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The map-based sequence of the rice
genome
International Rice Genome Sequencing Project*
Rice, one of the world’s most important food plants, has important syntenic relationships with the other cereal species
and is a model plant for the grasses. Here we present a map-based, finished quality sequence that covers 95% of the
389 Mb genome, including virtually all of the euchromatin and two complete centromeres. A total of 37,544 non-
transposable-element-related protein-coding genes were identified, of which 71% had a putative homologue in
Arabidopsis. In a reciprocal analysis, 90% of the Arabidopsis proteins had a putative homologue in the predicted rice
proteome. Twenty-nine per cent of the 37,544 predicted genes appear in clustered gene families. The number and
classes of transposable elements found in the rice genome are consistent with the expansion of syntenic regions in the
maize and sorghum genomes. We find evidence for widespread and recurrent gene transfer from the organelles to the
nuclear chromosomes. The map-based sequence has proven useful for the identification of genes underlying agronomic
traits. The additional single-nucleotide polymorphisms and simple sequence repeats identified in our study should
accelerate improvements in rice production.
Rice (Oryza sativa L.) is the most important food crop in the world
and feeds over half of the global population. As the first step in a
systematic and complete functional characterization of the rice
genome, the International Rice Genome Sequencing Project
(IRGSP) has generated and analysed a highly accurate finished
sequence of the rice genome that is anchored to the genetic map.
Our analysis has revealed several s alient features o f the rice
genome:
. We provide evidence for a genome size of 389 Mb. This size
estimation is , 260 Mb larger than the fully sequenced dicot plant
model Arabidopsis thaliana. We generated 370 Mb of finished
sequence, representing 95% coverage of the genome and virtually
all of the euchromatic regions.
. A total of 37,544 non-transposable-element-related protein-cod-
ing sequences were detected, compared with ,28,000–29,000 in
Arabidopsis, w ith a lower gene density of one gene per 9.9 kb in
rice. A total of 2,859 genes seem to be unique to rice and the other
cereal s, some of which might di fferentiate monocot and dicot
lineages.
. Gene knockouts are useful tools for determining gene function
and relating genes to phenotypes. We identified 11,487 Tos17 retro-
transposon insertion sites, of which 3,243 are in genes.
. Between 0.38 and 0.43% of the nuclear genome contains orga-
nellar DNA fragments, representing repeated and ongoing transfer of
organellar DNA to the nuclear genome.
. The transposon content of rice is at least 35% and is populated by
representatives from all known transposon superfamilies.
. We have identified 80,127 polymorphic sites that distinguish
between two c ultivated rice subspecies, j aponica and indica,
resulting in a high-resol ution genetic map for rice. Single-nucleo-
tide polymorp hism (SNP) frequency varies from 0.53 to 0.78%,
which is 20 times the frequency observed between the Co lumbia
and Landsberg erecta ecotypes of Arabidopsis.
. A comparison between the IRGSP genome sequence and the
6.3 £ indica and 6 £ japonica whole-genome shotgun sequence
assemblies revealed that the draft sequences provided coverage of
69% by indica and 78% by japonica relative to the map-based
sequence.
Rice has played a central role in human nutrition and culture for
the past 10,000 years. It has been estimated that world rice pro-
duction must increase by 30% over the next 20 years to meet
projected demands from population increase and economic devel-
opment
1
. Rice grown on the most productive irrigated land has
achieved nearly maximum production with current strains
1
.
Environmental degradation, including pollution, increase in night
time temperature due to global warming
2
, reductions in suitable
arable land, water, labour and energy-dependent fertilizer provide
additional constraints. These factors make steps to maximize rice
productivity particularly important. Increasing yield potential and
yield stability will come from a combination of biotechnology and
improved conventional breeding. Both will be dependent on a high-
quality rice genome sequence.
Rice benefits from having the smallest genome of the major cereals,
dense genetic maps and relative ease of genetic transformation
3
. The
discovery of extensive genome colinearit y among the Poaceae
4
has
established rice as the model organism for the cereal grasses. These
properties, along with the finished sequence and other tools under
development, set the stage for a complete functional characterization
of the rice genome.
The International Rice Genome Sequencing Project
The IRGSP, formally established in 1998, pooled the resources of
sequencing groups in ten nations to obtain a complete finished
quality sequence of the rice genome (Oryza sativa L. ssp. japonica
cv. Nipponbare). Finished quality sequence is defined as containing
less than one error in 10,000 nucleotides, having resolved ambigu-
ities, and having made all state-of-the-art attempts to close gaps.
The IRGSP released a high-quality map-based draft sequence in
ARTICLES
*Lists of participants and affil iations appear at the end of the paper
Vol 436|11 August 2005|doi:10.1038/nature03895
793
© 2005 Nature Publishing Group
December 2002. Three completely sequenced chromosomes have
been published
5–7
, as well as two completely sequen ced centro-
meres
8–10
. As the IRGSP subscribed to an immediate-release policy,
high-quality map-based sequence has been public for some time.
This has permitted rice geneticists to identify several genes under-
lying traits, and revealed very large and previously unknown seg-
mental duplications that comprise 60% of the genome
11–13
. The
public sequence has also revealed new details about the syntenic
relationships and gene mobility between ri ce, maize and sor-
ghum
13–15
.
