42. Multiple Alignment: Greedy Approach
Choose most similar pair of strings and combine into a profile , thereby reducing alignment of k sequences to an alignment of of k-1 sequences/profiles. Repeat
This is a heuristic greedy method
u1= ACg/tTACg/tTACg/cT…
u2 = TTAATTAATTAA… …
uk = CCGGCCGGCCGG…
u1= ACGTACGTACGT…
u2 = TTAATTAATTAA…
u3 = ACTACTACTACT… …
uk = CCGGCCGGCCGG
k-1 k

43. Greedy Approach: Example
Consider these 4 sequences
s1 GATTCA
s2 GTCTGA
s3 GATATT
s4 GTCAGC

44. Greedy Approach: Example (cont’d)
There are = 6 possible alignments
s2 GTCTGA
s4 GTCAGC (score = 2)
s1 GAT-TCA
s2 G-TCTGA (score = 1)
s3 GATAT-T (score = 1)
s1 GATTCA--
s4 G—T-CAGC(score = 0)
s2 G-TCTGA
s3 GATAT-T (score = -1)
s3 GAT-ATT
s4 G-TCAGC (score = -1)

45. Greedy Approach: Example (cont’d)
s2 and s4 are closest; combine:
s2 GTCTGA
s4 GTCAGC
s2,4 GTCt/aGa/cA (profile)
new set of 3 sequences:
s1 GATTCA
s3 GATATT
s2,4 GTCt/aGa/c

46. Progressive Alignment
Progressive alignment is a variation of greedy algorithm with a somewhat more intelligent strategy for choosing the order of alignments.
Progressive alignment works well for close sequences, but deteriorates for distant sequences
Gaps in consensus string are permanent
Use profiles to compare sequences

47. ClustalW Popular multiple alignment tool today
‘W’ stands for ‘weighted’ (different parts of alignment are weighted differently).
Three-step process
1.) Construct pairwise alignments
2.) Build Guide Tree
3.) Progressive Alignment guided by the tree

48. Step 1: Pairwise Alignment

49. Step 2: Guide Tree Create Guide Tree using the similarity matrix
ClustalW uses the neighbor-joining method
Guide tree roughly reflects evolutionary relations

51. Step 3: Progressive Alignment
Start by aligning the two most similar sequences
Following the guide tree, add in the next sequences, aligning to the existing alignment
Insert gaps as necessary

53. Multiple Alignment: History
1975 Sankoff
Formulated multiple alignment problem and gave dynamic programming solution
1988 Carrillo-Lipman
Branch and Bound approach for MSA
1990 Feng-Doolittle
Progressive alignment
1994 Thompson-Higgins-Gibson-ClustalW
Most popular multiple alignment program
1998 Morgenstern et al.-DIALIGN
Segment-based multiple alignment
2000 Notredame-Higgins-Heringa-T-coffee
Using the library of pairwise alignments
2004 MUSCLE

54. Practice of MSA

55. Choosing the Right Sequences
When building an alignment, it is your job to select the sequences
Two main factors when selecting sequences:
Number of sequences
Nature of the sequences
A reasonable number of sequences: 20 to 50
Ideal for most methods
Small alignments are easy to display and analyze
Types of sequences
Well-selected sequences  informative alignment

56. Some Guidelines for Choosing the Right Sequences

57. DNA or Proteins? DNA sequences are harder to align than proteins
DNA-comparison models are less sophisticated
Most methods work for both DNA and proteins
The results are less useful for DNA
If your DNA is coding, work on the translated proteins
If sequences are homologous . . .
Along their entire length  use progressive alignment methods (next slide)
In terms of local similarity  use motif-discovery methods (end of chapter)

58. Choosing Sequences That Are Different Enough
An alignment is useful if . . .
The sequences are correctly aligned
It can be used to produce trees, profiles, and structure predictions
To obtain this result, the sequences must be
Not too similar
Not too different
Sequences that are very similar . . .
Are easy to align correctly
Are not informative  useless trees and profiles, bad predictions
Sequences that are very different . . .
Are difficult to align
Are very informative  good trees and profiles, good predictions

59. Steps Gathering right sequences
Compute MSA using servers/local programs
Evaluate the results visually
If it is hard to interpret
Closer examination, remove trouble makers
Redo and trim if needed

60. Gathering Sequences with BLAST
The most convenient way to select your sequences is to use a BLAST server
Some BLAST servers are integrated with multiple-alignment methods:
(protein only)
srs.ebi.ac.uk (DNA/protein)
npsa-pbil.ibcp.fr

61. Gathering Sequences with BLAST
Select some of the top sequences
Evenly select some sequences down to the bottom
The idea is to have many intermediate sequences

