1. Bioinformatics Sequence Analysis III
Ulf Schmitz
Bioinformatics and Systems Biology Group
Ulf Schmitz, Sequence Analysis III

2. Ulf Schmitz, Sequence Analysis III
Outline
Multiple sequence alignment
introduction to msa
methods of msa
progressive global alignment
Iterative methods
Alignments based on locally conserved patterns
Ulf Schmitz, Sequence Analysis III

3. Methods pairwise sequence alignmentno no no
choose two sequences
are the sequences protein sequences?
do sequences encode proteins (e.g. cDNA)?
does sequence encode proteins and have introns?
Methods pairwise sequence alignment
yes
yes
yes
perfom local alignment
translate sequences
predict gene structure
is alignment of high quality? no
alter parameters e.g. scoring matrix, gap penalties, and repeat alignment
yes
perform statistical test of alignment score
examine sequences for presence of repeats or low-complexity sequences
yes
did alignment improve? no
is alignment score significant? no
sequences are not detectably similar
yes
sequences are significantly similar
Ulf Schmitz, Sequence Analysis III

4. Multiple Sequence Alignment
Motivation
DNA sequences of different organisms are often related
Similar genes performing similar function
Genes are represented in highly conserved forms in organisms
Through simultaneous alignment of the sequences of the genes, sequence patterns may be analyzed
Ulf Schmitz, Sequence Analysis III

5. Multiple Sequence Alignment
things to consider
2 protein sequences length = 300, excluding gaps
number of comparisons by dynamic programming
3 protein sequences length = 300, excluding gaps
number of comparisons by dynamic programming
number of steps and memory required for a 300-aminmo-acid sequence = 300N,
where N is the number of sequences
Ulf Schmitz, Sequence Analysis III

6. Relationship of MSA to Phylogenetic analysis
once the msa has been found, the number or types of changes in the aligned sequences may be used for a phylogenetic analysis
seqA N – F L S
seqB N – F – S
seqC N K Y L S
seqD N – Y L S
N Y L S
N K Y L S
N F S
N F L S
+ K
- L
Y to F
hypothetical evolutionary tree that could have generated three sequence changes
Ulf Schmitz, Sequence Analysis III

7. Phylogenetic analysis
Ulf Schmitz, Sequence Analysis III

8. Ulf Schmitz, Sequence Analysis III
MSA methods
Approximate methods are used:
progressive global alignment
starting with an alignment of the most alike sequences and then building an alignment by adding more sequences
Iterative methods
makes an initial alignment of groups of sequences and then revises the alignment to achieve a more reasonable result
Alignments based on locally conserved patterns
statistical methods
probabilistic models of sequences
Ulf Schmitz, Sequence Analysis III

9. Ulf Schmitz, Sequence Analysis III
MSA Tools
Name
Source
Global alignments including progressive
CLUSTALW or CLUSTALX (latter has graphical interface)
ftp.ebi.ac.uk/pub/software/unix
MSA
ftp://fastlink.nih.gov/pub/msa
PRALINE
Iterative and other methods
DIALIGN segment alignment
MultAlin
SAGA genetic algorithm
Ulf Schmitz, Sequence Analysis III

10. Ulf Schmitz, Sequence Analysis III
MSA Tools
Name
Source
Local alignments of proteins
BLOCKS Web site
HMMER hidden Markov model software
MEME Web site, expectation maximization method
eMOTIF web server
GIBBS, the Gibbs sampler statistical method
ftp://ftp.ncbi.nlm.nih.gov/pub/neuwald/gibbs9_95/
Aligned Segment Statistical Evaluation Tool (Asset)
ncbi.nlm.nih.gov/pub/neuwald/asset
SAM hidden Markov model web site
Ulf Schmitz, Sequence Analysis III

11. Ulf Schmitz, Sequence Analysis III
MSA scoring
Another computational challenge is identifying a reasonable method of obtaining a cumulative score for the substitutions in the columns of a msa
And also the placement and scoring of gaps in various sequences of an msa
one method for optimizing the msa by
maximizing the number of matched pairs summed over all columns in the msa
Ulf Schmitz, Sequence Analysis III

12. MSA scoring with the SP model
the method assumes a model for evolutionary change in which any of the sequences could be the ancestor of the others
Sequence Column A Column B Column C
N N N
N N N
N N N
N N C
N C C N N N C N N N C
Column A
Column B
Column C
No. of N - N matched pairs (each scores 6):
No. of N - C matched pairs (each scores -3):
BLOSUM62 score:
Ulf Schmitz, Sequence Analysis III

13. Progressive multiple sequence alignment
alignment on each of the pairs of sequences
next, trail msa is produced by first predicting a phylogenetic tree for the sequences
sequences are then multiply aligned in order of their relationship on the tree
starting with the most related sequences
then progressively adding less related sequences to the initial alignment
used by PILEUP and CLUSTALW
not guaranteed to be optimal
Ulf Schmitz, Sequence Analysis III

14. Progressive msa - general principles1
Score 1-2 2 1
Score 1-3 3 4
Score 4-5 5
Scores
5×5
Similarity matrix
Scores to distances
Iteration possibilities
Guide tree
Multiple alignment
Ulf Schmitz, Sequence Analysis III

15. General progressive msa technique (follow generated tree)1 3 1 3 2 5 1 3 2 5
root 1 3 2 5 4
Ulf Schmitz, Sequence Analysis III

16. Ulf Schmitz, Sequence Analysis III
CLUSTALW / CLUSTALX
‘W’ stands for “weighting”
ability to provide weights to sequence and program parameters
CLUSTALX – with graphical interface
provides global msa
Not constructed to perform local alignments.
Similarity in small regions is a problem.
Problems with large insertions.
Problems with repetitive elements, such as domains.
ClustalW does not guarantee an optimal solution
Ulf Schmitz, Sequence Analysis III