Physical maps, sequencing and coverage
The IRGSP sequenced the genome of a single inbred cultivar, Oryza
sativa ssp. japonica cv. Nipponbare, and adopted a hierarchical clone-
by-clone method using bacterial and P1 artificial chromosome clones
(BACs and PACs, respectively). This strategy used a high-density
genetic map
16
, expressed-sequence tags (ESTs)
17
,yeastartificial
chromosome (YAC)- and BAC-based physical maps
18–20
, BAC-end
sequences
21
and two draft sequences
22,23
. A total of 3,401 BAC/PAC
clones (Table 1) were sequenced to approximately tenfold sequence
coverage, assembled, ordered and finished to a sequence quality of
less than one error per 10,000 bases. A majority of physical gaps in
the BAC/PAC tiling path were bridged using a variety of substrates,
including PCR fragments, 10-kb plasmids and 40-kb fosmid
clones. A total of 62 unsequenced physical gaps, including nine
centromere and 17 telomere gaps, remain on the 12 chromosomes
(Table 2). Chromosome arm and telomere gaps were measured,
and the nine centromere gaps were estimated on the basis of
CentO satellite DNA content. The remaining gaps are estimated to
total 18.1 Mb.
Ninety-seven percent of the BAC/PACs and gap sequences (3,360)
have been submitted as finished quality in the PLN division of
GenBank/DDBJ/EMBL. These and the remaining draft-sequenced
clones were used to construct pseudomolecules representing the 12
chromosomes of rice (Fig. 1). The total nucleotide sequence of the 12
pseudomolecules is 370,733,456 bp, with an N-average continuous
sequence length of 6.9 Mb (see Table 1 for a definition of N-average
length). Sequence quality was ass essed by comparing 1.2 Mb of
overlapping sequence produced by different laboratories. The overall
accuracy was calculated as 99.99% (Supplementary Table 2). The
statistics of sequenced PAC/BAC clones and pseudomolecules for
each chromosome are shown in Table 1.
The genome size of rice (O. sativa ssp. japonica cv. Nipponbare)
was reported to have a haploid nuclear DNA content of 394 Mb on
the basis of flow cytometry
24
, and 403 Mb on the basis of lengths of
anchored BAC contigs and estimates of gap sizes
20
. Table 2 shows the
calculated size for each chromosome and the estimated coverage.
Adding the estimated length of the gaps to the sum of the non-
overlapping sequence, the total length of the rice nuclear genome was
calculated to be 388.8 Mb. Therefore, the pseudomolecules are
expected to cover 95.3% of the entire genome and an estimated
98.9% of the euchromatin. An independent measure of genome
coverage represented by the pseudomolecules was obtained by
searching for unique EST markers
19
; of 8,440 ESTs, 8,391 (99.4%)
were identified in the pseudomolecules.
Centromere location
Typical eukaryotic centromeres contain repetitive sequences, includ-
ing satellite DNA at the centre and retrotransposons and transposons
in the flanking regions. All rice centromeres contain the highly
repetitive 155–165 bp CentO satellite DNA, together with centro-
mere-specific retrotransposons
25,26
. The CentO satellites are located
within the functional domain of the rice centromere
10,26
. Complete
sequencing of the centromeres of rice chromosomes 4 and 8 revealed
that they consist of 59 kb and 69 kb of clustered CentO repeats
(respectively)
8–10
, tandemly arrayed head-to-tail within the clusters.
Numerous retrotransposons, including the centromere-specific
RIRE7, are found between and around the CentO repeats. CentO
clusters show differences in len gth and orientation for the two
centromeres.
BLASTN analysis of the pseudomolecules indicated that about
0.9 Mb of CentO repeats (corresponding to more than 5,800 copies of
the satellite) were sequenced and found to be associated with
centromere-specific retroelements. Locations of all CentO sequences
correspond to genetically identified centromere regions (Supplemen-
tary Table 3). Our pseudomolecules cover the centromere regions on
chromosomes 4, 5 and 8, and portions of the centromeres on the
remaining chromosomes (Fig. 1).
Gene content, expression and distribution
We masked the pseudomolecules for repetitive sequences and used
the ab initio gene finder FGENESH to identify only non-transpo-
sable-element-related genes. A total of 37,544 non-transposable-
element protein-coding sequences were predicted, resulting in a
density of one gene per 9.9 kb (Supplementary Tables 4 and 5). As
the ability to identify unannotated and transposable-element-related
genes improves, the true protein-coding gene number in rice will
doubtless be revised.
Full-length complementary DNA sequences are available for rice
27
,
and provide a powerful resource for improving gene model structure
derived from ab initio gene finders
28
. Of the 37,544 non-transposa-
ble-element-related FGENESH models, 17,016 could be supported
by a total of 25,636 full-length cDNAs (Supplementary Table 6).
A total of 22,840 (61%) genes had a high identity match with a rice
ESTor full-length cDNA. On average, about 10.7 ESTsequences were
present for each expressed rice gene. A total of 2,927 genes aligned
well with ESTs from other cereal species, and 330 of these genes
matched only with a non-rice cereal EST (Supplementary Fig. 1).
