From Pairwise To

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From Pairwise To Multiple Sequence Alignments
Workshop, January 2008 Maya Schushan
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Outline
1. Introduction
2. Applications
3. General Alignment Methodology

• 4. Pairwise Alignment
• Smith-Waterman
• Needlman-Wunch

• 5. Multiple Sequence Alignment
• ClustalW
• MUSCLE
• T-coffee

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What Is An Alignment?
Introduction

• Comparing 2 (pairwise) or more (multiple)
  sequences.
• Searching for a series of identical or similar
  characters in the sequences.

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Introduction
What Is An Alignment?
A process of lining-up 2 or more sequences to
achieve maximum level of identity, in order to
find homologies.
T C A T G C A T T G
?
T C A T G C A T T G
T C A T G C A T T G
or
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Introduction
Basic Terms

• Homology
• Relation of sequences which is a result of
  divergence from a common ancestor.

• Identity
• Sequences or Sub-sequences that are invariant.

• Similarity
• Sequences or Sub-sequences that are related.

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Introduction
Homologues Orthology vs Paralogy



Reproduced from NCBI education website
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Introduction
The Limits of Sequence Similarity
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Outline
1. Introduction
2. Applications
3. General Alignment Methodology

• 4. Pairwise Alignment
• Smith-Waterman
• Needlman-Wunch

• 5. Multiple Sequence Alignment
• ClustalW
• MUSCLE
• T-coffee

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Applications
Why Sequence Alignment?

• Predict characteristics of a protein
• Use the structure or function information on
  homologous protein to predict structure\function
  of an unknown protein.
• Similar sequences ? similar proteins (?)
• Conserved vs. variable regions ? functional site

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Applications
Why Sequence Alignment?

• Predict characteristics of a protein

VTISCTGSSSNIGAG-NHVKWYQQLPG VTISCTGSSSNIGS--ITVNW
YQQLPG LRLSCTGSGFIFSS--YAMYWYQQAPG LSLTCTGSGTSFDD-
QYYSTWYQQPPG
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Applications
Why Sequence Alignment?
A model is generated according to a template
structure of a homologous protein
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Applications
Why Sequence Alignment?

• 2. Learn about evolutionary relationships
• Two sequences from different organisms are
  similar ? they may have a common ancestor.
• Needed for construction of phylogenetic trees

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Applications
Why Sequence Alignment?

• Research of disease
• Comparison of sequences between individuals can
  detect changes that are related to diseases
• Analysis of residues substitutions mutation or
  polymorphism?

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Applications
Why Sequence Alignment?

• Examples for specific applications
• Evolutionary conservation analysis
  (ConSeq/ConSurf)
• Motif and domain prediction (Prosite/InterPro/Pfam
  )
• Phylogenetic trees

ConSurf analysis of PDB entry 1hyt-hydrolase
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Outline
1. Introduction
2. Applications
3. General Alignment Methodology

• 4. Pairwise Alignment
• Smith-Waterman
• Needlman-Wunch

• 5. Multiple Sequence Alignment
• ClustalW
• MUSCLE
• T-coffee

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Sequence Modifications
General Alignment Methodology

• 1. Insertion - an insertion of a letter or
  several letters to the
• sequence.
• AAGA? AAGTA
• 2. Deletion - deleting a letter (or more) from
  the sequence.
• AAGA? AGA
• 3. Substitution - replacing a sequence letter by
  another.
• AAGA? AACA

INEDL- Insertions Deletions
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General Alignment Methodology
Measuring An Alignment
S ACTG S AC_TG S ACTG S ACTG T
AGT T A_GT_ T AGT_ T _AGT
Good Identical characters- match. Bad Different
characters- mismatch gap (InDel).

• Each pair of characters gets a value, depending
  on its identity.
• The similarity score of the alignment is the sum
  of pair values.

