bioinformatics

1. COMPUTATIONAL METHODS
OF
SEQUENCE ALIGNMENT
CREATED AND DESIGNED BY
AMMAR KAREEM ALMAJDI
II YEAR III SEMESTER
Department of Biotechnology

2. OUTLINE Bioinformatics
• Sequence Alignment
• Types of a sequence alignment
• Methods of sequence alignment
• Dot Matrix method
• Dynamic programming method
• Word method or k-tuple method

3.  Sequence alignment is a way of arranging sequences of DNA,RNA or
protein to identifyidentify regions of similarity is made to align the entire
sequence. the similarity may indicate the funcutional,structural and
evolutionary significance of the sequence.
 The sequence alignment is made between a known sequence and
unknown sequence or between two unknown sequences.
 The known sequence is called reference sequence.the unknown
sequence is called query sequenc.
Definition of sequence alignment

4. Interpretation of sequence alignment
• Sequence alignment is useful for discovering structural, functional
and evolutionary information.
• Sequences that are very much alike may have similar secondary and
3D structure, similar function and likely a common ancestral
sequence. It is extremely unlikely that such sequences obtained
similarity by chance. For DNA molecules with n nucleotides such
probability is very low P = 4-n. For proteins the probability even much
lower P = 20 –n, where n is a number of amino acid residues
• Large scale genome studies revealed existence of horizontal
transfer of genes and other sequences between species, which may
cause similarity between some sequences in very distant species.

5. Types of Sequence Alignment
 Sequence Alignment is of two types , namely :
 Global Alignment
 Local Alignment
 Global Alignment : is a matching the residues of two sequences
across their entire length.
 global alignment matches the identical sequences .
 Local Alignment : is a matching two sequence from regions which
have more similarity with each other.

6. Types of Sequence Alignment
 Global alignment
 Input: treat the two sequences as potentially equivalent
 Goal: identify conserved regions and differences
 Applications:
- Comparing two genes with same function (in human vs. mouse).
- Comparing two proteins with similar function.

7. Types of Sequence Alignment
 Local alignment
 Input: The two sequences may or may not be related
 Goal: see whether a substring in one sequence aligns well with a
substring in the other
 Note: for local matching, overhangs at the ends are not treated as gaps
 Applications:
- Searching for local similarities in large sequences
(e.g., newly sequenced genomes).
- Looking for conserved domains or motifs in two proteins

8. Types of Sequence Alignmentu
• L G P S S K Q T G K G S - S R I W D N
• Globalalignment
• L N - I T K S A G K G A I M R L G D A
• - - - - - - - T G K G - - - - - - - -
• Localalignment
• - - - - - - - A G K G - - - - - - - -

9. • Dot matrix method
• The dynamic programming (DP) algorithm
• Word or k-tuple methods
Method of sequence alignment

10. • A dot matrix is a grid system where the similar nucleotides of two DNA
sequences are represented as dots.
• It also called dot plots.
• It is a pairwise sequence alignment made in the computer.
• The dots appear as colourless dots in the computer screen.
• In dot matrix , nucleotides of one sequence are written from the left to
right on the top row and those of the other sequence are written from the
top to bottom on the left side (column) of the matrix.At every point,
where the two nucleotides are the same , a dot in the intersection of row
and column becomes a dark dot. when all these darken dots are
connected, it gives a graph called dot plot. the line found in the dot plot is
called recurrence plot. Each dot in the plot represents a matching
nucleotide or amino acid.
Dot matrix analysis

11. • Dot matrix method is a qualitative and simple to analyze
sequences.however ,it takes much time to analyze large sequences.
• Dot matrix method is useful for the following studies :
•Sequence similarity between two nucleotide sequences or two amino
acid sequences.
•Insertion of short stretches in DNA or amino acid sequence.
•Deletion of short stretches from a DNA or amino acid sequence.
•Repeats or inserted repeats in a DNA or amino acid sequence.
Dot matrix analysis

12. • Nucleic Acids Dot Plots
Dot matrix analysis: Two identical sequences

13. • Nucleic Acids Dot Plots of genes
Dot matrix analysis: two very different sequences

14. • Nucleic Acids Dot Plots of genes
Dot matrix analysis: two similar sequences

15. Dynamic Programming Method
• Is the process of solving problems where one needs to find the best
decision one after another.
• It was introduced by Richard Bellman in 1940.
• The word programming here denotes finding an acceptable plan of action
not computer programming.
• It is useful in aligning nucleotide sequence of DNA and amino acid
sequence of proteins coded by that DNA .
• Dynamic programming is a three step process that involves :
1) Breaking of the problem into small subproblems.
2) Solving subproblems using recursive methods.
3) Construction of optimal solutions for original problem using the optimal
solutions .