Except for the short arms of chromosomes 4, 9 and 10, which are
known to be highly heterochromatic, the density of expressed genes
is greater on the d istal portions of the chromosome arms
compared with the regions around the centromeres (Supplementary
Fig. 2).
A total of 19,675 proteins had matches with entries in the Swiss-
Prot database; of these, 4,500 had no expression support. Domain
searches revealed a minimum of one motif or domain present in 63%
of the predicted proteins, with a total of 3,328 different domains
present in the predicted rice proteome. The five most abundant
domains were associated with protein kinases (Supplementary
Table 7). Fifty-one per cent of the predicted proteins could be
associated with a biological process (Supplementary Fig. 3a), with
metabolism (29.1%) and cellular physiological processes (11.9%)
representing the two most abundant classes.
Approximately 71% (26,837) of the predicted rice proteins have a
homologue in the Arabidopsis proteome (Supplementary Fig. 4). In a
reciprocal search, 89.8% (26,004) of the proteins from the Arabi-
dopsis genome have a homologue in the rice proteome. Of the 23,170
rice genes with rice EST, cereal EST, or full-length cDNA support,
20,311 (88%) have a homologue in Arabidopsis. Fewer putative
homologues were found in other model species: 38.1% in Drosophila,
40.8% in human, 36.5% in Caenorhabditis elegans, 30.2% in yeast,
17.6% in Synechocystis and 10.2% in Escherichia coli.
There are profound differences in plant architecture and biochem-
istry between monocotyledonous and dicotyledonous angiosperms.
Only 2,859 rice genes with evidence of transcription lack homologues
in the Arabidopsis genome. We investigated these to learn what
functions they en coded. The vast majority had no matches, or
most closely matched unknown or hypothetical proteins. The grasses
have a class of seed storage proteins called prolamins that is not found
in dicots. There are also families of hormone response proteins and
defence proteins, such as proteinase inhibitors, chitinases, patho-
genesis-related proteins and seed allergens, many of which are
tandemly repeated (Supplementary Table 8). Nevertheless, with a
large number of proteins of unknown function, the most interesting
ARTICLES NATURE|Vol 436|11 August 2005
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© 2005 Nature Publishing Group
differences between the genome content of these two groups of
angiosperms remain to be discovered.
Tos17 is an endogenous copia-like retrotransposon in rice that is
inactive under normal growth conditions. In t issue culture, it
becomes activated, transposes and is stably inherite d when the
plant is regenerated
29
. There are only two copies of Tos17 in the
rice cultivar Nipponbare. These features, together with its preferen-
tial insertion into gene-rich regions, make Tos17 uniquely suitable for
the functional analysis of rice genes by gene disruption. About 50,000
Tos17-insertion lines carrying 500,000 insertions have been pro-
duced
30
. A total of 11,487 target loci were mapped on the 12
pseudomolecules (Supplementary Fig. 5), with at least one insertion
detected in 3,243 genes. The density of Tos17 insertions is higher in
euchromatic regions of the genome
30
, in contrast to the distribution
of high-copy retrotransposons, which are more frequently found in
pericentromeric regions. A similar target site preference has been
reported for T-DNA insertions in Arabidopsis
31
.
Tandem gene families
One surprising outcome of the Arabidopsis genome analysis was the
large percentage (17%) of genes arranged in tandem repeats
32
. When
performing a similar analysis with rice, the percentage was compar-
able (14%). However, manual curation on rice chromosome 10
showed one gene family encoding a glycine-rich protein with 27
copies and one encoding a TRAF/BTB domain protein with 48
copies
33
. These tandemly repeated f amilies are interrupted wit h
other genes and are not included in strictly defined tandem repeats.
We therefore screened for all tandemly arranged genes in 5-Mb
intervals. Using these criteria, 29% of the genes (10,837) are ampli-
fied at least once in tandem, and 153 rice gene arrays contained 10–
134 members (Supplementary Fig. 6). Sixty five per cent of the
tandem arrays with over 27 members, and 33% of all the arrays with
over 10 members, contain protein kinase domains (Supplementary
Table 9).
Non-coding RNA genes
The nucleolar organizer, consisting of 17S–5.8S–25S ribosomal DNA
coding units, is found at the telomeric end of the short arm of
chromosome 9 (ref. 34) in O. sativa ssp. japonica, and is estimated to
comprise 7 Mb (ref. 35). A second 17S–5.8S–25S rDNA locus is
found at the end of the short arm of chromosome 10 in O. sativa ssp.
indica
34
. A single 5S cluster is present on the short arm of chromo-
some 11 in the v icinit y of the centromere
36
, and encompasses
0.25 Mb.
A total of 763 transfer RNA genes, including 14 tRNA pseudogenes
were detected in the 12 pseudomolecules. In comparison, a total of
611 tRNA genes were detected in Arabidopsis
32
. Supplementary Fig. 7
shows the distribution of these tRNA genes in each chromosome.
Chromosome 4 has a single tRNA cluster
6
, and chromosome 10 has
two large clusters derived from inserted chloroplast DNA
7
. Except for
regions of intermediate density on chromosomes 1, 2, 8 and 12, there
seem to be no other large clusters.
