1. Multiple Alignment, Distance Estimation, and Phylogenetic Analysis
Database search (keyword, similarity)
Conserved Regions
Multiple alignment
• find oligonucleotide primers for PCR
• predict secondary and tertiary structures of new sequences
• detect similarity between new sequences and existing sequence families
• find diagnostic patterns to characterize protein families
Distance estimation
Phylogenetic reconstruction
Function Prediction
April 2, 2004
BIOS816/VBMS818

2. Distance estimation ACTGTAGGAATCGC :X::X:X::::::: AATGAAAGAATCGC
nd = 3
L = 14
p = 3/14 = 0.214
The easiest
Number of nucleotide substitutions per site (p)
p = nd / L
nd: the number of different nucleotides
between the two sequences
L: the number of nucleotides compared
• It can be applied both for DNA and protein sequences
April 2, 2004
BIOS816/VBMS818

3. Distance estimation AATGTAGGAATCGC ACTGTAGGAATCGC AATGAAAGAATCGC
Ancestral
ACTGTAGGAATCGC
AATGAAAGAATCGC
April 2, 2004
BIOS816/VBMS818

4. Distance estimation AATGTAGGAATCGC ACTGTAGGAATCGC AATGAAAGAATCGC
Single
substitution
Ancestral
ACTGTAGGAATCGC
AATGAAAGAATCGC
April 2, 2004
BIOS816/VBMS818

5. Distance estimation AATGTAGGAATCGC ACTGTAGGAATCGC AATGAAAGAATCGC
Single
substitution
ACTGTAGGAATCGC
AATGAAAGAATCGC
April 2, 2004
BIOS816/VBMS818

6. Distance estimation AATGTAGGAATCGC ACTGTAGGAATCGC AATGAAAGAATCGC
Single
substitution
ACTGTAGGAATCGC
AATGAAAGAATCGC
April 2, 2004
BIOS816/VBMS818

7. No hidden substitution
Distance estimation
AATGTAGGAATCGC
ACTGTAGGAATCGC
AATGAAAGAATCGC
3 substitutions
No hidden substitution
April 2, 2004
BIOS816/VBMS818

8. Distance estimation AATGTAGGAATCGC G ACTGTAGGAATCGC AATGAAAGAATCGC
2 substitutions G
ACTGTAGGAATCGC
AATGAAAGAATCGC
April 2, 2004
BIOS816/VBMS818

9. Distance estimation AATGTAGGAATCGC G C ACTGTAGGAATCGC AATGAAAGAATCGC
2 substitutions
2 substitutions G C
ACTGTAGGAATCGC
AATGAAAGAATCGC
April 2, 2004
BIOS816/VBMS818

10. Distance estimation AATGTAGGAATCGC G C ACTGTAGGAATCGC AATGAAAGAATCGC
2 substitutions
2 substitutions G C
ACTGTAGGAATCGC
AATGAAAGAATCGC
Observed number of differences = 3
April 2, 2004
BIOS816/VBMS818

11. Actual number of substitutions = 6
Distance estimation
AATGTAGGAATCGC
2 substitutions
2 substitutions G C
Multiple hit!
ACTGTAGGAATCGC
AATGAAAGAATCGC
Observed number of differences = 3
<
Actual number of substitutions = 6
April 2, 2004
BIOS816/VBMS818

12. Effect of multiple substitutions (hits)
(Actual number of substitutions)
Actual divergence
Divergence
Time
April 2, 2004
BIOS816/VBMS818

13. Effect of multiple substitutions (hits)
(Actual number of substitutions)
Actual divergence
Divergence
Observed divergence
Time
April 2, 2004
BIOS816/VBMS818

14. Effect of multiple substitutions (hits)
(Actual number of substitutions)
Actual divergence
Divergence
Observed divergence
Actual = Observed
Time
April 2, 2004
BIOS816/VBMS818

15. Effect of multiple substitutions (hits)
Actual > Observed
(Actual number of substitutions)
Actual divergence
Divergence
Observed divergence
Actual = Observed
Time
April 2, 2004
BIOS816/VBMS818

16. Effect of multiple substitutions (hits)
Actual >> Observed
(Actual number of substitutions)
Actual divergence
Divergence
Observed divergence
Actual = Observed
Time
April 2, 2004
BIOS816/VBMS818

