Sequence alignment involves comparing two or more sequences to identify regions of similarity. It can be used to determine if genes or proteins are evolutionarily related or identify structurally similar regions. The alignment process involves placing the sequences side by side and introducing gaps to maximize matches. Dynamic programming algorithms like Needleman-Wunsch calculate the optimal global alignment through matrix initialization, scoring, and traceback steps.

1. Definition:
•Procedure for comparing two or more sequences by
searching for a series of individual characters or
character patterns that are in the same order in the
sequences.
– Pair-wise alignment: compare two sequences.
– Multiple sequence alignment: compare more than
two sequences.
Sequence Alignment

2. Need
• To find whether two (or more) genes or
proteins are evolutionarily related to each
Other.
• To find structurally or functionally similar
regions within proteins.

3. • Align abcdef with abdgf
• Write second sequence below the first
abcdef
abdgf
• Move sequences to give maximum match
between them.
• Show characters that match using vertical bar.
Example

4. Example sequence alignment
abcdef
||
abdgf
abcdef
|| || |
ab-dgf

5. Matching Similarity vs. Identity
• Alignments can be based on finding only identical
characters, or (more commonly) can be based on
finding similar characters.
• Conserved substitutions, semi-conserved
substitution, and non conserved substitution are
the terms used to define similarity.

6. Matching Similarity vs. Identity
Different colours denote different chemical groups of amino acids, i.e. hydrophobic, acidic, etc.

7. Inferences
• In sequence alignment, the degree of similarity
between particular regions in the sequences can
be interpreted as a rough measure of how
conserved a particular region or sequence motif is
among lineages
• In conserved regions the order of letters does not
change during evolution or changes only slightly,
i.e. has mostly conservative substitutions.
• Sequence motif = sequence pattern that is
widespread and has a biological significance
resulting in its conservation through evolution.

8. Global vs. Local Alignment
– Global alignment algorithms which optimize
overall alignment between two sequences.
– Local alignment algorithms which seek only
relatively conserved pieces of sequence.
Alignment stops at the ends of regions of
strong similarity.
– Favors finding conserved patterns in different pairs
of sequences.

9. • Global
LGPSSKQTGKGS-SRIWDN
LN-TKSAGKGAIMRLGDA
• Local
--------GKG--------
|||
--------GKG--------

10. Methods for Pair-wise Alignment
• Dot matrix analysis
• Dynamic Programming (Needleman- Wunsch,
Smith-Waterman algorithms)
• Word or k-tuple methods (BLAST and FASTA)

11. DOT MATRIX

12. Interpretation of Dot Matrices
• Regions of similarity appear as diagonal runs
of dots.
• Interruption in middle of diagonal line
indicates insertions or deletions.
• Parallel diagonal line within the matrix
represents repetitive regions of the sequence.

13. Uses
• Can use dot matrices to align two proteins or two
nucleic acid sequences.
• Can use to find amino acid repeats within a
protein by comparing a protein sequence to itself.
• Used in identifying Nucleic Acids secondary
structure detecting self complementarily of the
sequence.
• Used in comparative genomics by predicting gene
order conservation between closely related
genomes.

14. Limitations
• A problem with dot matrices for long
sequences is that they can be very noisy due
to lots of insignificant matches.
• Only a pairwise alignment method not
suitable for multiple alignment of sequences.
• It lacks statistically rigor in assessing the
quality of alignment.

15. Solution
• By using a window (W)/ tuple.
– compare character by character within a
window (have to choose window size).
– require certain fraction of matches within
window in order to display it with a “dot”.

16. W=23
set of
stacked
diagonals
in upper
Left

17. • Initialisation
• Matrix fill (scoring)
• Traceback (alignment)
Dynamic Programming Approach
Steps
M= (length of sequence i)
N= (Length of sequence ii)

18. Initialization Step

19. Scoring
• For each position, Mi,j is defined to be the
maximum score at position i,j; i.e.
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)]
• In the following case, Mi-1,j-1 will be red, Mi,j-1 will
be green and Mi-1,j will be blue.

20. • A simple scoring scheme is assumed where
– Si,j = 1 if the residue at position i of sequence #1 is
the same as the residue at position j of sequence
#2 (match score); otherwise
– Si,j = 0 (mismatch score)
– w = 0 (gap penalty)

21. Matrix Fill Step

26. Traceback Step

30. Final Alignment
G A A T T C A G T T A
| | | | | |
G G A _ T C _ G _ _ A

34. Summary
• The NW alignment is over the entire length of two Sequences (the
traceback starts from the lower right corner of the traceback
matrix, and completes in the upper left cell of this matrix).
• The Needleman-Wunsch algorithm works in the same way
regardless of the length or complexity of sequences and guarantees
to find the best alignment.
• The Needleman-Wunsch algorithm is appropriate for finding the
best alignment of two sequences which are
(i) of the similar length.
(ii) similar across their entire lengths.