MicroRNAs (miRNAs), a class of eukaryotic non-coding RNAs,
are believed to regulate gene expression by interacting with the target
messenger RNA
37
. miRNAs have been predicted from Arabidopsis
38
and rice
39
, and we mapped 158 miRNAs onto the rice pseudomole-
cules (Supplementary Table 10). Among other non-coding RNAs, we
identified 215 small nucleolar RNA (snoRNA) and 93 spliceosomal
RNA genes, both showing biased chromosomal distributions, in the
rice genome (Supplementary Table 11).
Organellar insertions in the nuclear genome
Mitochondria and chloroplasts originated from alpha-proteobac-
teria and cyanobacteria endosymbionts. A continuous transfer of
organellar DNA to the nucleus has resulted in the presence of
chloroplast and mitochondrial DNA inserted in the nuclear chromo-
somes. Although the endosymbionts probably contained genomes of
several Mb at the time they were internalized, the organellar genomes
diminished so that the present size of the mitochondrial genome is
less than 600 kb, and that of the chloroplast is only 150 kb. Homology
search es detected 421–453 chloroplast insertions and 909–1,191
mitochondrial insertions, depending upon the stringency adopted
(Supplementary Fig. 8 and Supplementary Table 12). Thus, chlor-
oplast and mitochondrial insertions contribute 0.20–0.24% and
0.18–0.19% of the nuclear genome of rice, respectively, and corre-
spond to 5.3 chloroplast and 1.3 mitochondrial genome equivalents.
The distribution of chloroplast and mitochondrial insertions over
the 12 chromosomes indicates that mitochondrial and chloroplast
transfers occurred independently. Two chromosomes harbour more
insertions than the others (Supplementary Fig. 8 and Supplementary
Table 12), with chromosome 12 containing nearly 1% mitochondrial
DNA and chromosome 10 containing approximately 0.8% chlor-
Table 1 | Classification and distribution of sequenced PAC and BAC clones* on the 12 rice chromosomes
Chr Sequencing laboratory† PAC BAC OSJNBa/b OJ OSJNO Others‡ Total§ Pseudomolecule (bp) N-average lengthk (bp) Accession no.
1 RGP, KRGRP 251 77 42 23 4 0 397 43,260,640 9,688,259 AP008207
2 RGP, JIC 117 16 80 142 4 0 359 35,954,074 7,793,366 AP008208
3 ACWW, TIGR 1 8 263 47 1 10 330 36,189,985 5,196,992 AP008209
4 NCGR 2 7 275 7 0 0 291 35,489,479 1,427,419 AP008210
5 ASPGC 67 11 113 87 0 0 278 29,733,216 3,086,418 AP008211
6 RGP 169 20 78 14 0 0 281 30,731,386 8,669,608 AP008212
7 RGP 102 19 68 97 0 0 286 29,643,843 14,923,781 AP008213
8 RGP 113 23 56 83 2 0 277 28,434,680 14,872,702 AP008214
9 RGP, KRGRP, BIOTEC, BRIGI 72 24 72 50 5 0 223 22,692,709 5,219,517 AP008215
10 ACWW, TIGR, PGIR 1 5 172 6 0 21 205 22,683,701 2,124,647 AP008216
11 ACWW, TIGR, IIRGS, PGIR, Genoscope 10 6 236 3 2 1 258 28,357,783 1,087,274 AP008217
12 Genoscope 2 6 179 79 0 2 268 27,561,960 7,600,514 AP008218
Total 907 222 1634 638 18 34 3453 370,733,456 6,928,182
Chr, chromosome.
*PAC, Rice Genome Research Program PAC; BAC, Rice Genome Research Program BAC; OSJNBa/b, Clemson University Genomics Institute BAC; OJ, Monsanto BAC; OSJNO, Arizona
Genomics Institute fosmid (http://www.genome.arizona.edu/orders/direct.html?library ¼ OSJNOa); Others, artificial gap-filling clones designated as OSJNA and OJA.
†ACWW (Arizona Genomics Institute, Cold Spring Harbor Laboratory, Washington University Genome Sequencing Center, University of Wisconcin) Rice Genome Sequencing Consortium;
ASPGC, Academia Sinica Plant Genome Center; BIOTEC, National Center for Genetic Engineering and Biotechnology; BRIGI, Brazilian Rice Genome Initiative; IIRGS, Indian Initiative for Rice
Genome Sequencing; JIC, John Innes Centre; KRGRP, Korea Rice Genome Research Program; NCGR, National Center for Gene Research; PGIR, Plant Genome Initiative at Rutgers; RGP, Rice
Genome Research Program; TIGR, The Institute for Genomic Research.
‡Constructs derived by joining (mostly from the clone gap regions) sequence from PCR fragments, Monsanto or Syngenta sequences and the neighbouring clone sequences.
