Sequence alignment involves arranging DNA, RNA, or protein sequences to identify regions of similarity. It is used to determine if sequences are evolutionarily related, observe patterns of conservation, and find similar regions within proteins. The key steps are representation of sequences in a matrix, insertion of gaps, and use of scoring schemes like PAM and BLOSUM matrices to identify the best alignment. Global alignment forces alignment over full sequence lengths while local alignment identifies short, well-matching segments. Algorithms like Needleman-Wunsch and Smith-Waterman use dynamic programming to calculate optimal pairwise sequence alignments.

1. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Sequence alignment

2. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
▪ Sequence alignment is a way of arranging the sequences
of DNA, RNA, or protein to identify regions of similarity
▪ Aligned sequences of nucleotide or amino acid residues are
typically represented as rows within a matrix.
▪ Gaps are inserted between the residues so that identical or similar
characters are aligned in successive columns.
1. To find whether two (or more) genes or proteins are evolutionarily
related to each other
2. To observe patterns of conservation (or variability).
3. To find structurally or functionally similar regions within proteins i.e
to find the common motifs present in both sequences.
4. To find out which sequences from the database are similar to the
sequence at hand
Purpose of sequence alignment

3. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
1. They are often used interchangeably, they have quite different
meanings.
2. Sequence identity refers to the occurrence of exactly the same
nucleotide or amino acid in the same position in aligned sequences.
3. The term ‘sequence homology’ is the most important (and the most
abused) of the three.
• When we say that sequence A has high homology to sequence B,
then we are making two distinct claims:
• not only are we saying that sequences A and B look much the
same, but also that all of their ancestors also looked the same,
going all the way back to a common ancestor.
Identity vs Similarity vs Homology

4. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Sequence Identity Sequence similarity
Sequence
homology
Definition
Proportion of
identical residues
between two
sequences.
Proportion of similar
residues between two
sequences. Two residues are
similar if their substitution
cost is higher than 0.
Sequences
derived from a
common
ancestor
Expressed as % identity % Similarity Yes or No
Rule-of-thumb: If two sequences are more than 100 amino acids long
(or 100 nucleotides long) they are considered homologues if 25% of
the amino acids are identical (70% of nucleotide for DNA).
Twilight zone = protein sequence similarity between ~ 0-20%

5. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Global alignment
• assumes that the two sequences are basically similar over the entire
length of one another.
• forces to match the sequences from end to end, even though parts of
the alignment are not very convincingly matching.
• most suitable when the two sequences are of similar length and are
with a significant degree of similarity throughout.
.
Computational approaches
• Global alignment
• Local alignment

6. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Local alignment
• Identifies segments of the two sequences that match well with no
attempt to force the entire sequences into alignment
• Parts that appear to have good similarity, according to some
criterion are aligned.
• Suitable when comparing substantially different sequences, which
possibly differ significantly in length, and have only short patches
of similarity

7. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
• Given two sequences there can be umpteen number of ways by which
both the sequences can be aligned
▪ Gaps are inserted between the residues to get the alignment
AT G G C G T
A T G - A G T
AT G G C G T
A -T G A G T
Aligning two sequences
• Scoring Scheme is needed to get the best possible alignment by
scoring the alignment
• Give two sequences we need a number to associate with each
possible alignment (i.e. the alignment score = goodness of alignment).

8. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
The scoring scheme is a set of rules which assigns the alignment
score to any given alignment of two sequences.
• The scoring scheme is residue based: it consists of residue substitution
scores (i.e. score for each possible residue alignment), plus penalties for
gaps.
• The alignment score is the sum of substitution scores and gap penalties.
AT G G C G T
A T G - A G T
AT G G C G T
A -T G A G T
For eg :- Gap:- -2; Match:- +1; Mismatch: -1
+1+1+1-2-1+1+1=+2 +1-2-1+1-1+1+1=0
Scoring scheme

9. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Substitution scores are given by :
For DNA : Substitution Matrix for DNA (Purine/Purine or purine/
pyramidine substitutions)
For proteins : Substitution matrix based on Polarity, Size, Charge or
Hydrophobicity
Evolutionary distance matrices :- PAM and BLOSUM for
protein sequences
Scoring schemes

10. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Point Accepted Mutation(PAM) Blocks substitution matrix (BLOSUM)
1.
Derived from global alignments
of closely related sequences.
Derived from local, ungapped alignments
of distantly related sequences
2.
Matrices for greater evolutionary
distances are extrapolated from those for
lesser ones.
All matrices are directly calculated; no
extrapolations are used
3
The number with the matrix (PAM40,
PAM100) refers to the evolutionary
distance; greater the number greater
the distance.
The number after the matrix
(BLOSUM62) refers to the minimum
percent identity of the blocks used to
construct the matrix; greater numbers
lesser distance.
Note : The BLOSUM series of matrices generally perform better than PAM matrices
for local similarity searches i.e. for more divergent sequences, the BLOSUM matrices
are often better, whereas the PAM matrix is suited for highly similar sequences.

11. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Types of alignment
Pairwise alignment Multiple Sequence alignment
Can be Global or Local

12. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Methods of pairwise alignment
• Dot Matrix method
• The dynamic programming method
• Needleman and Wunch
• Smith and Watermann
• Heuristic methods
• FASTA
• BLAST

13. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
• It is a visual graphical representation of similarities between two
sequences.
• Each axis represents one of the two sequences to be compared.
• In the dot matrix method when two sequences are similar over
their entire length a line will extend from one corner of the dot
plot to the diagonally opposite corner.
• If two sequences share only patches of similarity then it will be
revealed by diagonal stretches.
Dot Matrix method

14. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Interpretation of Dot Matrix
• Regions of similarity appear as diagonal runs of dots.
• Reverse diagonals (perpendicular to diagonal) indicate inversions.
• Reverse crossing diagonals (Xs) indicate palindromes.
Limitation:-
• The dot matrix computer programs do not show an actual alignment.

15. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
The dynamic programming
• Dynamic programming reduces the massive number of possibilities that
need to be considered in aligning sequences.
• This method was first used for global alignment of sequences by
Needleman-Wunch algorithm (1970) and for local alignment by Smith -
Waterman algorithm (1981).
• Both the algorithms involve initialization, matrix filling (scoring) and
trace back steps. The algorithms use either PAM or BLOSUM matrices
in the scoring step to fill the score matrix.

16. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Global alignment

17. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
The three main steps in this algorithm are :
1. Initialization
2. Matrix filling
3. Traceback for alignment
Initialization
1. Place the two sequences one across the row and other down the
column
2. The first column and first row should be a gap
3. Add the cumulative gap cost across the row and other down the
column to fill the first column and first row
Global alignment (Needleman and Wunch)

18. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
• Matrix filling
• Rules :-
• Check the side, top and diagonal values of the box
• Box Beside - (add gap cost)
• Box top - ( add gap cost)
• Diagonal box - (match/mismatch)
• Put the highest value in the respective boxes
• Proceed to the end of the scoring matrix
• Trace back
• Start from the end of the matrix and reach the start by tracing
back the value obtained in the box
• if diagonal - Place the characters
• if vertical or horizontal - place a gap in the sequence being
pointed by the arrow

19. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
gap T G A
gap 0 -2 -4 -6
A -2
-1 -4
-1
-4
-3 -6
-3
-3
-3 -8
-3
-5
T -4
-1 -3
-1
-6
-2 -5
-2
-3
-4 -5
-4
-4
G -6
-5 -3
-3
-8
0 -4
0
-5
-3 -6
-2
-2
C -8
-7 -5
-5
-10
-4 -2
-2
-7
-1 -4
-1
-4
Matrix Filling - Gap -2; Mismatch -1; Match +1 Box Beside : +gap; Box Top : +gap; Diagonal box : match or mismatch

20. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
gap T G A
gap 0 -2 -4 -6
A -2
-1 -4
-1
-4
-3 -6
-3
-3
-3 -8
-3
-5
T -4
-1 -3
-1
-6
-2 -5
-2
-3
-4 -5
-4
-4
G -6
-5 -3
-3
-8
0 -4
0
-5
-3 -3
-2
-2
C -8
-7 -5
-5
-10
-4 -2
-2
-7
-1 -4
-1
-3
Trace back

21. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
ATGC
- TGA
-2+1+1-1
= -1

22. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
gap G A C T A C
gap 0 -1 -2 -3 -4 -5 -6
A -1
0 -2
0
-2
0 -3
0
-1
-2 -4
-1
-1
-3 -5
-2
-2
-3 -6
-3
-3
-5 -7
-4
-4
C -2
-1 -1
-1
-3
0 -1
0
-2
+1 -2
+1
-1
-1 -3
0
0
-2 -4
-1
-1
-3 -5
-2
-2
G -3
-1 -2
-1
-4
-1 -1
-1
-2
0 0
0
-2
+1 -1
+1
-1
0 -2
0
0
-1 -3
-1
-1
C -4
-3 -2
-2
-5
-1 -2
-1
-3
0 -1
0
-2
0 0
0
-1
+1 -1
+1
-1
+1 -2
+1
0
Matrix Filling - Gap -1; Mismatch - 0; Match +1

23. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Matrix Filling - Gap -1; Mismatch - 0; Match +1
gap G A C T A C
gap 0 -1 -2 -3 -4 -5 -6
A -1
0
-2
0
-2
0
-3
0
-1
-2
-4
-1
-1
-3
-5
-2
-2
-3
-6
-3
-3
-5
-7
-4
-4
C -2
-1
-1
-1
-3
0
-1
0
-2
+1
-2
+1
-1
-1
-3
0
0
-2
-4
-1
-1
-3
-5
-2
-2
G -3
-1
-2
-1
-4
-1
-1
-1
-2
0
0
0
-2
+1
-1
+1
-1
0
-2
0
0
-1 -3
-1
-1
C -4
-3
-2
-2
-5
-1
-2
-1
-3
0
-1
0
-2
0
0
0
-1
+1
-1
+1
-1
+1
-2
+1
0

24. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
ACG-C
GACTAC
-1+1+1+0-1+1
= +1
AC-GC
GACTAC
-1+1+1-1+0+1
= +1

25. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
gap T G A
gap 0 -2 -4 -6
A -2
T -4
G -6
C -8
Matrix Filling - Gap -2; Mismatch -1; Match +1 Box Beside : +gap; Box Bottom : +gap; Diagonal box : match or mismatch

26. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
gap G A C T A C
gap 0
A -2
C -4
G -6
C -8
Matrix Filling - Gap -2; Mismatch - -1; Match +1

27. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
gap G A C T A C
gap
A
C
G
C
Matrix Filling - Gap -1; Mismatch - 0; Match +1

28. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
gap T G A
gap
A
T
G
C
Matrix Filling - Gap -2; Mismatch -1; Match +1 Box Beside : +gap; Box Bottom : +gap; Diagonal box : match or mismatch

29. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
The three main steps in this algorithm are :
1. Initialization
2. Matrix filling
3. Traceback for alignment
Initialization
1. Place the two sequences one across the row and other down the
column
2. The first column and first row should be a gap
3. Place zeros in first column and first row
Matrix filling
1. The value of each box thereon depends on the top, diagonal and
side boxes (Box Beside - (add gap cost); Box top - ( add gap
cost); Diagonal box - (match/mismatch)
2. If the value is negative - put the value as zero
3. The highest of the three values is placed in the box
4. The same is continued till the end of the matrix
Smith and Waterman algorithm (Local alignment)

30. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
gap T G A
gap 0 0 0 0
A 0
0 0
0
0
0 0
0
0
+1 0
+1
0
T 0
+1 0
+1
0
0 0
0
0
0 0
0
0
G 0
0 0
0
0
+2 0
+2
0
0 0
0
0
C 0
0 0
0
0
0 0
0
0
+1 0
+1
0
Matrix Filling - Gap -2; Mismatch -1; Match +1

31. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
TG
TG
+1+1=+2

32. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
gap G C C T A C C C G A A T
gap 0 0 0 0 0 0 0 0 0 0 0 0 0
G 0
+1 0
+1
0
0 0
0
0
0 0
0
0
0 0
0
0
0 0
0
0
0 0
0
0
0 0
0
0
0 0
0
0
+1 0
+1
0
0 0
0
0
0 0
0
0
0 0
0
0
A 0
0 0
0
0
0 0
0
0
0 0
0
0
0 0
0
0
+1 0
+1
0
0 0
0
0
0 0
0
0
0 0
0
0
0 0
0
0
+2 0
+2
0
+1 0
+1
0
0 0
0
0
A 0
0 0
0
0
0 0
0
0
0 0
0
0
0 0
0
0
+1 0
+1
0
0 0
0
0
0 0
0
0
0 0
0
0
0 0
0
0
+1 0
+1
0
+3 0
+3
0
0 0
+1
+1
T 0
0 0
0
0
0 0
0
0
0 0
0
0
+1 0
+1
0
0 0
0
0
0 0
0
0
0 0
0
0
0 0
0
0
0 0
0
0
0 0
0
0
0 +1
+1
0
+4 0
+4
0
Matrix Filling - Gap -2; Mismatch -1; Match +1

33. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
GAAT
GAAT
1+1+1+1=+4

34. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Global alignment - example

35. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
gap A T G G C G T
gap 0 -2 -4 -6 -8 -10 -12 -14
A -2
+1 -4
+1
-4
-3 -6
-1
-1
-5 -8
-3
-3
-7 -10
-5
-5
-9 -12
-7
-7
-11 -14
-9
-9
-13 -16
-11
-11
T -4
-3 -1
-1
-6
+2 -3
+2
-3
-2 -5
0
0
-4 -7
-2
-2
-6 -9
-4
-4
-8 -11
-6
-6
-8 -13
-8
-8
G -6
-5 -3
-3
-8
-2 0
0
-5
+3 -2
+3
-2
+1 -4
+1
+1
-3 -6
-1
-1
-3 -8
-3
-3
-7 -10
-5
-5
A -8
-5 -5
-5
-10
-4 -2
-2
-7
-1 +1
+1
-4
+2 -1
+2
-1
0 -3
0
0
-2 -5
-2
-2
-4 -7
-4
-4
G -10
-9 -7
-7
-12
-6 -4
-4
-9
-1 -1
-1
-6
+2 0
+2
-3
+1 -2
+1
0
+1 -4
+1
-1
-3 -6
-1
-1
T -12
-11 -9
-9
-14
-6 -6
-6
-11
-5 -3
-3
-8
-2 0
0
-5
+1 -1
+1
-2
0 -1
0
-1
+2 -3
+2
-2
Matrix Filling - Gap -2; Mismatch -1; Match +1

36. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
gap A T G G C G T
gap 0 -2 -4 -6 -8 -10 -12 -14
A -2
+1 -4
+1
-4
-3 -6
-1
-1
-5 -8
-3
-3
-7 -10
-5
-5
-9 -12
-7
-7
-11 -14
-9
-9
-13 -16
-11
-11
T -4
-3 -1
-1
-6
+2 -3
+2
-3
-2 -5
0
0
-4 -7
-2
-2
-6 -9
-4
-4
-8 -11
-6
-6
-8 -13
-8
-8
G -6
-5 -3
-3
-8
-2 0
0
-5
+3 -2
+3
-2
+1 -4
+1
+1
-3 -6
-1
-1
-3 -8
-3
-3
-7 -10
-5
-5
A -8
-5 -5
-5
-10
-4 -2
-2
-7
-1 +1
+1
-4
+2 -1
+2
-1
0 -3
0
0
-2 -5
-2
-2
-4 -7
-4
-4
G -10
-9 -7
-7
-12
-6 -4
-4
-9
-1 -1
-1
-6
+2 0
+2
-3
+1 -2
+1
0
+1 -4
+1
-1
-3 -6
-1
-1
T -12
-11 -9
-9
-14
-6 -6
-6
-11
-5 -3
-3
-8
-2 0
0
-5
+1 -1
+1
-2
0 -1
0
-1
+2 -3
+2
-2
Matrix Filling - Gap -2; Mismatch -1; Match +1

37. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
gap A T G G C G T
gap 0 -2 -4 -6 -8 -10 -12 -14
A -2
+1 -4
+1
-4
-3 -6
-1
-1
-5 -8
-3
-3
-7 -10
-5
-5
-9 -12
-7
-7
-11 -14
-9
-9
-13 -16
-11
-11
T -4
-3 -1
-1
-6
+2 -3
+2
-3
-2 -5
0
0
-4 -7
-2
-2
-6 -9
-4
-4
-8 -11
-6
-6
-8 -13
-8
-8
G -6
-5 -3
-3
-8
-2 0
0
-5
+3 -2
+3
-2
+1 -4
+1
+1
-3 -6
-1
-1
-3 -8
-3
-3
-7 -10
-5
-5
A -8
-5 -5
-5
-10
-4 -2
-2
-7
-1 +1
+1
-4
+2 -1
+2
-1
0 -3
0
0
-2 -5
-2
-2
-4 -7
-4
-4
G -10
-9 -7
-7
-12
-6 -4
-4
-9
-1 -1
-1
-6
+2 0
+2
-3
+1 -2
+1
0
+1 -4
+1
-1
-3 -6
-1
-1
T -12
-11 -9
-9
-14
-6 -6
-6
-11
-5 -3
-3
-8
-2 0
0
-5
+1 -1
+1
-2
0 -1
0
-1
+2 -3
+2
-2
Matrix Filling - Gap -2; Mismatch -1; Match +1

38. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
gap A T G G C G T
gap 0 -2 -4 -6 -8 -10 -12 -14
A -2
+1 -4
+1
-4
-3 -6
-1
-1
-5 -8
-3
-3
-7 -10
-5
-5
-9 -12
-7
-7
-11 -14
-9
-9
-13 -16
-11
-11
T -4
-3 -1
-1
-6
+2 -3
+2
-3
-2 -5
0
0
-4 -7
-2
-2
-6 -9
-4
-4
-8 -11
-6
-6
-8 -13
-8
-8
G -6
-5 -3
-3
-8
-2 0
0
-5
+3 -2
+3
-2
+1 -4
+1
+1
-3 -6
-1
-1
-3 -8
-3
-3
-7 -10
-5
-5
A -8
-5 -5
-5
-10
-4 -2
-2
-7
-1 +1
+1
-4
+2 -1
+2
-1
0 -3
0
0
-2 -5
-2
-2
-4 -7
-4
-4
G -10
-9 -7
-7
-12
-6 -4
-4
-9
-1 -1
-1
-6
+2 0
+2
-3
+1 -2
+1
0
+1 -4
+1
-1
-3 -6
-1
-1
T -12
-11 -9
-9
-14
-6 -6
-6
-11
-5 -3
-3
-8
-2 0
0
-5
+1 -1
+1
-2
0 -1
0
-1
+2 -3
+2
-2
Matrix Filling - Gap -2; Mismatch -1; Match +1

39. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
ATGGCGT
AT-GAGT
1+1-2+1-1+1+1=+2

40. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
ATGGCGT
ATGA-GT
1+1+1-1-2+1+1=+2

41. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
ATGGCGT
ATG-AGT
1+1+1-2-1+1+1=+2

42. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
ATGGCGT
ATG-AGT
1+1+1-2-1+1+1=+2

43. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
ATGGCGT
AT-GAGT
1+1-2+1-1+1+1
= +2
ATGGCGT
ATGA-GT
1+1+1-1-2+1+1
= +2
ATGGCGT
ATG-AGT
1+1+1-2-1+1+1
= +2

44. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Local alignment

45. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
gap A T G G C G T
gap 0 0 0 0 0 0 0 0
A 0
+1 0
+1
0
0 0
0
0
0 0
0
0
0 0
0
0
0 0
0
0
0 0
0
0
0 0
0
0
T 0
0 0
0
0
+2 0
+2
0
0 0
0
0
0 0
0
0
0 0
0
0
0 0
0
0
+1 0
+1
0
G 0
0 0
0
0
0 0
0
0
+3 0
+3
0
+1 0
+1
0
0 0
0
0
+1 0
+1
0
0 0
0
0
A 0
+1 0
+1
0
0 0
0
0
0 0
0
0
0 0
0
0
0 0
0
0
0 0
0
0
0 0
0
0
G 0
0 0
0
0
0 0
0
0
+1 0
+1
0
+1 0
+1
0
0 0
0
0
+1 0
+1
0
0 0
0
0
T 0
0 0
0
0
+1 0
+1
0
0 0
0
0
0 0
0
0
0 0
0
0
0 0
0
0
+2 0
+2
0
Matrix Filling - Gap -2; Mismatch -1; Match +1

46. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
ATG
ATG
1+1+1=+3

47. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
gap A T G G C G T
gap 0 -2 -4 -6 -8 -10 -12 -14
A -2
T -4
G -6
A -8
G -10
T -12
Matrix Filling - Gap -2; Mismatch -1; Match +1

48. Computational Biology and Genomics Facility, Indian Veterinary Research Institute
Note :- Always take the value of gap cost or mismatch cost a negative
value and the values have to be different

49. Fast All (FASTA)
FASTA is the heuristic method first developed by Lipman and Pearson in
1985.
How the FASTA algorithm works?
•FASTA initially finds all hot-spots. Hot-spots are pairs of words of
length k (2 a.a. or 6 nt) that exactly match
•It then scores (substitution matrix) and identifies the 10 best diagonal
runs. A diagonal run is a sequence of nearby hot spots on the same
diagonal.
•All good diagonal runs from close diagonals are combined, to achieve
an alignment. All such alignments are scored to get the best scored
alignment.

51. The Basic Local Alignment Search Tool (BLAST)
▪ The BLAST algorithm was developed by Altschul et al. in 1990 and later
modified in 1997 by them.
▪ It finds regions of local similarity between sequences.
▪ The program compares nucleotide or protein sequences to sequence databases
and calculates the statistical significance of matches.

