Content of the previous class Introduction The evolutionary basis of sequence alignment The Modular Nature of proteins

Optimal alignment methods

Number of distinct alignments is large So it is usually of interest to identify the ‘‘best’’ one among them The Needleman-Wunsch algorithm is an application of a best-path strategy It is called dynamic programming to the problem of finding optimal sequence alignments

It is important that optimal methods always report the best alignment that can be achieved, even if it has no biological meaning. The one with the highest score is reported as the optimal local alignment. When searching for local alignments there may be several significant alignments so it is a mistake to look only at the optimal one.

Alignment methods 1. Dot matrix analysis 2. The dynamic programming a). Global alignment of sequences by Needleman and Wunsch b). Local alignment by Smith and Waterman 3. Word or k-tuple methods, such as used by the programs FASTA and BLAST

Dot matrix analysis

A dot matrix analysis is a method for comparing two sequences to look for possible alignment of characters between the sequences First described by Gibbs and McIntyre (1970) Dot matrix method displays any possible sequence alignments as diagonals on the matrix.

Dot matrix analysis can readily reveal the presence of insertions/deletions and direct and inverted repeats.

Tolls DNA Strider used on a Macintosh computer. The program DOTTER has interactive features for the UNIX The Genetics Computer Group programs(GCG) COMPARE and DOTPLOT also perform a dot matrix analysis.

Dynamic programming

Dynamic programming is a computational method that is used to align two protein or nucleic acid sequences. It provides the very best or optimal alignment between sequences The method compares every pair of characters in the two sequences and generates an alignment.

This alignment will include matched and mismatched characters and gaps in the two sequences The method has been proven mathematically to produce the best or optimal alignment between two sequences under a given set of match conditions.

The dynamic programming method, first used for global alignment of sequences by Needleman and Wunsch (1970) and For local alignment by Smith and Waterman (1981), provides one or more alignments of the sequences

Global Alignment: Needleman- Wunsch Algorithm The optimal score at each matrix position is calculated by adding the current match score to previously scored positions and subtracting gap penalties Each matrix position may have a positive or negative score, or 0.

Local Alignment: Smith-Waterman Algorithm Local alignments are usually more meaningful than global matches because they include patterns that are conserved in the sequences.

The rules for calculating scoring matrix values are slightly different The most important differences being (1)the scoring system must include negative scores for mismatches, and (2) when a dynamic programming scoring matrix value becomes negative, that value is set to zero which has the effect of terminating any alignment up to that point