17. Distance estimation with multiple-hit corrections
(nucleotide substitutions)
Jukes-Cantor method
(one-parameter method)
Kimura’s 2-parameter
method A C G T -  A C G T -  
k = -3/4ln(1-4p/3)
k: the expected number of nucleotide substitutions per site
p: the proportion of nucleotide differences
All substitutions are equally likely
Transitions and Transversions
have different rates
k = -1/2ln[1/(1-2P-Q)]+1/4ln[1/(1-2Q)]
P: the proportion of transitional (Ts) differences
Q: the proportion of transversional (Tv)
differences
April 2, 2004
BIOS816/VBMS818

18. Distance estimation with multiple-hit corrections
(nucleotide substitutions)
k = -3/4ln(1-4p/3)
If p ≥ 0.75,
JC distance cannot be estimated
k (Jukes-Cantor distance)
(k = p)
p = 0.75
p (uncorrected nucleotide difference)
April 2, 2004
BIOS816/VBMS818

19. Distance estimation with multiple-hit corrections
(nucleotide substitutions)
There are many distance estimation methods based on different models.
1. More parameters:
• 1-parameter (Jukes-Cantor method)
• 2-parameter (Kimura’s 2-p method)
• 3, 4, 6, ... up to 12 parameters!!
2. Variation in base composition (A  C  G  T):
• 1-p & base comp. (Tajima & Nei or F81 method)
• 2-p & base comp. (HKY85 or F84 method), etc.
3. Rate-variation among sites:
approximated by a gamma-distribution
CV (coefficient of variation of the rate): smaller  less variation
• 1-p & rate variation (Jin & Nei method), etc.
4. LogDet method:
• No constrains on parameters, base composition can be varied among sequences
• No among-site rate variation can be considered A C G T -
C
G
T
A
G
T
A
C
April 2, 2004
BIOS816/VBMS818

20. Distance estimation with multiple-hit corrections
(nucleotide substitutions)
Which distance method should we choose?
Substitution pattern (e.g., Ts/Tv)
Things to consider: Base composition bias
Rate-heterogeneity among sites
1. More parameters  more flexible, more realistic
2. More parameters 
larger sampling errors (lower precision)
3. More parameters 
more “undefined” distance problem
(e.g., if p ≥ 0.75 in JC method, k becomes “undefined” or
“infinite”) [k = -3/4ln(1-4p/3)]
April 2, 2004
BIOS816/VBMS818

21. Distance estimation with multiple-hit corrections
(amino acid substitutions)
1. Poisson distance: k = -ln(1-p)
k: the expected number of amino acid substitutions per site
p: the proportion of amino acid differences
2. Kimura’s distance: k = -ln(1-p-0.2p2)
• Approximation of PAM distance below (accurate when p < 0.75)
• Distance becomes infinite when p ≥
3. PAM distance & JTT distance
• Distance based on PAM or JTT amino acid substitution matrix
• JTT matrix is newer and based on much larger protein sample
NOTE for ClustalW/ClustalX Kimura’s distance
Hybrid between Kimura’s and PAM distances
p ≤ 0.75
Use Kimura’s correction
0.75 < p ≤ 0.93
Use a conversion table with 0.01 interval (.75, .751, ...)
0.93 ≤ p
k = 10.0
April 2, 2004
BIOS816/VBMS818

22. Phylogenetic reconstruction methods
Neighbor Joining
(NJ)
Maximum Parsimony
(MP)
Maximum Likelihood
(ML)
Data type:
Distance
Minimum evolution
(shortest total branch length)
*NJ does not search the ME tree. NJ provides a simplified (approximated) algorithm to find the ME tree.
Sequence (or other) data
Maximum parsimony (smallest number of evolutionary changes)
Maximum Likelihood (highest probability of observing the data under a given tree and a given model of substitutions)
Optimality
criterion:
Fastest
Slowest
April 2, 2004
BIOS816/VBMS818

23. Phylogenetic reconstruction: distance matrix methods
UPGMA
(unweighted pair-group method with arithmetic mean)
Example: a distance matrix for 5 sequences. B C D E
C/D
A/B/E A
.53
.99
1.02
.82
A/B
.90
.98
.78
.94
.80
.93
.73
.65

.86
.81

The pair with the smallest distance is grouped  until all of the sequences are clustered in a tree
• assumes all sequences evolve at the same rate
• generates a rooted tree
April 2, 2004
BIOS816/VBMS818

24. Phylogenetic reconstruction: distance matrix methods
NJ (neighbor joining method)
Example: a distance matrix for 5 sequences. B C D E A
.53
.99
1.02
.82
.80
.93
.73
.65
.81
.94
1) Start with a star-like phylogeny.
2) The total length of the tree (the sum of the branch lengths) is estimated.
3) Find neighbors sequentially that minimize the total length of the tree.
• does not assume a constant rate
• generates a unrooted tree
April 2, 2004
BIOS816/VBMS818

