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This homepage will link you to other
sites regarding background information to the genome project and
the current state with respect to annotation. Links can be found
at the left.
Azotobacter@JGI - JGI is the
Joint Genome Institute in Walnut Creek, CA, where the sequence
and assembly took place. Some raw data is available.
Technical questions concerning the project
can be addressed to either of the Co-PI's of the project: Nirav
Merchant (nirav@arl.arizona.edu),
director of the Biotechnology Computing Facility at the University
of Arizona or Christina Kennedy (ckennedy@u.arizona.edu),
in the Department of Plant Pathology, University of Arizona. Paul
Rudnick is no longer project coordinator but remains part of the
annotation team while at NCI-Frederick.
Azotobacter vinelandii was
selected for whole genome sequencing by the Department of Energy's
Joint Genome Institute as part of
the Microbial
Genomes Program and is currently in the annotation-by-hand
phase. Automated annotation was carried out at
Oak Ridge National Laboratories under the direction of Dr.
Frank Larimer, using their Prokaryotic
Genome Annotation pipeline. A gathering of collaborators took
place during the week of July 8th at the University of Arizona
in Tucson. This was named the Azotobacter Genome Annotation Workshop
and was supported by DOE and Arizona Research Laboratory, Biotechnology
division. A photoalbum of workshop activities will be ready for
viewing on the site soon. If anyone else would like to be involved
in annotation of Azotobacter, or would like to see how the process
unfolds, please contact Nirav or Christina.
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Current Status
from JGI (10/01/02)
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Strain:AvOP
Size: 4.5 Mb (initially estimated)
Release: Version 3 (PHRAP assembly)
5,349,134 bases total
Coverage: 86,204 reads (~ 8.0x) based on 600bp readlength
Total Contigs: 91
Major contigs(>20 reads, >2 Kb): 67 (55,330,053
bases)
Largest Contigs: 991Kb, 438Kb, 338Kb
Currently: Pre-finishing, JGI Andrea Aerts
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Current Status
from ORNL (4/19/02)
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5,183,316 bp (200kb
of Ns) in 53 contigs of 20 reads or greater
63.1 % GC
5574 candidate protein-encoding gene models
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