§A total of 2,494 BAC and 907 PAC clones were used for draft and finished sequencing. Monsanto draft-sequenced BACs underlie 638 finished clones. The Syngenta draft sequence
contributed to the assemblies of 140 IRGSP clone sequences. Thirty-four sequence submissions are artificial constructs derived by joining a regional sequence (mostly from the clone gap
regions) from PCR fragments, Monsanto or Syngenta sequences with the neighbouring clone sequences. This also includes 93 clones submitted as phase 1 or phase 2 to the HTG section of
GenBank.
kN-average length: the average length of a contiguous segment (without sequence or physical gaps) containing a randomly chosen nucleotide.
NATURE|Vol 436|11 August 2005 ARTICLES
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© 2005 Nature Publishing Group
oplast DNA. It is clear that several successive transfer events have
occurred, as insertions of less than 10 kb have heterogeneous iden-
tities. The longest insertions, however, systematically show .98.5%
identity to organellar DNA (Supplementary Table 13), indicating
recent insertions for both chloroplast and mitochondrial genomes.
Transposable elements
The rice genome is populated by representatives from all known
transposon superfamilies, including elements that cannot be easily
classified into either class I or II (ref. 40). Previous estimates of the
transposon content in the rice genome range from 10 to 25% (refs 21,
40). However, the increased availability of transposon quer y
sequences and the use of profile hidden Markov models allow the
identification of more divergent elements
41
and indicate that the
transposon content of the O. sativa ssp. japonica genome is at least
35% (Table 3). Chromosomes 8 and 12 have the highest transposon
content (38.0% and 38.3%, respectively), and chromosomes 1
(31.0%), 2 (29.8%) and 3 (29.0%) have the lowest proportion of
transposons. Conversely, elements belonging to the IS5/Tourist and
IS630/Tc1/mariner superfamilies, which are generally correlated with
gene density, are prevalent on the first three chromosomes and least
frequent on chromosomes 4 and 12.
Class II elements, characterized by terminal inverted-repeats and
including the hAT, CACTA, IS256/Mutator,IS5/Tourist, and IS630/
Tc1/mariner superfamilies, outnumber class I elements, which
includ e long termi nal-repeat (LTR) retrotransposons (Ty1/copi a,
Ty3/gypsy and TRIM) and non-LTR retrotransposons (LINEs and
SINEs, or long- and short-interspersed nucleotide elements, respect-
ively), by more than twofold (Table 3). However, the nucleotide
contribution of class I is greater than that of class II, due mostly to the
large size of LTR retrotransposons and the small size of IS5/Tourist
and IS630/Tc1/mariner elements. The inverse is the case for maize,
for which class I elements outnumber class II elements
42
. Given their
larger sizes, differential amplification of LTR elements in maiz e
compared with rice is consistent w ith the genomic expansion
found between orthologous regions of rice and maize
15,33
.
Most class I elements are concentrated in gene-poor, heterochro-
matic regions such as the centromeric and pericentromeric regions
(Supplementary Table 14). In contrast, members of some transposon
superfamilies, including IS5/Tourist,IS630/Tc1/mariner and LINEs,
have a significant positive correlation with both recombination rate
and gene density. There is an effect o f average element length
associated with these patterns: short elements generally show a
positive correlation with recombination rate and gene density, and
are under-represented in the centromere regions, whereas larger
elements have higher centromeric and pericentromeric abundance.
Intraspecific sequence polymorphism
Map-based cloning to identify genes that are associated with agro-
nomic traits is dependent on having a high frequency of polymorphic
markers to order recombination events. In rice, most of the segregat-
ing populations are generated from crosses between the two major
subspecies of cultivated rice, Oryza sativa ssp. japonica and O. sativa
ssp. indica. Although several studies on the polymorphisms detected
between japonica and indica subspecies have been reported
6,43,44
, the
analysis reported here uses an approach that ensures comparison of
orthologous sequences. O. sativa ssp. indica cv. Kasalath and O. sativa
ssp. japonica cv. Nipponbare are the parents of the most densely
mapped rice population
16
. BAC-end sequences were obtained from a
Kasalath BAC library of 47,194 clones. Only high quality, single-copy
sequences were mapped to the Nipponbare pseudomolecules, and
only paired inverted sequences that mapped within 200 kb were
considered. A total of 26,632 paired Kasalath BAC-end sequences
were mapped to the 12 rice pseudomolecules (Supplementary
Table 15). Kasalath BAC clones spanned 308 Mb or 79% of the
Nipponbare genome. Sequence alignments with a PHRED quality
value of 30 covered 12,319,100 bp (3%) of the total rice genome. A
total of 80,127 sites differed in the corresponding regions in Nip-
ponbare and Kasalath. The frequency of SNPs varied between
chromosomes (0.53–0.78%). Insertions and deletions w ere also
detected. The ratio of small insertion/deletion site nucleotides (1–
14 bases) against the alignment length (0.20–0.27%) was similar
among the different chromosomes, and there was no preference for
the direction of insertions or deletions. The main patterns of base
substitutions obser ved between Nipponbare and Kasalath are shown
in Supplementary Table 16. Transitions (70%) were the most
prominent substitutions; this is a substantially higher fraction than
found between Arabidopsis ecotypes Columbia and Landsberg erecta
32
.
