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Plant Physiol, March 2001, Vol. 125, pp. 1166-1174
Rice Bioinformatics. Analysis of Rice Sequence Data and
Leveraging the Data to Other Plant Species1
Qiaoping
Yuan,
John
Quackenbush,
Razvan
Sultana,
Mihaela
Pertea,
Steven L.
Salzberg, and
C. Robin
Buell*
The Institute for Genomic Research, 9712 Medical Center Drive,
Rockville, Maryland 20850
Rice (Oryza sativa) is a model species for
monocotyledonous plants, especially for members in the grass family.
Several attributes such as small genome size, diploid nature,
transformability, and establishment of genetic and molecular resources
make it a tractable organism for plant biologists. With an estimated
genome size of 430 Mb (Arumuganathan and Earle, 1991), it is feasible
to obtain the complete genome sequence of rice using current
technologies. An international effort has been established and is in
the process of sequencing O. sativa spp.
japonica var "Nipponbare" using a bacterial
artificial chromosome/P1 artificial chromosome shotgun sequencing
strategy. Annotation of the rice genome is performed using
prediction-based and homology-based searches to identify genes.
Annotation tools such as optimized gene prediction programs are being
developed for rice to improve the quality of annotation. Resources are
also being developed to leverage the rice genome sequence to partial
genome projects such as expressed sequence tag projects, thereby
maximizing the output from the rice genome project. To provide a low
level of annotation for rice genomic sequences, we have aligned all
rice bacterial artificial chromosome/P1 artificial chromosome sequences
with The Institute of Genomic Research Gene Indices that are a set of
nonredundant transcripts that are generated from nine public plant
expressed sequence tag projects (rice, wheat, sorghum, maize, barley,
Arabidopsis, tomato, potato, and barrel medic). In addition, we
have used data from The Institute of Genomic Research Gene Indices and
the Arabidopsis and Rice Genome Projects to identify putative
orthologues and paralogues among these nine genomes.
1
This work was supported in part by the U.S.
Department of Agriculture (grant no. 99-35317-8275 to C.R.B.), by the
National Science Foundation (grant no. DBI998282 to C.R.B.), and by the U.S. Department of Energy (grant no. DE-FG02-99ER20357 to C.R.B.). This work was also supported by the U.S. Department of Energy (grant
no. DE-FG02-99ER62852 to J.Q.) and by the U.S. National Science
Foundation (grant nos. DBI-9983070, DBI-9813392, and DBI-9975866 to
J.Q.). J.Q. was also supported in part by the National Science Foundation (grant no. KDI-9980088). S.L.S. and M.P. were supported in
part by the National Institutes of Health (grant no. R01-LM06845) and
by the National Science Foundation (grant nos. KDI-9980088 and
IIS-9902923).
*
Corresponding author; e-mail rbuell{at}tigr.org; fax
301-838-0208.
© 2001 American Society of Plant Physiologists
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