Pairwise Sequence Alignment is alignment.pptx

Sequence Alignment
Aligning sequences

Homology
Homology =
Similarity
between
objects due to
a common
ancestor
Hund = Dog,
Schwein = Pig

Sequence homology
VLSPAVKWAKVGAHAAGHG
||| || |||| | ||||
VLSEAVLWAKVEADVAGHG
Similarity between sequences as a
result of common ancestry.

Sequence alignment
Alignment: Comparing two (pairwise)
or more (multiple) sequences.
Searching for a series of identical or
similar characters in the sequences.

Why align?
VLSPAVKWAKV
||| || ||||
VLSEAVLWAKV
1.To detect if two sequences are homologous. If so,
homology may indicate similarity in structure (and
function).
2.Given a sequenced DNA, from an unknown region,
align it to the genome.
3.Required for evolutionary studies (e.g., tree
reconstruction).
4.To detect conservation (e.g., a tyrosine that is
evolutionary conserved is more likely to be a
phosphorylation site).

‫גנים‬ ‫של‬ ‫אבולוציה‬
‫אורתולוגים‬ ‫גנים‬
‫שמצויים‬ ‫גנים‬ -
‫במינים‬
(
species
)
‫שונים‬
,
.‫משותף‬ ‫קדמון‬ ‫אב‬ ‫ולהם‬
‫פונקציה‬ ‫להם‬ ‫ויש‬ ‫הגן‬ ‫ברצף‬ ‫דומים‬ ‫אורתולוגים‬ ,‫כלל‬ ‫בדרך‬
.‫המינים‬ ‫בשני‬ ‫דומה‬

‫אורתולגים‬ ‫גנים‬
( ‫במינים‬ ‫שמצויים‬ ‫גנים‬ -
species
.‫משותף‬ ‫קדמון‬ ‫אב‬ ‫ולהם‬ ,‫שונים‬ )
.‫המינים‬ ‫בשני‬ ‫דומה‬ ‫פונקציה‬ ‫להם‬ ‫ויש‬ ‫הגן‬ ‫ברצף‬ ‫דומים‬ ‫אורתולוגים‬ ,‫כלל‬ ‫בדרך‬
( ‫אדם‬
Human
)
( ‫מקוק‬ ‫קוף‬
Rhesus macaque
)
‫משותף‬ ‫קדמון‬ ‫אב‬
(
Common ancestor
)
Hemoglobin
Hemoglobin
Hemoglobin

?‫אורתולגים‬ ‫מוצאים‬ ‫איך‬
:‫שונים‬ ‫אורגנזימים‬ ‫בין‬ ‫דומים‬ ‫גנים‬ ‫רצפי‬ ‫של‬ ‫חיפוש‬
‫האדם‬ ‫גנום‬ ‫המקוק‬ ‫גנום‬
Gene 1
Gene 1
Gene 3
Gene 4
… …
Gene A
Gene B
Gene C
Gene D

Gene 1
Gene 1
Gene 3
Gene 4
… …
‫רצפים‬ ‫של‬ ‫חיפוש‬
:‫דומים‬
‫אדם‬ >-‫מקוק‬
‫מקוק‬ >-‫אדם‬
Gene A
Gene B
Gene C
Gene D

Gene 1
Gene 1
Gene 3
Gene 4
Gene A
Gene B
Gene C
Gene D
… …
‫רצפים‬ ‫של‬ ‫חיפוש‬
:‫דומים‬
‫אדם‬ >-‫מקוק‬
‫מקוק‬ >-‫אדם‬
BLAST similarity
search
Human->
Macaque
Macaque ->
Human

Insertions, deletions, and
substitutions

Sequence alignment
If two sequences share a common ancestor,
we can represent their evolutionary
relationship using a tree
Example: human and dog hemoglobin
VLSPAV-WAKV
||| || ||||
VLSEAVLWAKV
VLSPAV-WAKV VLSEAVLWAKV

Perfect match
VLSPAV-WAKV
||| || ||||
VLSEAVLWAKV
A perfect match suggests that no change has
occurred from the common ancestor (although
this is not always the case).