25. Rooted trees vs. unrooted treesA B C D
(Time) A B C D A B C D
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BIOS816/VBMS818

26. Rooted trees vs. unrooted treesA B C D
(Time) A B C D
Root A B C D B A C D
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BIOS816/VBMS818

27. Rooted trees vs. unrooted treesA B C D
(Time) A B C D A B C D B A C D C D A B
April 2, 2004
BIOS816/VBMS818

28. Rooted trees vs. unrooted treesA B C D
(Time) A B C D
Root A B C D
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BIOS816/VBMS818

29. Rooted trees vs. unrooted treesA B C D
(Time) A B C D A B C D
Outgroup
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30. Phylogenetic reconstruction: bootstrap analysis
used to estimate the confidence level of phylogenetic hypotheses
Multiple alignment S5 S4 S3 S2 S1 T C 8 G A 1 2 3 7 6 5 4
Site #
April 2, 2004
BIOS816/VBMS818

31. Phylogenetic reconstruction: bootstrap analysis
used to estimate the confidence level of phylogenetic hypotheses T C 8 G A 1 2 3 7 6 5 4
Each column is independent sample
April 2, 2004
BIOS816/VBMS818

32. Phylogenetic reconstruction: bootstrap analysis
used to estimate the confidence level of phylogenetic hypotheses T C 8 G A 1 2 3 7 6 5 4
S1 GGAGGTTA S1 CGCAGCAC S1 TTTTGGCG ... Many
S2 GGAGGTTA S2 CGCAGTAT S2 TTTTGGTG resamplings
S3 AAAAACTA S3 CACAACAC S3 CTCTAACA (~1000 replications)
S4 AAAAATCA S4 CACAACAC S4 TCTCAACA
S5 GGAGGTCG S5 TGTAGCGC S5 TCTCGGCG
April 2, 2004
BIOS816/VBMS818

33. Phylogenetic reconstruction: bootstrap analysis
used to estimate the confidence level of phylogenetic hypotheses
S1 GGAGGTTA S1 CGCAGCAC S1 TTTTGGCG ... Many
S2 GGAGGTTA S2 CGCAGTAT S2 TTTTGGTG resamplings
S3 AAAAACTA S3 CACAACAC S3 CTCTAACA (~1000 replications)
S4 AAAAATCA S4 CACAACAC S4 TCTCAACA
S5 GGAGGTCG S5 TGTAGCGC S5 TCTCGGCG S1 S2 S3 S4 S5 S1 S2 S3 S4 S5 S1 S2 S3 S4 S5
...
A phylogeny is reconstructed from each pseudoreplicate
April 2, 2004
BIOS816/VBMS818

34. Phylogenetic reconstruction: bootstrap analysis
used to estimate the confidence level of phylogenetic hypotheses S1 S2 S3 S4 S5 S1 S2 S3 S4 S5 S1 S2 S3 S4 S5
...
Bootstrap support (%)
100 S1
100 S1 S2 S2 S3 S3 S4 S4 40 S5 S5
April 2, 2004
BIOS816/VBMS818

35. Phylogenetic reconstruction by ClustalX/W & Phylip
Phylogeny programs:
ClustalW/ClustalX (DNA/protein distance, NJ)
Phylip3.5 & Phylip3.6a3 (standalone, web-interface)
PAUP (also included in GCG)
Visualization: Phylip (treegram, etc.), TreeView, PAUP, NJplot
More phylogeny programs 
April 2, 2004
BIOS816/VBMS818

36. Phylip programs Bootstrap: seqboot (sequence data)
DNA distance: dnadist (nucleotide sequence data)
Protein distance: protdist (amino acid sequence data)
Neighbor joining: neighbor (distance matrix)
Consensus tree: consense (tree file)
Tree drawing: drawgram, drawtree, retree (treefile)
Phylip3.5
Phylip3.6a3
Input file: infile infile, intree
Output file: outfile, treefile outfile, outtree
dnadist: Kimura, Jin/Nei, ML (F84), F84, Kimura, JC, LogDet
JC (rate variation can be incorporated
with all but LogDet)
protdist: Kimura, PAM (Dayhoff) Kimura, PAM (Dayhoff), JTT
April 2, 2004
BIOS816/VBMS818