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Example Aligning Two Globins
General Alignment Methodology

• Human Hemoglobin (HH)
• VLSPADKTNVKAAWGKVGAHAGYEG
• Sperm Whale Myoglobin (SWM)
• VLSEGEWQLVLHVWAKVEADVAGHG

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Example Aligning Two Globins
General Alignment Methodology

• No Gaps
• Percent identity 36
• Percent similarity 40

• (HH) VLSPADKTNVKAAWGKVGAHAGYEG
• (SWM) VLSEGEWQLVLHVWAKVEADVAGHG

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Example Aligning Two Globins
General Alignment Methodology

• With Gaps
• Gaps 2
• Percent identity 45.833 (instead of 36 without
  gaps)
• Percent similarity 54.167 (instead of 40
  without gaps)

• (HH) VLSPADKTNVKAAWGKVGAH-AGYEG
• (SWM) VLSEGEWQLVLHVWAKVEADVAGH-G

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General Alignment Methodology
Types of Gap Penalties
(insertions or deletions)

• InDels are rare in evolution once created, easy
  to
• extend
• Gap open penalty for the first residue in a
  gap
• Gap extension penalty for additional residue
  in a gap.

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General Alignment Methodology
Types of Gap Penalties
Motivation Aligning cDNAs to Genomic DNA
cDNA query
Genomic DNA
Conclusion gap opening and extension should be
ranked differently to properly align the sequences
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Alignment Scoring
General Alignment Methodology

• 1. Assume independent mutation model
• 2. Score at each position
• Positive if the same/similar
• Negative if different or gap
• 3. Score of an alignment is sum of position score

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Scoring Matrix
General Alignment Methodology

• A matrix n ? n n4 for DNA, n20 for proteins
• Each entry matrix defines the score for observing
  the two letters in the alignment
• Positive if likely to change
• Negative otherwise

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Alignment Scoring
General Alignment Methodology

• Different scoring ? different best alignments
• Scoring systems implicitly represent a particular
  theory of evolution
• Some mismatches are more plausible
• Transition vs. Transversion
• Lys?Arg ? Lys?Cys
• Gap extension Vs. Gap opening

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DNA scoring matrices
General Alignment Methodology
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DNA scoring matrices
General Alignment Methodology

• Transitions purine to purine or pyrmidine to
  pyrmidine
• (4 possibilities)
• Transversions purine to pyrmidine or pyrmidine
  to purine (8 possibilities)
• By chance alone transversions should occur twice
  as often as transitions.
• De-facto transitions are more frequent than
  transversions.

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General Alignment Methodology
DNA scoring matrices
Transversion
Match
Transition
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General Alignment Methodology
Proteins scoring matrices
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General Alignment Methodology
Proteins scoring matrices

• Observation some substitutions are more frequent
  than others, e.g., chemically similar amino acids
  
• As for DNA, protein matrices define the
  probabilities of change between the different
  amino acids
• Popular matrices are based on empirical data
• PAM
• BLOSUM

T L Y D K T L Y D K T L Y E K T L Y D K T L Y Q
K T L Y D K
In the fourth column E and D are found in 7 / 8
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PAM Matrices
General Alignment Methodology

• PAM matrices are based on sequences with 85
  identity.
• The changes are accepted by natural selection
• 1 PAM unit
• the probability of 1 point mutation per 100
  residues.
• Multiplying PAM1 by itself gives higher PAMs
  matrices that are suitable for larger
  evolutionary distance.

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General Alignment Methodology
PAM250
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General Alignment Methodology
PAM Matrices
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General Alignment Methodology
BLOSUM Matrices

• Based on BLOCKS database
• 2000 blocks from 500 families of related
  proteins
• Blocks short conserved patterns of 3-60 aa
  without gaps
• Different BLOSUMn matrices are calculated
  independently from BLOCKS
• BLOSUMn is based on sequences that shared at
  least n percent identity

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General Alignment Methodology
BLOSUM Matrices

• Low BLUSOM numbers for distant sequences
• High BLUSOM numbers for similar sequence
• Generally
• BLOSUM62 for general use
• BLOSUM80 for close relations
• BLOSUM45 for distant relations

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General Alignment Methodology
Proteins scoring matrices
Closer sequences

• PAM100 BLOSUM90
• PAM120 BLOSUM80
• PAM160 BLOSUM60
• PAM200 BLOSUM52
• PAM250 BLOSUM45

Distant sequences
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General Alignment Methodology
Proteins scoring matrices

• BLOSUM matrices are based on the replacement
  patterns found in more highly conserved regions
  of the sequences without gaps
• PAM matrices based on mutations observed
  throughout a global alignment, includes both
  highly conserved and highly mutable regions

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Outline
1. Introduction
2. Applications
3. General Alignment Methodology

• 4. Pairwise Alignment
• Smith-Waterman
• Needlman-Wunch

• 5. Multiple Sequence Alignment
• ClustalW
• MUSCLE
• T-coffee

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Local vs. Global
Pairwise Alignment

• Global alignment finds the best alignment
  across the whole two sequences.