A substitution
VLSPAV-WAKV
||| || ||||
VLSEAVLWAKV
A substitution suggests that at least one
change has occurred since the common
ancestor
We cannot say in which lineage it occurred!

Indel
VLSPAV-WAKV
||| || ||||
VLSEAVLWAKV
VLSPAV-WAKV
VLSEAVLWAKV
Option 1: The ancestor had L and it was lost
here. In such a case, the event was a deletion.
VLSEAVLWAKV

Indel
VLSPAV-WAKV
||| || ||||
VLSEAVLWAKV
VLSPAV-WAKV
VLSEAVWAKV
Option 2: The ancestor was shorter and the L
was inserted here. In such a case, the event
was an insertion.
VLSEAVLWAKV
L

Indel
VLSPAV-WAKV
Normally, given two sequences we cannot tell
whether it was an insertion or a deletion, so
we term the event as an indel.
VLSEAVLWAKV
Deletion? Insertion?

Indels in protein coding genes
Indels in protein coding genes are often of 3bp,
6bp, 9bp, etc...
Why?
Gene Search
In fact, searching for indels of length 3K
(K=1,2,3,…) can help algorithms that search a
genome for coding regions

Global and Local pairwise
alignments

Global vs. Local
• Global alignment – finds the best
alignment across the entire two
sequences.
• Local alignment – finds regions of
similarity in parts of the sequences.
ADLGAVFALCDRYFQ
|||| |||| |
ADLGRTQN-CDRYYQ
ADLG CDRYFQ
|||| |||| |
ADLG CDRYYQ
Global
alignment:
forces
alignment in
regions which
differ
Local
alignment will
return only
regions of
good
alignment

Global alignment
FAK1 (a.k.a. PTK2, protein tyrosine kinase 2) of human and mouse
…

Proteins are comprised of domains
Domain B
Protein tyrosine
kinase domain
Domain A
Human PTK2 :
Global alignment may be problematic!

Domain X
Protein tyrosine kinase
domain
Domain B
Protein tyrosine kinase
domain
Domain A
Leukocyte TK
PTK2 The sequence similarity is
restricted to a single domain

Global alignment of PTK and LTK
(partial)

Local alignment of PTK and LTK

Conclusions
Use global alignment when the two sequences
share the same overall sequence arrangement.
Use local alignment to detect regions of
similarity.

Pairwise alignment
AAGCTGAATTCGAA
AGGCTCATTTCTGA
AAGCTGAATT-C-GAA
AGGCT-CATTTCTGA
-
One possible alignment:

AAGCTGAATT-C-GAA
AGGCT-CATTTCTGA
-
This alignment includes:
2 mismatches
4 indels (gap)
10 perfect matches

Choosing an alignment
for a pair of sequences
AAGCTGAATTCGAA
AGGCTCATTTCTGA
AAGCTGAATT-C-GAA
AGGCT-CATTTCTGA
-
A-AGCTGAATTC--GAA
AG-GCTCA-TTTCTGA-
Which alignment is better?
Many different alignments are
possible for 2 sequences:

Scoring system (naïve)
AAGCTGAATT-C-GAA
AGGCT-CATTTCTGA
-
Score: = (+1)x10 + (-2)x2 + (-1)x4 = 2 Score: = (+1)x9 + (-2)x2 + (-1)x6 = -1
A-AGCTGAATTC--GAA
AG-GCTCA-TTTCTGA-
Higher score  Better alignment
Perfect match: +1
Mismatch: -2
Indel (gap): -1

Alignment scoring - scoring of
sequence similarity:
Assumes independence between positions:
each position is considered separately
Scores each position:
• Positive if identical (match)
• Negative if different (mismatch or gap)
Total score = sum of position scores
Can be positive or negative