16. • The method compares every pair of characters in the two sequences and
generates an alignment, which is the best or optimal.
• This is a highly computationally demanding method. However the latest
algorithmic improvements and ever increasing computer capacity make possible
to align a query sequence against a large DB in a few minutes.
• Each alignments has its own score and it is essential to recognise that several
different alignments may have nearly identical scores, which is an indication
that the dynamic programming methods may produce more than one optimal
alignment. However intelligent manipulation of some parameters is important
and may discriminate the alignments with similar scores.
• Global alignment program is based on Needleman-Wunsch algorithm and local
alignment on Smith-Waterman. Both algorithms are derivates from the basic
dynamic programming algorithm.
Dynamic programming algorithm for sequence
alignment

17. • The alignment procedure depends upon scoring system, which can be based on
probability that 1) a particular amino acid pair is found in alignments of related
proteins (pxy); 2) the same amino acid pair is aligned by chance (pxpy); 3)
introduction of a gap would be a better choice as it increases the score.
• The ratio of the first two probabilities is usually provided in an amino acid
substitution matrix. There are many such matrices, two of them PAM and
BLOSUM are considered later.
• The score for the gap introduction and its extension is also calculated from the
matrices and represent a prior knowledge and some assumptions. One of them is
quite simple, if negative cost of a gap is too high a reasonable alignment
between slightly different sequences will be never achieved but if it is too low
an optimal alignment is hardly possible. Other assumptions are based on
sophisticated statistical procedures.
Description of the dynamic programming algorithm

18. Derivation of the dynamic programming
algorithm
1. Score of new = Score of previous + Score of new
alignment alignment (A) aligned pair
V D S - C Y V D S - C Y
V E S L C Y V E S L C Y
15 = 8 + 7
2. Score of = Score of previous + Score of new
alignment (A) alignment (B) aligned pair
V D S - C V D S - C
V E S L C V E S L C
8 = -1 + 9
3. Repeat removing aligned pairs until end of alignments is reached

19. Scoring matrices: PAM (Percent Accepted
Mutation)
Amino acids are grouped according to to the chemistry of the side group: (C) sulfhydryl, (STPAG)-
small hydrophilic, (NDEQ) acid, acid amide and hydrophilic, (HRK) basic, (MILV) small hydrophobic,
and (FYW) aromatic. Log odds values: +10 means that ancestor probability is greater, 0 means that the
probability are equal, -4 means that the change is random. Thus the probability of alignment YY/YY is
10+10=20, whereas YY/TP is –3-5=-8, a rare and unexpected between homologous sequences.

20. Scoring matrices: BLOSUM62
(BLOcks amino acid SUbstitution Matrices)
Ideology of BLOSUM is similar but it is calculated from a very different and much larger set
of proteins, which are much more similar and create blocks of proteins with a similar pattern

21. • This diagram indicates the moves that are possible to reach a certain position (i,j) starting from the
previous row and column at position (i -1, j-1) or from any position in the same row or column
• Diagonal move with no gap penalties or move from any other position from column j or row i, with
a gap penalty that depends on the size of the gap
Formal description of dynamic programming
algorithm
i -x
i -1
j -1i -y j
i
Si - x,j - wx
Si –1, j- 1 + s(ai , bj)
Si, j - y - wy Si, j

22. Word Method or K-tuple method
• It is used to find an optimal alignment solution,but is more than dynamic
programming .
• This method is useful in large-scale database searches to find whether there
is significant match available with the query sequence.
• Word method is used in the database search tools FASTA and the BLAST
family .
• They identify a series of short ,non-overlapping subsequences (words) of
the query sequence.
• Then they are matched to candidate database sequences to get result .

23. Word Method or K-tuple method
• In the FASTA method ,the user defines a value k to use as the word length
to search the database .it is slower but more sensitive at lower values of
k .they are also perferred for serches involving a very short qurery
sequence .
• The BLAST provides a number of algorithms optimized for particular types
of queries ,for distantly related sequence matches.
• It is a good alternative to FASTA .However , the results are not very
accurate .
• Like FASTA ,BLAST uses a word search of length k ,but evaluates only
the most significant word m,latches rather than every word match .

24. T H A N K Y O U