ADLGAVFALCDRYFQ ADLGRTQN-CDRYYQ

• Local alignment finds regions of similarity in
  parts of the
• sequences.

ADLG CDRYFQ ADLG CDRYYQ
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Pairwise Alignment
Global Needleman Wunsch (1970)

• Involves an iterative matrix method of
  calculation
• All possible pairs of residues are presented
• All possible alignments are presented as
  pathways through
• this array

Needleman, S. B. and Wunsch, C. D., 1970
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Pairwise Alignment
Local Smith Waterman (1981)

• Makes an optimal alignment of the best segment
  of similarity
• between two sequences
• Sequences that but contain regions that are
  highly similar
• Use when one sequence is short and the other is
  very long
• Can return a number of highly aligned segments

Smith, T.F. and Waterman, M.S., 1981
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Alignment in reality
Pairwise Alignment

• Main use of pairwise alignment finding sequence
  in a large database
• Database are huge ? heuristic procedures in
  search
• Take a lot of time
• Take a lot of memory
• Find local alignments
• In use in Blast, Fasta

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User Input
Pairwise Alignment

• Pair of sequences
• Local or global alignment
• Scoring
• Gap penalties opening/extension
• Scoring matrix

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Scoring
Pairwise Alignment

• The final score of the alignment is the sum of
  the positive scores and penalty scores
• Number of Identities
• Number of Similarities
• - Number of Gap insertions
• - Number of Gap extensions
• Alignment score

Scoring Matrix
Gap penalties
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Outline
1. Introduction
2. Applications
3. General Alignment Methodology

• 4. Pairwise Alignment
• Smith-Waterman
• Needlman-Wunch

• 5. Multiple Sequence Alignment
• ClustalW
• MUSCLE
• T-coffee

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Multiple Sequence Alignment
Pairwise Vs. Multiple Sequence Alignment
Alignments help to analyze sequence
data organize, visualize.
MSA For more than 2 sequences
Pairwise For 2 sequences

• F G K ? G K G
• F G K F G K G
• - G K Q G K G
• - - K F G K G

F G K ? G K G F G K F G K G
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Multiple Sequence Alignment
Rules For Choosing Sequences

• Very similar sequences have little information
• Very different sequences cause troublelt30
  identical with more than half of the other
  sequences in the set
• Choose sequences as distantly related as possible
• Sequence between 30-80 identical with more than
  half of the sequences in the set
• The more sequences the better

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Multiple Sequence Alignment
Similarity Score of MSA

• Each position gets a value, depending on its
  identity.
• The similarity score of the alignment is the sum
  of all
• position values.
• A popular way to compute position values
• SP - Sum of Pairs - each pair gets the score
  from the
• similarity matrix (PAM, BLOSUM).

Goal Find MSA with maximum similarity score Bad
News This problem is NP hard
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Multiple Sequence Alignment
ClustalW- Introduction

• This heuristic approach works because it uses
  the biological
• meaning of MSA
• Based on the idea that the sequences we usually
  want to align
• are phylogenetically related
• The first program to implement progressive MSA
• Was introduced in 1994 and still used today.

Thompson, J.D. et al, 1994
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Multiple Sequence Alignment
ClustalW- Progressive Alignment
Hbb_Human 1 Hbb_Horse 2 Hba_Human 3 Hba_Horse
4 Myg_Whale 5
1. Quick pairwise alignment calculate distance
matrix
Hbb_Human Hbb_Horse Hba_Human Hba_Horse Myg_Whale
2. Build a guide tree using the NJ phylogenetic
method
3. Progressive alignment following guide tree
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Multiple Sequence Alignment
ClustalW- Progressive Alignment
A
B
C
D
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Multiple Sequence Alignment
ClustalW- Additional Features

• Sequence weighting
• Each sequence gets a weight derived from the
  guide tree
• Close sequences are down-weighted
• Distant sequences receive high weights
• The weights are normalized so that the highest
  is 1

W(Hbb_Human) .081 ½.226 ¼.061 1/5.015
1/6.062 0.221
w1
w2
w3
w4
w5
w6
w7
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Multiple Sequence Alignment
ClustalW- Additional Features