62. ExPASY

68. >sp|P20472|PRVA_HUMAN Parvalbumin alpha OS=Homo sapiens GN=PVALB PE=1 SV=2
MSMTDLLNAEDIKKAVGAFSATDSFDHKKFFQMVGLKKKSADDVKKVFHMLDKDKSGFIE EDELGFILKGFSPDARDLSAKETKMLMAAGDKDGDGKIGVDEFSTLVAES
>sp|P80079|PRVA_FELCA Parvalbumin alpha OS=Felis catus GN=PVALB PE=1 SV=2
MSMTDLLGAEDIKKAVEAFTAVDSFDYKKFFQMVGLKKKSPDDIKKVFHILDKDKSGFIE EDELGFILKGFYPDARDLSVKETKMLMAAGDKDGDGKIDVDEFFSLVAKS
>sp|P02627|PRVA_RANES Parvalbumin alpha OS=Rana esculenta PE=1 SV=1
PMTDLLAAGDISKAVSAFAAPESFNHKKFFELCGLKSKSKEIMQKVFHVLDQDQSGFIEK EELCLILKGFTPEGRSLSDKETTALLAAGDKDGDGKIGVDEFVTLVSES
>sp|P02626|PRVA_AMPME Parvalbumin alpha OS=Amphiuma means PE=1 SV=1
SMTDVIPEADINKAIHAFKAGEAFDFKKFVHLLGLNKRSPADVTKAFHILDKDRSGYIEE EELQLILKGFSKEGRELTDKETKDLLIKGDKDGDGKIGVDEFTSLVAES
>sp|P02619|PRVB_ESOLU Parvalbumin beta OS=Esox lucius PE=1 SV=1
SFAGLKDADVAAALAACSAADSFKHKEFFAKVGLASKSLDDVKKAFYVIDQDKSGFIEED ELKLFLQNFSPSARALTDAETKAFLADGDKDGDGMIGVDEFAAMIKA
>sp|P43305|PRVU_CHICK Parvalbumin, thymic CPV3 OS=Gallus gallus PE=1 SV=2
MSLTDILSPSDIAAALRDCQAPDSFSPKKFFQISGMSKKSSSQLKEIFRILDNDQSGFIE EDELKYFLQRFECGARVLTASETKTFLAAADHDGDGKIGAEEFQEMVQS
>sp|Q91482|PRVB1_SALSA Parvalbumin beta 1 OS=Salmo salar PE=1 SV=1
MACAHLCKEADIKTALEACKAADTFSFKTFFHTIGFASKSADDVKKAFKVIDQDASGFIE VEELKLFLQNFCPKARELTDAETKAFLKAGDADGDGMIGIDEFAVLVKQ
>sp|P02620|PRVB_MERME Parvalbumin beta OS=Merluccius merluccius PE=1 SV=1
AFAGILADADITAALAACKAEGSFKHGEFFTKIGLKGKSAADIKKVFGIIDQDKSDFVEE DELKLFLQNFSAGARALTDAETATFLKAGDSDGDGKIGVEEFAAMVKG
>sp|P02622|PRVB_GADCA Parvalbumin beta OS=Gadus callarias PE=1 SV=1
AFKGILSNADIKAAEAACFKEGSFDEDGFYAKVGLDAFSADELKKLFKIADEDKEGFIEE DELKLFLIAFAADLRALTDAETKAFLKAGDSDGDGKIGVDEFGALVDKWGAKG

69. If Know Protein Sequences

70. Aligning Your Sequences
Aligning sequences correctly is very difficult
It’s hard to align protein sequences with less than 25% identity (70% identity for DNA)
All methods are approximate
Alignment methods use the progressive algorithm
Compares the sequences two by two
Builds a guide tree
Aligns the sequences in the order indicated by the tree

71. Selecting a Method Many alternative methods exist for MSAs
Most of them use the progressive algorithm
They all are approximate methods
None is guaranteed to deliver the best alignments
All existing methods have pros and cons
ClustalW is the most popular (21,000 citations)
T-Coffee and ProbCons are more accurate but slower
MUSCLE is very fast, ideal for very large datasets

72. Selecting a Method (cont’d.)
It’s impossible to guess in advance which method will do best.
Accuracy is merely an average estimation
Methods are tested on reference datasets
Their accuracy is the average accuracy obtained on the reference
The most accurate method can always be outperformed by a less accurate method on a given dataset.
An alternative: Use consensus methods such as MCOFFEE

73. ClustalW www.ebi.ac.uk/clustalw
pir.georgetown.edu/pirwww/search/multialn.shtml

78. Tcoffee TCOFFEE: www.tcoffee.org CORE: evaluate MSA
MCOFFEE: run many and combine
EXPRESSO: with structural information

82. Running Many Methods at Once
MCOFFEE is a a meta-method
It runs all the individual MSA methods
It gathers all the produced MSAs
It combines the MSAs into a single MSA
MCOFFEE is more accurate than any individual method
Its color output lets you estimate the reliability of your MSA
MCOFFEE is available on

83. MCOFFEE Color Output Red and orange residues are probably well aligned
Yellow should be treated with caution
Green and blue are probably incorrectly aligned

