Sequence Alignment

1. Sequence Alignment Sequence alignment is the procedure of comparing two (pair-wise alignment) or more multiple sequences by searching for a series of individual characters or patterns that are in the same order in the sequences. A way of arranging the sequences of DNA, RNA, or protein to identify Regions of Similarity Types of alignment: - Local alignment - Global alignment

2. Global Alignment In global alignment, an attempt is made to align the entire sequence. If two sequences have approximately the same length and are quite similar, they are suitable for the global alignment. L G P S S K Q T G K G S - S R I W D N L N - I T K S A G K G A I M R L G D A

3. Local Alignment - - - - - - - T G K G - - - - - - - - - - - - - - - A G K G - - - - - - - - Local alignment concentrates on finding stretches of sequences with high level of matches.

4. Sequence Interpretation 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. 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. Sequence Alignment Method Dot matrix analysis The dynamic programming

6. Dot Matrix Analysis A dot matrix analysis is a method for comparing two sequences to look for possible alignment (Gibbs and McIntyre 1970) One sequence (A) is listed across the top of the matrix and the other (B) is listed down the left side Starting from the first character in B, one moves across the page keeping in the first row and placing a dot in many column where the character in A is the same The process is continued until all possible comparisons between A and B are made Any region of similarity is revealed by a diagonal row of dots Isolated dots not on diagonal represent random matches

7. Dot Matrix Analysis Detection of matching regions can be improved by filtering out random matches and this can be achieved by using a sliding window It means that instead of comparing a single sequence position more positions is compared at the same time and dot is printed only if a certain minimal number of matches occur Dot matrix analysis can also be used to find direct and inverted repeats within the sequences

8. Dot Matrix Analysis

9. Dot Matrix Analysis Two very different sequences Two similar sequences

10. Dynamic Programming 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. 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. 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.

11. Needleman-Wunsch Method • It quantifies the similarity between two sequences. • Any measurement of similarity must be done with respect to the best possible alignment between two sequences • If good matches are found, the search results in a high scoring segment pairs. • Over the course of evolution, some positions of base or amino acid in a sequence undergo - Substitutions - Insertions - Deletions • Insertion/deletion are less common as compared to substitutions. • Gaps are penalized more heavily than mismatches when calculating a similarity score.