Class 1 simple sequence repeats in the rice genome
Class 1 simple sequence repeats (SSRs) are perfect repeats .20
nucleotides in length
45
that behave as hypervariable loci, providing
a rich source of markers for use in genetics and breeding. A total of
18,828 Class 1 di, tri and tetra-nucleotide SSRs, representing 47
distinctive motif families, were identified and annotated on the rice
genome (Supplementary Fig. 9). Supplementary Table 17 provides
information about the physical positions of all Class 1 SSRs in
relation to widely used restriction-fragment length polymorphisms
(RFLPs)
16,46
and previously published SSRs
45
. There was an average of
51 hypervariable SSRs per Mb, with the highest density of markers
occurring on chromosome 3 (55.8 SSR Mb
21
) and the lowest occur-
ring on chromosome 4 (41.0 SSR Mb
21
). A summary of information
about the Class 1 SSRs identified in the rice pseudomolecules appears
Table 2 | Size of each chromosome based on sequence data and estimated gaps
Chr Sequenced bases (bp) Gaps on arm regions Telomeric gaps* (Mb) Centromeric gap† (Mb) rDNA‡ (Mb) Total (Mb) Coverage§ (%) Coveragek (%)
No. Length (Mb)
1 43,260,640 5 0.33 0.06 1.40 45.05 99.1 96.0
2 35,954,074 3 0.10 0.01 0.72 36.78 99.7 97.7
3 36,189,985 4 0.96 0.04 0.18 37.37 97.3 96.8
4 35,489,479 3 0.46 0.20 36.15 98.7 98.2
5 29,733,216 6 0.22 0.05 30.00 99.3 99.1
6 30,731,386 1 0.02 0.03 0.82 31.60 99.8 97.2
7 29,643,843 1 0.31 0.01 0.32 30.28 98.9 97.9
8 28,434,680 1 0.09 0.05 28.57 99.7 99.5
9 22,692,709 4 0.13 0.14 0.62 6.95 30.53 98.8 74.3
10 22,683,701 4 0.68 0.13 0.47 23.96 96.6 94.7
11 28,357,783 4 0.21 0.04 1.90 0.25 30.76 99.1 92.2
12 27,561,960 0 0.00 0.05 0.16 27.77 99.8 99.2
All 370,733,456 36 3.51 0.81 6.59 7.20 388.82 98.9 95.3
*Estimated length including the telomeres, calculated with the average value of 3.2 kb for each chromosome
24
.
†Estimated length of centromere-specific CentO repeats on each chromosome
26
.
‡Represents the estimated length of the17S–5.8S–25S rDNA cluster on Chr 9 (ref. 35) and the 5S cluster on Chr 11 (ref. 24).
§Coverage of the pseudomolecules for the euchromatic regions in each chromosome.
kCoverage of the pseudomolecules over the full length of each chromosome.
ARTICLES NATURE|Vol 436|11 August 2005
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© 2005 Nature Publishing Group
in Supplementary Table 18. Several thousand of these SSRs have
already been shown to amplify well and be polymorphic in a panel of
diverse cultivars
45
, and thus are of immediate use for genetic analysis.
Genome-wide comparison of draft versus finished sequences
Two whole-genome shotgun assemblies of draft-quality rice
sequence have been published
23,47
, and reassemblies of both have
just appeared
48
. One of these is an assembly of 6.28 £ coverage of O.
sativa ssp. indica cv. 93-11. The second sequence is a , 6 £ coverage
of O. sativa ssp. japonica cv. Nipponbare
23,48
. These assembl ies
predict genome sizes of 433 Mb for japonica and 466 Mb for. indica,
which differ from our estimation of a 389 Mb japonica genome.
Contigs from the whole-genome shotgun assembly of 93-11 and
Nipponbare
48
were aligned with the IRGSP pseudomolecules. Non-
redundant coverage of the pseudomolecules by the indica assembly
varied from 78% for chromosome 3 to 59% for chromosome 12, with
an overall coverage of 69% (Supplementary Table 19). When genes
supported by full-length cDNA coverage were aligned to the covered
regions, we found that 68.3% were completely covered by the indica
sequences. The average size of the indica contigs is 8.2 kb, so it is not
surprising that many did not completely cover the gene models
defined here. The coverage of the Nipponbare whole-genome shot-
gun assembly varied from 68–82%, with an overall coverage of 78%
of the genome, and 75.3% of the full-length cDNAs supported gene
models.
We undertook a detailed comparison of the first Mb of these
assemblies on 1S (the short arm of chromosome 1) with the IRGSP
chromosome 1 (Supplementary Fig . 10 and Supplementary Table
20). The num bers from this comparison agree with the whole-
genome comparison described above. In addition, we observed
that a substantial portion of the contigs from each assembly were
non-homologous, m isaligned or provided duplicate coverage.
Indeed, the whole-genome shotgun assembly d iffered by 0.05%
base-pair mismatches for the two aligned regions from the same
Nipponbare cultivar. The two assemblies were further examined for
the presence of the CentO sequence (Supplementary Table 21). Sixty-
eight per cent of the copies observed in the 93-11 assembly and 32%
of the CentO-containing contigs in the whole-genome shotgun
Nipponbare assembly were found outside the centromeric regions.