• Sequence weighting
• Each sequence gets a weight derived from the
  guide tree
• Close sequences are down-weighted
• Distant sequences receive high weights
• The weights are normalized so that the highest
  is 1

Without weights Score M(t,v) M(t,i) M(i,v)
M(l,i) M(k,v) M(k,i) M(k,v) M(k,i) / 8
With weights Score M(t,v)W1W5 M(t,i)W1W6
M(i,v)W2W5 M(l,i)W2W6 M(k,v)W3W5 M(k,i)W3W6
M(k,v)W4W5 M(k,i)W4W6 / 8
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Multiple Sequence Alignment
ClustalW- Additional Features

• Chooses different scoring matrices, as the
  alignment proceeds, depending
• on the estimated divergence of the sequences
  at each stage.
• Position specific gap penalties depends on the
  position of in the alignment
• - Small penalty
• 1. In free loops (stretches of hydrophilic
  residues)
• 2. Existing gaps
• - Big penalty Near existing gaps
• Residue specific gap penalty

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Multiple Sequence Alignment
ClustalW- Problems

• Sequences that are similar only in some smaller
  regions
• ? ClustalW tries to find global alignments, not
  local.
• Sequence that contains a large insertion
  compared to the rest
• ? global not local
• Sequence that contains a repetitive element,
  while another sequence only
• contains one copy.

Vs
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Multiple Sequence Alignment
MUSCLE- Introduction

• The most recent popular MSA software
• Considered to be the most accurate MSA software
  available
• today
• The basic idea Progressive Alignment

Edgar, R.C., 2004
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Multiple Sequence Alignment
MUSCLE Innovations

• Faster distance estimation between the input
  sequences
• Faster construction of an evolutionary tree
• (UPGMA instead of NJ in ClustalW )

faster

• Applying new score function to the profile
  alignments
• Refinement of the initial results

more accurate
Edgar R.C., 2004
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Multiple Sequence Alignment
MUSCLE Innovations- Refinement Step

• An edge is chosen from the progressive alignment
  tree.
• The tree is divided into two subtrees by deleting
  this edge.
• The MSA from each subtree is computed by
  progressive alignment.
• The two MSAs are aligned, generating an entire
  new MSA
• If the new MSA achieves higher score than the
  previous ? keep it

New MSA ------------------------------------------
--------------------------------------------------
------------------
Old MSA ------------------------------------------
--------------------------------------------------
------------------
MSA2 ----------------------------
MSA1 ----------------------------------
--------
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Multiple Sequence Alignment
MUSCLE- Its Even More Complicated
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Multiple Sequence Alignment
T-Coffee- Introduction

• Heuristic methods based on progressive alignment
  use a phylogenetic guide tree to gradually built
  the alignment.
• ? Errors made in the first alignment cant be
  correct later on as more sequences are added
• T-Coffee will attempted to minimize the effect by
  using data from the all the sequences while
  building the alignment.
• T-coffee combine the best properties of global
  and local multiple alignments

Notredame et al., 2000
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Multiple Sequence Alignment
T-Coffee-Alignment Stages

• Create pairwise library
• Primary library- global pairwise (ClustalW)
• Extended library- 10 top scoring non intersecting
  local alignments
• Assign weights to each residue pair using the
  extended library
• Use weights to produce pairwise alignments
  instead of regular matrices
• Producing a distance matrix and tree using the NJ
  method
• MSA using the guide tree

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Multiple Sequence Alignment
T-Coffee- Applications

• Disadvantages
• Except for the distance matrix, there is no
  possibility to choose parameters (like open gap,
  gap extension).
• Advantages
• - Combination of local and global alignments
• - Data from the all the sequences while building
  the alignment
• Makes the results more accurate and is
  especially good for
• alignments with low identity and big insertions
  ? identification
• of motifs and domains

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Multiple Sequence Alignment
All Against All- SH2 domains
T-coffee
MUSCLE
Edgar, R.C., 2004
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Multiple Sequence Alignment
All Against All- BaliBase 2005
MUSCLE is superior!
Edgar, R.C., 2004
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Thanks
This presentation was partly based on
presentations of Dr. Meytal Landau, Dr. Metsada
Pasmanik-Chor and the Pupko lab