52. ▪ The query is broken down into words (3 for a.a and 11 for nt). (the
maximum number of words can be calculated: L - w +1= max. word no.
(L=seq.length, w=word)).
▪ For each word from the query sequence find the list of words with high
score using a substitution matrix(PAM or BLOSUM)
▪ For each exact word match, alignment is extended in both directions to
find high score segments

53. 1.Raw score: Scores based on either PAM or BLOSUM
2. Bit-score/Normalized score: To compare different alignments, based on
different scoring systems the score needs to be normalized, e.g.
different substitution matrices.
Important terms in BLAST:

54. E-Value (Expectation Value):
▪In the context of database searches, the E-value (associated to a score S)
is the number of distinct alignments, with a score equivalent to or better
than S, that are expected to occur in a database search by chance.
▪The lower the E value, the more significant the score is.
n = query sequence length;
m = the sum of the lengths of the sequences in the database.
P-value: Probability that an alignment with this score occurs by
chance in a database of size N. The closer the P-value is towards
0, the better the alignment
N * P

55. Interpretation of E-value :
1. E-value is the number of matches with this score one can expect to find
by chance in a database of size N. The closer the E-value is towards 0, the
better the alignment.
▪For nucleotide based searches, one should look for hits with E-values of
10-6 or less and sequence identity of 70% or more
▪For protein based searches, one should look for hits with E-values of 10-3
or less and sequence identity of 25% or more

56. Step 1: Go to the NCBI site (http://www.ncbi.nlm.nih.gov) click on Blast
or type the URL (http://blast.ncbi.nlm.nih.gov/ Blast.cgi)
Step 2: In the page shown below the following options are available :
BLAST

57. In this chapter we will deal with blastn and blastp
▪Step 3 : Clicking on blastn opens a window which provides space for
pasting our query sequence or for entering the accession number

58. Query length is 1662bp
The colour of the line gives
an idea about the total score
of the query with the hit
Step 4 : Click on blast to proceed and see the result (there is an option to
view the results in a new window by ticking on the check box at the
bottom of the page)

59. Max score and Total score are the same until
and unless the query matches with the
subject in complete segments
The total query(100%) of 1662 is
involved in the alignment match
The 99% of the total query length of
1662bp is involved in the alignment match
percent of the query length that is
included in the aligned segments

60. 100% of the query (1-1662)
is involved in the alignment
out of which there is only
9 7 % i d e n t i t y i . e .
1604/1662=96.5%

61. Blastp : Step 1- step 2 are similar, in step 3 press blasp and paste your
sequence of interest and view the result. The only one extra
parameter in blastp result is the positives which is nothing but the
similarity Identities Plus the conservative
substitutions gives the similarity
(positives)

62. Total score vs Max score

65. Multiple Sequence Alignment
▪ It is an extension of pairwise alignment to incorporate more than
two sequences at a time.
▪ Multiple alignment methods try to align all of the sequences in a
given query set.
▪ Alignments are also used to aid in establishing evolutionary
relationships by constructing phylogenetic trees.
▪ Multiple sequence alignment methods can be performed by
following methods :
1.Dynamic programming method : Very slow and out of vogue
2.Progressive methods : Clustal W, Clustal X and T-coffee

66. Steps in Clustal W/Clustal X
▪ Determining all pairwise alignments between sequences and
degrees of similarity between each pair.
▪ Constructing a "rough" similarity tree
▪ Combining the alignments starting from the most closely related
groups to most distantly related groups.

67. 3.Iterative methods : MUSCLE (multiple sequence alignment by log-
expectation),MAFFT (MAFFT is a multiple sequence alignment program
for unix-like operating systems).
These methods work similarly to progressive methods but repeatedly
realign the initial sequences as well as adding new sequences to the
growing MSA.
4.Hidden Markov model : PROBCONS (A practical tool for progressive
protein multiple sequence alignment based on probabilistic consistency),
5.Motif finding methods: MEME(Multiple Expectation Maximization for Motif
finding)

68. Multiple Sequence alignment by ClustalX
Step 1: Load Sequences into ClustalX by choosing a file by browsing
through the computer.

70. Step 2: Do complete Alignment by selecting from the alignment
menu

71. Alignment in progress

72. Alignment file created

73. Step 3: This file can be saved in any of the formats(Clustal,
PHYLIP, fasta etc.).The alignment is saved both in clustal as well as
PHYLIP formats

74. Check the boxes against the formats required and click
OK to save the file in an appropriate location