Scoring system
• In the example above, the choice of +1 for
match,-2 for mismatch, and -1 for gap is
quite arbitrary
• Different scoring systems  different
alignments
• We want a good scoring system…

Scoring matrix
T C G A
2 A
2 -6 G
2 -6 -6 C
2 -6 -6 -6 T
• Representing the scoring
system as a table or
matrix n X n (n is the
number of letters the
alphabet contains. n=4
for nucleotides, n=20 for
amino acids)
• symmetric

DNA scoring matrices
• Uniform substitutions between all nucleotides:
T C G A From
To
-6 -6 -6 2 A
-6 -6 2 -6 G
-6 2 -6 -6 C
2 -6 -6 -6 T
Match
Mismatch

Can take into account biological phenomena
such as:
• Transition-transversion

• Non-uniform substitutions between all nucleotides:
T C G A From
To
-6 -6 -2 2 A
-6 -6 2 -2 G
-2 2 -6 -6 C
2 -2 -6 -6 T
Match
Mismatch

Scoring gaps (I)
In advanced algorithms, two gaps of one amino-
acid are given a different score than one gap of
two amino acids. This is solved by giving a penalty
to each gap that is opened.
Gap extension penalty < Gap opening penalty

Scoring gaps (II)
Assume that the gap opening cost is -3. The
length contributes -1 per base pair
AGGGTTC—GA
AGGGTTCTGA
Score = -4
AGGGTT-—GA
AGGGTTCTGA
Score = -5
AGGGT--—GA
AGGGTTCTGA
Score = -6
AGGG--C—GA
AGGGTTCTGA
Score = -9
Linear penalty

Intermediate summary
1. Scoring system =
substitution matrix + gap penalty.
2. Used for both global and local alignment

Many possible alignments
AAGCTGAATTCGAA
AGGCTCATTTCTGA
AAGCT-GAATT-C-GAA
A-GGCT-CATTTCTGA
-
AAGCTGAATT-C-GAA
AGGCT-CATTTCTGA
-
AAG-CTGAATT-C-GAA
AGGCT-CATTT-CTGA
-
Which alignment has the best
score?
• Two sequences of length 10 have
>> 1,000,000 possible alignments
>> 100,000,000,000,000 possible
alignments
>>
10,000,000,000,000,000,000,000
possible alignments

Optimal alignment algorithms
• Needleman-Wunsch (global) [1970]
• Smith-Waterman (local) [1981]
Only length(seq1) x length(seq2) operations!
Length of seq1,seq2 #
operations Instead of
10 100 1,000,000
20 400 100,000,000,000,000
30 900 10,000,000,000,000,000,000,
000

Matrix Representation
Match = 1
Mismatch = -1
Indel = -2
A G C
0 -2 -4 -6
A -2 1 -1 -3
A -4 -1 0 -2
A -6 -3 -2 -1
C -8 -5 -4 -1
score(AAAC,AGC) = -1
Seq2
Seq1

score(AAA,AG) = -2
A G C
0 -2 -4 -6
A -2 1 -1 -3
A -4 -1 0 -2
A -6 -3 -2 -1
C -8 -5 -4 -1
Match = 1
Mismatch = -1
Indel = -2
Seq2
Seq1

score(,AG) = -4
A G C
0 -2 -4 -6
A -2 1 -1 -3
A -4 -1 0 -2
A -6 -3 -2 -1
C -8 -5 -4 -1
Match = 1
Mismatch = -1
Indel = -2
Seq2
Seq1

The best alignment path
A G – C
A A A C
Match = 1
Mismatch = -1
Indel = -2
Seq2
Seq1
A G C
0 -2 -4 -6
A -2 1 -1 -3
A -4 -1 0 -2
A -6 -3 -2 -1
C -8 -5 -4 -1

