6
$\begingroup$

So if you see the BLAST parameters it says

The Expected value E is a parameter that describes the number of hits one can "expect" to get by chance when searching a database of particular size. It decreases exponentially as the score (S) increases. Essential, the E value describes the randomness background noise. For example an E value of 1 assigned to a hit can be interpreted as meaning that a database of the current size one might expect to see 1 match with a similar score simply by chance. Lower the E value the better."

How could there be something "BY CHANCE" it's a software right ?

$\endgroup$
7
$\begingroup$

The Blast E is the expected frequency of obtaining false positive. This will depend on your query size (number of nucleotide or amino acid residues) and the size of the database. With a short query and a large database you are more likely to have a sequence in your database that matches your query by simple chance. In summary, the smaller the query and the larger the database the greater the chance of a spurious result and that is what E is measuring.

$\endgroup$
5
$\begingroup$

Sure, so you start out with what is called a bitscore, which is a normalized to the score calculated from the alignment between the 2 seqs which depends on the following equation. It is independent of database size:

(lambda * S - ln(k))/(ln)2

Then, the p-value of a local blast is just:

1/2^bitscore

So if your bitscore is 15, you need 1/32768 alignments before you will get a score as good or better (highly similar sequences) BY CHANCE ALONE. This addresses your "BY CHANCE" question.

Earlier I said the bitscore was independent of database size. The E value is just the p-value above normalized to the database size (so it is dependent on the db size) by the following equation:

(query length * database length * p-value)

which simplifies a bit to:

E = (query length* (db length/2^bitscore)

I hope this helps!

$\endgroup$
0
$\begingroup$

Calculation of E for Blast is a good question.

Two methods:

  • Use a Poison or binomial distribution.
  • Use randomization

I think its no. 1 because no. 2 would use too much computational power. I did know but someone borrowed by O'Reilly book on the subject.

One of the contributors here will know, because I suspect their genomics developers.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.