84. MCOFFEE

85. TCOFFEE

86. TCOFFEE Results

87. Interpreting Your MSA
Don’t put blind trust in the output of the servers
Specialists always edit their MSAs by hand
You must always estimate the biological accuracy of your MSA
Use the color code of Tcoffee
Use the conservation patterns of ClustalW:
‘*’ Completely conserved position
‘:’ Highly conserved position
‘.’ Conserved position
Use experimental knowledge of your proteins

88. Understanding Conserved Positions

89. Finding Information from Alignment
Conserved regions
Insert/delete
Phylogenetic Reconstruction
Motif …

90. >sp|P02586|TNNC2_RABIT Troponin C, skeletal muscle OS=Oryctolagus cuniculus GN=TNNC2 PE=1 SV=2
MTDQQAEARSYLSEEMIAEFKAAFDMFDADGGGDISVKELGTVMRMLGQTPTKEELDAII EEVDEDGSGTIDFEEFLVMMVRQMKEDAKGKSEEELAECFRIFDRNADGYIDAEELAEIF RASGEHVTDEEIESLMKDGDKNNDGRIDFDEFLKMMEGVQ
>sp|P20472|PRVA_HUMAN Parvalbumin alpha OS=Homo sapiens GN=PVALB PE=1 SV=2
MSMTDLLNAEDIKKAVGAFSATDSFDHKKFFQMVGLKKKSADDVKKVFHMLDKDKSGFIE EDELGFILKGFSPDARDLSAKETKMLMAAGDKDGDGKIGVDEFSTLVAES
>sp|P80079|PRVA_FELCA Parvalbumin alpha OS=Felis catus GN=PVALB PE=1 SV=2
MSMTDLLGAEDIKKAVEAFTAVDSFDYKKFFQMVGLKKKSPDDIKKVFHILDKDKSGFIE EDELGFILKGFYPDARDLSVKETKMLMAAGDKDGDGKIDVDEFFSLVAKS
>sp|P02627|PRVA_RANES Parvalbumin alpha OS=Rana esculenta PE=1 SV=1
PMTDLLAAGDISKAVSAFAAPESFNHKKFFELCGLKSKSKEIMQKVFHVLDQDQSGFIEK EELCLILKGFTPEGRSLSDKETTALLAAGDKDGDGKIGVDEFVTLVSES
>sp|P02626|PRVA_AMPME Parvalbumin alpha OS=Amphiuma means PE=1 SV=1
SMTDVIPEADINKAIHAFKAGEAFDFKKFVHLLGLNKRSPADVTKAFHILDKDRSGYIEE EELQLILKGFSKEGRELTDKETKDLLIKGDKDGDGKIGVDEFTSLVAES
>sp|P02619|PRVB_ESOLU Parvalbumin beta OS=Esox lucius PE=1 SV=1
SFAGLKDADVAAALAACSAADSFKHKEFFAKVGLASKSLDDVKKAFYVIDQDKSGFIEED ELKLFLQNFSPSARALTDAETKAFLADGDKDGDGMIGVDEFAAMIKA
>sp|P43305|PRVU_CHICK Parvalbumin, thymic CPV3 OS=Gallus gallus PE=1 SV=2
MSLTDILSPSDIAAALRDCQAPDSFSPKKFFQISGMSKKSSSQLKEIFRILDNDQSGFIE EDELKYFLQRFECGARVLTASETKTFLAAADHDGDGKIGAEEFQEMVQS
>sp|Q91482|PRVB1_SALSA Parvalbumin beta 1 OS=Salmo salar PE=1 SV=1
MACAHLCKEADIKTALEACKAADTFSFKTFFHTIGFASKSADDVKKAFKVIDQDASGFIE VEELKLFLQNFCPKARELTDAETKAFLKAGDADGDGMIGIDEFAVLVKQ
>sp|P02620|PRVB_MERME Parvalbumin beta OS=Merluccius merluccius PE=1 SV=1
AFAGILADADITAALAACKAEGSFKHGEFFTKIGLKGKSAADIKKVFGIIDQDKSDFVEE DELKLFLQNFSAGARALTDAETATFLKAGDSDGDGKIGVEEFAAMVKG
>sp|P02622|PRVB_GADCA Parvalbumin beta OS=Gadus callarias PE=1 SV=1
AFKGILSNADIKAAEAACFKEGSFDEDGFYAKVGLDAFSADELKKLFKIADEDKEGFIEE DELKLFLIAFAADLRALTDAETKAFLKAGDSDGDGKIGVDEFGALVDKWGAKG

92. When Sequences Are Hard to Align
Most MSA programs assume your sequences are related along their whole length
When this assumption is not true, the progressive approach will not work
The only alternative is to compare multiple sequences locally

93. Local Multiple-Comparison Methods
Gibbs Sampler
Will make a local multiple alignment
Will ignore unrelated segments of your sequences
Ideal for finding DNA patterns such as promoters
Motif discovery methods
Will look for motifs conserved in your sequences
The sequences do not need to be aligned
The most popular motif-discovery methods:
TEIRESIAS, MEME, SMILE, PRATT