In contrast, the CentO repeats were restricted to the centromeric
regions in the IRGSP pseudomolecules. It is unlikely that there are
dispersed centromeres in indica rice; misassembly of the whole-
genome shotgun sequences is a more likely explanation for dispersed
CentO repeats. These observations indicate that the draft sequences,
although providing a useful preliminary survey of the genome, might
not be adequate for gene annotation, functional genomics or the
identification of genes underlying agronomic traits.
Concluding remarks
The attainment of a complete and accurate map-based sequence for
rice is compelling. We now have a blueprint for all of the rice
chromosomes. We know, with a high level o f confidence, the
distribution and location of all the main components
—
the genes,
repetitive sequences and centromeres. Substantial portions of the
map-based sequence have been in public databases for some time,
and the availability of provisional rice pseudomolecules based on this
sequence has provided the scientific community with numerous
opportunities to evaluate the genome, as indicated by the number of
publications in rice biology and genetics over the past few years.
Furthermore, the wealth of SNP and SSR information provided here
Figure 1 | Maps of the twelve rice chromosomes. For each chromosome
(Chr 1–12), the genetic map is shown on the left and the PAC/BAC contigs
on the right. The position of markers flanking the PAC/BAC contigs (green)
is indicated on the genetic map. Physical gaps are shown in white and the
nucleolar organizer on chromosome 9 is represented with a dotted green
line. Constrictions in the genetic maps and arrowheads to the right of
physical maps represent the chromosomal positions of centromeres for
which rice CentO satellites are sequenced. The maps are scaled to genetic
distances in centimorgans (cM) and the physical maps are depicted in
relative physical lengths. Please refer to Table 2 for estimated lengths of the
chromosomes.
NATURE|Vol 436|11 August 2005 ARTICLES
797
© 2005 Nature Publishing Group
and elsewhere will accelerate marker-assisted breeding and positional
cloning, facilitating advances in rice improvement.
The syntenic relationships between rice and the cereal grasses have
long been recognized
4
. Comparing genome organization, genes and
intergenic regions between cereal species will permit identification of
regions that are highly conserved or rapidly evolving. Such regions
are expected to yield crucial insights into genome evolution, specia-
tion and domestication.
METHODS
Physical map and sequencing. Nine genomic libraries from Oryza sativa ssp.
japonica cultivar Nipponbare were used to establish the physical map of rice
chromosomes by polymerase chain reaction (PCR) screening
19
, fingerprinting
20
and end-sequencing
21
. The PAC, BAC and fosmid clones on the physical map
were subjected to random shearing and shotgun sequencing to tenfold redun-
dancy, using both universal primers and the dye-terminator or dye-primer
methods. The sequences were assembled using PHRED (http://www.genome.-
washington.edu/UWGC/analysistools/Phred.cfm) and PHRAP (http://www.ge-
nome.washington.edu/UWGC/analysistools/Phrap.cfm) software packages or
using the TIGR Assembler (http://www.tigr.org/software/assembler/).
Sequence gaps were resolved by full sequencing of gap-bridge clones, PCR
fragments or direct sequencing of BACs. Sequence ambiguities (indicated by
PHRAP scores less than 30) were resolved by confirming the sequence data using
alternative chemistries or different polymerases. We empirically determined that
a PHRAP score of 30 or above exceeds the standard of less than one error in
10,000 bp. BAC and PAC assemblies were tested for accuracy by comparing
computationally derived fingerprint patterns with experimentally determined
patterns of restriction enzyme digests. Sequence quality was also evaluated by
comparing independently obtained overlapping sequences.
Small physical gaps were filled by long-range PCR. Remaining physical gaps
were measured using fluorescence in situ hybridization analysis. We used the
length of CentO arrays
26
to estimate the size of each of the remaining centromere
gaps.
Annotation and bioinformatics. Gene models were predicted using FGENESH
(http://www.softberry.com/berry.phtml?topic ¼ fgenesh) using the monocot
trained matrix on the native and repeat-masked pseudomolecules. Gene models
with incomplete open reading frames, those encoding proteins of less than 50
amino acids, or those corresponding to organellar DNA were omitted from the
final set. The coordinates of transposable elements, excluding MITEs (miniature
inverted-repeat transposable elements), were used to mask the pseudomolecules.
Conserved domain/motif searches and association with gene ontologies were
performed using InterproScan (http://www.ebi.ac.uk/InterProScan/) in combi-
nation with the Interpro2Go program. For biological processes, the number of
detected domains was re-calculated as number of non-redundant proteins.
The predicted rice proteome was searched using BLASTP against the
proteomes of several model species for which a complete genome sequence
and deduced protein set was available. Each rice chromosome was searched
against the TIGR rice gene index (http://www.tigr.org/tdb/tgi/ogi/) and against
gene index entries that aligned to gene models corresponding to expressed genes.
In addition, five cereal gene indices (http://www.tigr.org/tdb/tgi/) were searched
against the rice chromosomes, and gene index matches were recorded. We
searched the Oryza sativa ssp. japonica cv. Nipponbare collection of full-length
cDNAs (ftp://cdna01.dna.affrc.go.jp/pub/data/), after first removing the trans-
posable-element-related sequences, against the FGENESH models.