17. Ulf Schmitz, Sequence Analysis III
PILEUP
very similar to CLUSTALW
part of the genetic computer group (GCG)
does not guarantee optimal alignment
plots a cluster dendogram of similarities betwenn sequences
This is not an evolutionary tree!
Ulf Schmitz, Sequence Analysis III

18. limits of progressive alignment
initial pairwise alignment
the very first sequences to be aligned are the most closely related in the tree
if they align well, there will be few errors
the more distantly related the more errors
choice of suitable scoring matrices and gap penalties
when to use progressive alignment?
for more closely related sequences
large number of sequences
Ulf Schmitz, Sequence Analysis III

19. Iterative methods of msa
repeatedly realigns subgroups of sequences
then aligning these subgroups into global alignment of all the sequences
aim is to improve the overall alignment score
selection of groups is based on the phylogenetic tree
separation of one or two sequences from the rest
similar to that of progressive alignment
Ulf Schmitz, Sequence Analysis III

20. Localized alignments in Sequences
1st
profile analysis
2nd
blocks analysis
3rd
pattern-searching or statistical methods
Ulf Schmitz, Sequence Analysis III

21. Ulf Schmitz, Sequence Analysis III
Profile analysis
is a sequence comparison method for finding and aligning distantly related sequences
Finding new family members
Profile = position-specific scoring table
from global MSA of a group of sequences more highly conserved regions are removed into a smaller MSA
a scoring matrix (called profile) is then made
Ulf Schmitz, Sequence Analysis III

22. Ulf Schmitz, Sequence Analysis III
Profile analysis
A profile is used to search a target sequence for possible matches to the profile
Scores in the table are used to evaluate the likelihood at each position
e.g. a profile that is 25 amino acids long will have 25 rows of 20 scores
each score in a row for matching one of the amino acids at the corresponding position in the profile
Ulf Schmitz, Sequence Analysis III

23. Ulf Schmitz, Sequence Analysis III
Profile example
Con A C D E F G H I K L M N P Q R S T V W Y 8 -2 5 4 -4 24 15 13 1 -7 2 22 21
-18 -6 -5 18 19 7 14 11 10 -1 9 29 3
-28
-14 12
-10 17
-12 6 -9 34 -8
-15
– Each column is independent
– Average Method: profile matrix values are weighted by the proportion of each amino acid
in each column of MSA
– Evolutionary Method: calculate the evolutionary distance (Dayhoff model) required to
generate the observed amino acid distribution
Ulf Schmitz, Sequence Analysis III

24. Ulf Schmitz, Sequence Analysis III
Profile analysis
Disadvantages:
Profile extraction from an msa is only as representative of the variation in the family of sequences as the msa itself.
If several sequences are similar, the derived profile will be based in favor of those sequences
Solution: sequences are weighted by the distance of relation based on a phylog. tree
Some amino acids may not be represented in a column because not enough sequences have been included
Ulf Schmitz, Sequence Analysis III

25. Ulf Schmitz, Sequence Analysis III
Block analysis
like profiles, blocks represent a conserved region in msa
but they don’t consider deletions and insertions
Instead columns include only matches and mismatches
Blocks are made by searching an alignment for sections that are highly conserved
no scoring matrices are used
Ulf Schmitz, Sequence Analysis III

26. Ulf Schmitz, Sequence Analysis III
Blocks
Gapless alignment blocks
Ulf Schmitz, Sequence Analysis III

27. Ulf Schmitz, Sequence Analysis III
Block analysis
Extraction of Blocks from a global or local msa
Global msa of related sequences usually include regions without gaps in any of the sequences
These ungapped patterns are extracted and used to build blocks
These blocks are only as good as the msa from which they are derived
The BLOCKS server ( extracts blocks of width from a protein MSA of up to 400 sequences.
Ulf Schmitz, Sequence Analysis III

28. Ulf Schmitz, Sequence Analysis III
Block analysis
conserved patterns in protein or dna
sequences can be represented by sequence
logos
the horizontal scale represents sequential
positions in the motif
height of a amino acid is proportional to the
frequency of the amino acid in the column
Amino acids are shown in decreasing order of
abundance from the top
Extractable information:
consensus may be read across the columns as
the top amino acid in each column
Relative frequency of each amino acid
height of a column provides measure of how
useful that column is for reducing the level of
uncertainty
Ulf Schmitz, Sequence Analysis III

29. Methods multiple sequence alignment
yes
choose three or more sequences
is a convincing alignment produced?
are the sequences protein sequences?
perfom global alignment
yes
Methods multiple sequence alignment no
are there large number of sequences?
yes
do sequences encode proteins (e.g. cDNA)?
translate sequences no no no
make a profile or PSSM representation of the alignment
yes
predict gene structure
are the sequences genomic sequences that encode related proteins?
produce a hidden markov model. no
yes
analyze promoter regions, inton-exon boundaries, etc. no
do the sequences encode RNA molecules?
analyze for patterns, repeats, etc.
yes
search for blocks
analyze for secondary structure
Ulf Schmitz, Sequence Analysis III

30. Ulf Schmitz, Sequence Analysis III
Outlook
Statistical methods and probabilistic models
Expectation Maximization Algorithm
the Gibbs Sampler
Hidden Markov Models
Ulf Schmitz, Sequence Analysis III

31. Ulf Schmitz, Sequence Analysis III
Sequence Alignment
Thanks for your attention!
Ulf Schmitz, Sequence Analysis III