[BiO BB] GC variation in Genome
wwhsiao at sfu.ca
Tue Oct 14 13:09:19 EDT 2003
Our lab has developed a web service, IslandPath
(http://www.pathogenomics.sfu.ca/islandpath) that is useful in looking
for horizontally transferred regions. The source code for the
application is available if you wish to use it for your in-house
analysis. Background in Perl will be required to modify the source code
for your own needs. Alternatively, once your genome is published or if
the genomic sequence and annotation becomes available, I can put it
through IslandPath to produce the output.
Graduate Student, Brinkman Laboratory
Department of Molecular Biology and Biochemistry
Simon Fraser University, 8888 University Dr.
Burnaby, BC, Canada V5A 1S6
Phone: 604-291-4206 Fax: 604-291-5583
From: bio_bulletin_board-admin at bioinformatics.org
[mailto:bio_bulletin_board-admin at bioinformatics.org] On Behalf Of Adarsh
Sent: October 14, 2003 3:55 AM
To: bio_bulletin_board at bioinformatics.org
Subject: [BiO BB] GC variation in Genome
A warm hello! Based on you expertice, I was
interested to ask you guys for some opinions and I
would highly appreciate if you could sqeeze in any
time you have for me to reply.
Basically I am working with a very peculiar
prokaryotic genome(yet to be annotated) and I am sure
you agree with me that they are all unique in their
I am confronted with a few difficulties and felt I
could run by you to have your opinion on it.
There is a very high GC variation(as low as 40-67% at
some regions) in the so far highly polished genome
contig we have. I believe and suspect a lot of lateral
gene transfers and funny starts and codon usage
statistics. I would like to basically understand these
variation and quantify them with respect to their
inherent nature of variation.
I am looking at finding these regions of variations,
first of all to see if they(these variation in
geneome) are close to each other in clusters or
entirely in random at different regions of genome,
then I would like to see the gene's in these regions
and study their codon usage(if they are similar or
different and how variable are these with amongst
themselves), extract such typical funny ones,
statistically group them according to their location
in genome, and then try to do some similarity searches
to fish out if any homologs from other published data,
try to build a phyologenitc tree to derive at
conclusion of variation, gene families and so on.
Could you guys suggest how I could do them or rather
best way of doing it? I mean is there any software
that you know off where I can graphically see the
variations in the contigs with respect to GC?
I would be very grateful and highly appreciate of any
time you can offer to inform me on these issues.
Hoping to hear soon. Have a great day!
Thanks and Regards
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