[bip] testing for clustered-ness on non-random background

[Bruce Southey]

On Tue, Feb 17, 2009 at 4:57 PM, Brent Pedersen <bpederse at gmail.com> wrote:
> On Tue, Feb 17, 2009 at 2:28 PM, Bruce Southey <bsouthey at gmail.com> wrote:
>> Brent Pedersen wrote:
>>>
>>> hi, this isn't a python question per se, but it seems like it might be
>>> a good place to ask.
>>> so i'd like to take a class of genes on a chromosome and see if they
>>> are "clustered".
>>> is there a good way to do this given that the genes are _already_
>>> clustered/non-randomly distributed
>>> along the chromosome due to the centromere, local duplications, etc?
>>>
>>
>> What do you mean by 'non-random' and, therefore, 'random'?
>> For that matter, what do you mean by 'distributed' and how do you measure
>> it?
>>
>
> well, that's pretty much what i am asking how to ask.

Actually not :-)

> i guess i could
> phrase it like the proverbial coin toss where knowing you just flipped
> heads tells you nothing about future flips. so: does knowing the
> location of a given member of a gene family tell me /anything/ about
> the probable locations of the other members of the gene family? and
> from there, take into account the non-randomness of the genome.

There are plenty of examples were the duplications are adjacent,
examples of duplications on different chromosomes and examples of
both. So in some cases simply knowing that a gene is a member of a
certain family in a specific species that has been sufficiently
characterized will tell you if another member is adjacent or not. If
it is a member of the insulin family then it could be adjacent but
also probably not as it really depends on what species and actual
gene.

What do you mean by non-randomness of the genome?
For what you are looking at the fact that genome has recombination
hotspots then that may be irrelevant as I am not sure that you would
find extensive association with gene duplication.

Bruce