Mi,j = MAXIMUM[
Mi-1, j-1 + Si,j (match/mismatch in the diagonal),
Mi,j-1 + w (gap in sequence #1),
Mi-1,j + w (gap in sequence #2)]
Computing the score of each cell
Mi,j = the score in cell (i,j)
Mi-1,j-1 Mi-1,j
Mi,j-1 Mi,j

Computing the Matrix
A G C
0 -2 -4 -6
A -2
A -4
A -6
C -8
Match = 1
Mismatch = -1
Indel = -2
This cell reflects putting A in the sequence in the
row against a gap in the sequence of the column:
Col -
Row A
Mi-1,j-1 Mi-1,j
Mi,j-1 Mi,j

A G C
0 -2 -4 -6
A -2
A -4
A -6
C -8
Match = 1
Mismatch = -1
Indel = -2
This cell reflects putting AAAC in the sequence in
the column against 4 gaps in the sequence of the
row: Col AAAC
Row ----
Mi-1,j-1 Mi-1,j
Mi,j-1 Mi,j

A G C
0 -2 -4 -6
A -2 ?
A -4
A -6
C -8
Match = 1
Mismatch = -1
Indel = -2
This arrow reflects putting A against A and
coming from putting nothing against nothing:
Col A
Row A
Col
Row
score =0+1=1
Mi-1,j-1 Mi-1,j
Mi,j-1 Mi,j

A G C
0 -2 -4 -6
A -2 ?
A -4
A -6
C -8
Match = 1
Mismatch = -1
Indel = -2
This arrow reflects putting A from the column
sequence against an indel:
Mi-1,j-1 Mi-1,j
Mi,j-1 Mi,j
Col -A
Row A-
Col -
Row A
score =-2-2=-4

A G C
0 -2 -4 -6
A -2 ?
A -4
A -6
C -8
Match = 1
Mismatch = -1
Indel = -2
This arrow reflects putting A from the row
sequence against an indel:
Mi-1,j-1 Mi-1,j
Mi,j-1 Mi,j
Col A-
Row -A
Col A
Row -
score =-2-2=-4

A G C
0 -2 -4 -6
A -2 1
A -4
A -6
C -8
Match = 1
Mismatch = -1
Indel = -2
We have three possible paths to the A against A,
and we chose the path that has the best score

A G C
0 -2 -4 -6
A -2 1
A -4
A -6
C -8
Match = 1
Mismatch = -1
Indel = -2
This arrow reflects putting A from the row
sequence against indel:
Col AA –
Row – –A
Col AA
Row – –
score=
-4-2=-6
Mi-1,j-1 Mi-1,j
Mi,j-1 Mi,j

A G C
0 -2 -4 -6
A -2 1
A -4
A -6
C -8
Match = 1
Mismatch = -1
Indel = -2
sequence:
Col AA
Row A–
Col A
Row A
score=
1-2=-1
Mi-1,j-1 Mi-1,j
Mi,j-1 Mi,j

A G C
0 -2 -4 -6
A -2 1
A -4
A -6
C -8
Match = 1
Mismatch = -1
Indel = -2
sequence and A from the row sequence:
Col AA
Row –A
Col A
Row –
score=
-2+1=-1
Mi-1,j-1 Mi-1,j
Mi,j-1 Mi,j

A G C
0 -2 -4 -6
A -2 1
A -4 -1
A -6
C -8
Match = 1
Mismatch = -1
Indel = -2
We chose one of the best arrows. The best
alignment for this cell is
Col AA
Row A–

Computing the entire Matrix
A G C
0 -2 -4 -6
A -2 1 -1 -3
A -4 -1 0 -2
A -6 -3 -2 -1
C -8 -5 -4 -1
Match = 1
Mismatch = -1
Indel = -2