37. Phylogenetic reconstruction by ClustalW & Phylip
Bioinformatics Core Facility Web server (Phylip3.5)
Bioinformatics Web: IU Center for Genomics & Bioinformatics (Phylip3.5)
Institut Pasteur, Biological Software list (Phylip3.6a3)
Phylip download site (Windows, Macintosh, Linux/Unix)
Phylip3.5:
Phylip3.6b:
TreeView download site (Windows, Macintosh, Linux/Unix)
NJPlot download site (Windows, Macintosh, Linux/Unix)
April 2, 2004
BIOS816/VBMS818

38. Multiple Alignment by CLUSTALW
Bioinformatics Core Facility Web server
Bioinformatics Web: IU Center for Genomics & Bioinformatics
Institut Pasteur, Biological Software list
EMBL-EBI ClustalW Form
ClustalX FTP site (Windows, Macintosh, Linux/Unix)
ftp://ftp-igbmc.u-strasbg.fr/pub/ClustalX/
April 2, 2004
BIOS816/VBMS818

39. ClustalW/Phylip Exercise
1. Download the two sample data from the course web site:
bglobin.seq - protein sequences
Dloop.seq - DNA sequences
[Use either DOS format or non-DOS format whichever the ones that work for you.]
 These sequences are in FASTA format.
>HBB_HUMAN
VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKV
KAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGK
EFTPPVQAAYQKVVAGVANALAHKYH
>HBB_HORSE
VQLSGEEKAAVLALWDKVNEEEVGGEALGRLLVVYPWTQRFFDSFGDLSNPGAVMGNPKV
KAHGKKVLHSFGEGVHHLDNLKGTFAALSELHCDKLHVDPENFRLLGNVLVVVLARHFGK
DFTPELQASYQKVVAGVANALAHKYH
April 2, 2004
BIOS816/VBMS818

40. ClustalW/Phylip Exercise (continued)
2. Go to this ClustalW web site:
3. Enter your address.
4. Copy and paste bglobin.seq data.
5. Check “Phylip alignment ouput format”.
6. Check the available options.
7. Click “Run clustalw” button to start the alignment.
Wait until the page changes to the results page...
8. Click “infile.phy” link to open the multiple alignment in Phylip format.
9. From the pull-down menu, choose “protdist” program.
April 2, 2004
BIOS816/VBMS818

41. ClustalW/Phylip Exercise (continued)
10. Click “Run the selected program on infile.phy” button to start protdist.
11. Choose “Jones-Taylor-Thornton (JTT) matrix” as the distance model.
12. Check “Perform a bootstrap ...” option.
Enter a “Random number seed”
Enter 10 as the number of replicates.
 For the real analysis, use more than 500 or more (~1000).
13. Click “Run protdist” button to run the program.
Wait until the page changes to the results page...
14. Click “outfile” link to check the distance matrix file you generated.
15. From the pull-down menu, choose “neighbor” program.
April 2, 2004
BIOS816/VBMS818

42. ClustalW/Phylip Exercise (continued)
16. Click “Run the selected program on outfile” button to start neighbor.
17. Choose “Neighbor-joining” from the distance method.
18. Check Randomize (jumble) input order.
Enter a “Random number seed”
 Using “randomization” option slows down the program. But usually it is a better idea to use this option to avoid any artifact.
19. Check “Analyze multiple data set”.
 If you are not doing boostrap analysis, you don’t have to check this option.
Enter the number of data set (10 for this example)
Check “Compute a consense tree”
20. Click “Run neighbor” to run the program.
Wait until the page changes to the results page...
April 2, 2004
BIOS816/VBMS818

43. ClustalW/Phylip Exercise (continued)
21. Click “outfile.consense” to open the output file.
22. Click “outtree.consense” to open the output file.
 Note that the numbers after the taxon names are not branch lengths but bootstrap values.
23. Click “outtree” to open the output file. This file contain 10 trees based on bootstrapped alignment. Save the first tree in a file to use it for TreeView demonstration.
 We are using this tree as an example. For the real analysis, you should create a NJ tree without doing bootstrap analysis to create a real NJ tree from the original multiple alignment.
 In the item 12, uncheck “Perform a bootstrap ...” option to generate the NJ tree without bootstrap analysis.
April 2, 2004
BIOS816/VBMS818

44. TreeView Exercise
24. Find “TreeView” software on your machine and start the program.
25. From File menu, open the tree file you saved.
26. Try to click different tree icons to change the phylogeny format.
 Which format shows different branch lengths?
27. From Tree menu, select “Define outgroup”
Choose one sequence as an outgroup.
28. From Tree menu, select “Root with outgroup”
28. From Edit menu, select “Edit tree”. Check how you can edit your tree.
The assignment from my lectures (Assignment #4) is found in the Blackboard
Assignment page .
April 2, 2004
BIOS816/VBMS818