Levenstein distance is Global Alignment

Useful to compare proteins

But not good when we look for a gene in a genome
or a domain in a protein

What shall we do when the query is much smaller than all subjects?

Semi-global alignment

In this case we want to go from one side to the other

We showed Edit distance

“Distance” means that smaller numbers are better

If the distance is 0, then the two sequences are identical

If the distance is small, the two sequences are similar

If the distance is big, the sequences are different

Different alignments for different questions

• Global alignment compares genes to genes, or proteins to proteins
  • We expect that both sequences are similar
• Semi-global alignment compares genes to genomes
  • We expect that query is similar to part of subject
• Local alignment compares genomes to genomes
  • We expect that part of the query is similar to part of subject

Local alignment is not a distance

Since external gaps do not count, we can always use them

In this case the smallest distance is always 0

The smallest cost is achieved taking one letter

         ABRACADABRA
         |
CHUPACABRA

We cannot solve the minimization problem

Score instead of distance

Instead of finding a minimum, we look for a maximum

• Matching places get positive score
• Mismatch and gaps get negative score

The philosophy is the same, but we look for big numbers instead of small numbers

Dot plot becomes a matrix

Initially we placed 1 or 0 on each cell depending on match or mismatch

Now we put positive or negative numbers, depending of single-letter scores

Single-letter scores (example)

If we pair two letters \(x\) and \(y\), we need to know how they contribute to the score value. For example,

• If \(x = y\) the score increases by 1
• If \(x ≠ y\) the score decreases by 1
• If one of the letters is - (a gap), the score reduces by 2

In general, we have a substitution scoring matrix \(C_{x, y}\) for each \(x\) and \(y\)

Single-letter scores (example)

In one equation

\[C_{x, y} = \begin{cases} +1 & \quad x=y\\ -1 & \quad x≠y\\ -2 & \quad x=\texttt{"-"} \\ -2 & \quad y=\texttt{"-"} \end{cases}\]

Substitution Scoring matrices

The Substitution Scoring Matrix has one row and one column for each letter in our alphabet

For example 4 rows and 4 columns for DNA sequences

In DNA it is common to have +1 for match and -2 for mismatch

Sometimes we can use (+1,-3), (+1,-1) or even (4,-5)

Amino-acid scoring matrices

For proteins the alphabet has 20 letters, so the scoring matrix is 20×20

The scores of these matrices are based on the relative frequency of each mutation in nature

Obviously, we only care about non-lethal mutations

More specifically, we care about the Point Mutations that are Accepted

These are called PAM matrices (Margaret Dayhoff, 1978)

PAM matrices

Based on 1572 mutations in 71 families of closely related proteins

Dayhoff only used alignments having at least 85% identity

She assumed that any aligned mismatches were the result of a single mutation event, rather than several at the same location

PAM matrices

PAM1 is the scoring matrix for proteins in a “short” period

With some easy mathematics we can calculate PAM2, PAM30, PAM250, etc

They represent mutations in 2, 30, and 250 short periods

BLOSUM matrices

Using local alignment we can identify conserved regions

In 1992 Steven Henikoff and Jorja Henikoff created new substitution matrices based on local alignment of blocks

BLOcks SUbstitution Matrix

Idea: each protein domain can evolve at different speeds