BLAST

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20 Tips to Improve Your BLAST Searches

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8.5 Use the Karlin-Altschul Equation

to Design Experiments

The Karlin-Altschul equation is very useful for predicting the outcome of a BLAST

experiment, especially in large search spaces. Suppose you want to find exons in the

human genome by looking for similarities in the pufferfish genome. These genomes

last shared a common ancestor about 450 million years ago. You might assume that

any similarities at this distance must be due to evolutionary conservation.

Recall from Chapter 4 that the number of alignments expected by chance (E) is a

function of the search space (M, N), the normalized score (λS), and a minor con-

stant (K).

The typical cross-species parameters +1/-1 match/mismatch have a target frequency

of 75 percent identity and 0.55 nats per aligned letter on average (H). A 50-bp align-

ment therefore contains about 27.5 nats. Substituting this normalized score into the

Karlin-Altschul equation with K=0.334, M=1.5 GB (assuming half of the human

genome contains repeats), and N=450 MB (the size of the repeat-poor pufferfish

genome), you expect about 230,000 alignments by chance. That’s roughly the same

as the number of exons in the human genome. If you want to look for 50-bp exons,

you’ll have to sift through a lot of false positives.

To