A G C
0 -2 -4 -6
A -2 1 -1 -3
A -4 -1 0 -2
A -6 -3 -2 -1
C -8 -5 -4 -1
‫המלאה‬ ‫המטריצה‬ ‫מתוך‬ ‫העימוד‬ ‫שחזור‬
AG-C
AAAC
We chose a path that goes from the end to
the beginning (marked here in Red). This is
called the Trace Back

A G C
0 -2 -4 -6
A -2 1 -1 -3
A -4 -1 0 -2
A -6 -3 -2 -1
C -8 -5 -4 -1
A-GC
AAAC

A G C
0 -2 -4 -6
A -2 1 -1 -3
A -4 -1 0 -2
A -6 -3 -2 -1
C -8 -5 -4 -1
-AGC
AAAC

Local alignment (note, letters were changed)
G A C
0 0 0 0
A 0
G 0
A 0
T 0
Match = 1
Mismatch = -1
Indel = -2
We initialize everything with a 0. Across the
matrix, there are no negative values (every time
we need to put a negative value, we put a 0).

Local alignment
G A C
0 0 0 0
A 0 0
G 0
A 0
T 0
Match = 1
Mismatch = -1
Indel = -2

Local alignment
G A C
0 0 0 0
A 0 0 1 0
G 0 1 0 0
A 0 0
T 0 0
Match = 1
Mismatch = -1
Indel = -2

Local alignment
G A C
0 0 0 0
A 0 0 1 0
G 0 1 0 0
A 0 0 2 0
T 0 0 0 1
Match = 1
Mismatch = -1
Indel = -2

Local alignment
G A C
0 0 0 0
A 0 0 1 0
G 0 1 0 0
A 0 0 2 0
T 0 0 0 1
Match = 1
Mismatch = -1
Indel = -2
In trace-back, we start with the highest value and
go back to a zero (anywhere).
Col GA
Row GA

Global and Local pairwise alignments -
summary
• Global alignment – finds the best
alignment over all the positions
• Local alignment – finds regions of
similarity in parts of the sequences

Global & Local Alignments
Global Most useful when the sequences are similar in the
entire alignment.
Local More useful for dissimilar sequences that
are suspected to contain regions of similarity
or similar sequence motifs.

Pairwise alignment
• Local alignment – Waterman algorithm
• Global alignment – Needleman algorithm
• Protein and nucleotide alignments
• Input: two sequences
• Output: optimal sequence alignment

Question #1:
For highly similar sequences, which method
is more compatible for alignment?
Global
Local

Global Alignment Webserver
(Needleman-Wunsch algorithm)
• https://www.ebi.ac.uk/jdispatcher/psa/emb
oss_needle

Local Alignment Webserver (Smith-
Waterman algorithm)
• https://www.ebi.ac.uk/jdispatcher/psa/emb
oss_water

Altering parameters
• Scoring matrix, e.g. BLOSUM:
– Higher numbers in matrix for similar
sequences
• Gap open & extend penalty
– Higher gap open penalty for similar
sequences
– If gap length matters increase gap extend
penalty
• Output format (pair)

Protein scoring matrix
The blosum 62 matrix.
The cost of substituting one amino acid by another.

BLOcks SUbstitution Matrix
• Based on comparisons of blocks of sequences from the
Blocks database.
• BLOSUM62 was built using data from sequences that
were up to 62% identical.

Results page
• Submission details
• Parameters
• Scores
– Identity
– Similarity
– Gaps
– Total score

Results page
• The alignment
• Notice
– Positions
– Matches |
– Similarities :
– Mismatches .
– Indels -

Question #3:
In case sequences are of unequal lengths – in which of the
sequences there will be more gaps in global alignment?
1
2

Answer #3:
Sequence no. 1
In local alignment:

Question #4:
For closely related sequences,
which BLOSUM matrix is better – BLOSUM62 or
BLOSUM30?

• Sequence similarity
– Alignment - scoring system, local vs. global
• Pairwise alignment tools
– Needle for global
– Water for local
Summary

Pairwise Sequence Alignment is alignment.pptx

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