Gene models with rice full-length cDNA, EST or cereal EST matches but
without identifiable homologues in the Arabidopsis genome were searched for
conserved domains/motifs using InterproScan, and for homologues in the
Swiss-Prot database (http://us.expasy.org/sprot/) using BLASTP. All proteins
with positive blast matches were further compared with the nr database (http://
www.ncbi.nlm.nih.gov/blast/html/blastcgihelp.html#protein_databases), using
BLASTP to eliminate truncated proteins and those with matches to other dicots.
Tandem gene families. The rice genome was subjected to a BLASTP search as
previously described
32
. The search was also performed by permitting more than
one unrelated gene within the arrays, and the limit of the search was set to 5-Mb
intervals to exclude large chromosomal duplications.
Non-coding RNAs. Transfer-RNA genes were detected by the program tRNA-
scan SE (http://www.genetics.wustl.edu/eddy/tRNAscan-SE/). The miRNA reg-
istry in the Rfam database (http://www.sanger.ac.uk/Software/Rfam/) was used
as a reference database for miRNAs. In addition, experimentally validated
miRNAs of other species, excluding Arabidopsis miRNAs, were used for BLASTN
queries against the pseudomolecules. Spliceosomal and snoRNAs were retrieved
from the Rfam database and used for queries. BLASTN was used to find the
location of snoRNAs and spliceosomal RNAs in the pseudomolecules.
Organellar insertions. Oryza sativa ssp. japonica Nip ponbare chloroplast
(GenBank NC_001320) and mitochondrial (GenBank BA000029) sequences
were aligned with the pseudomolecules using BLASTN and MUMmer
49
.
Transposable elements. The TIGR Oryza Repeat Database, together with other
published and unpublished rice transposable element sequences, was used to
create RTEdb (a rice transposable element database)
50
and determine transpo-
sable element coordinates on the rice pseudomolecules. In the case of hAT, IS256/
Mutator,IS5/Tourist and IS630/Tc1/mariner elements, family-specific profile
hidden Markov models were applied using HMMER
41
(http://hmmer.wustl.edu/).
The remaining superfamilies were annotated using RepeatMasker (http://
www.repeatmasker.org/).
Tos17 insertions. Flanking sequ ences of trans posed copies of 6,278 Tos17
insertion lines were isolated by modified thermal asymmetric interlaced
(TAIL)-PCR and suppression PCR, and screened against the pseudomolecule
sequences.
SNP discovery. BAC clones from an O. sativa ssp. indica var. Kasalath BAC
library were end-sequenced. Sequence reads were omitted if they contained more
than 50% nucleotides of low quality or high similarity to known repeats. The
remaining sequences were subjected to BLASTN analysis against the pseudo-
molecule s. Gaps within the alignments were classified as small insertions/
deletions.
SSR loci. The Simple Sequence Repeat Identification Tool (http://www.gramene.
org/) was used to identify simple sequence repeat motifs, and the physical
position of all Class 1 SSRs was recorded. The copy number of SSR markers was
estimated using electronic (e)-PCR to determine the number of independent hits
of primer pairs on the pseudomolecules.
Whole-genome shotgun assembly analysis. Contigs from the BGI 6.28 £
whole genome assembly of O. sativa ssp. indica 93-11 (GenBank/DDBJ/EMBL
accession number AAAA02000001–AAAA02050231) and the Syngenta 6 £
whole gen ome a ssembly of O. sa ti va ssp. japonica cv. Nipponbare
(AACV01000001–AACV01035047; ref. 48) were aligned with the pseudomole-
cules using MUMmer
49
. The number of IRGSP Nipponbare full-length cDNA-
supported gene models completely covered by the aligned contigs was tabulated.
The 155-bp CentO consensus sequence was used for BLAST analysis against the
93-11 and Nipponbare whole-genome shotgun contigs, and the coordinates of
the positive hits recorded. Locations of centromeres for each indica chromosome
were obtained with the CentO sequence positions on the IRGSP pseudomolecule
of the corresponding chromosome. A detailed comparison of the BGI-assembled
and -mapped Syngenta contigs (AACV01000001–AACV01000070) and the 93-
11 contigs (AAAA02000001–AAAA02000093) was obtained by BLAST analysis
against the IRGSP chromosome 1 pseudomolecule.
Detailed procedures for the analyses described above can be found in the
Supplementary Information.
Received 29 December 2004; accepted 25 May 2005.
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Supplementary Information is linked to the online version of the paper at
www.nature.com/nature.
Acknowledgements Work at the RGP was supported by the Ministry of
Agriculture, Forestry and Fisheries of Japan. Work at TIGR was supported by
grants to C.R.B. from the USDA Cooperative State Research, Education and
Extension Service–National Research Initiative, the National Science Foundation
and the US Department of Energy. Work at the NCGR was supported by the
Chinese Ministry of Science and Technology, the Chinese Academy of Sciences,
the Shanghai Municipal Commission of Science and Technology, and the
National Natural Science Foundati on of China. Work at Genoscope was
supported by le Ministe
`
re de la Recherche, France. Funding for the work at the
AGI and AGCoL was provided by grants to R.A.W. and C.S. from the USDA
Cooperative State Research, Education and Extension Service–National Research
Initiative, the National Science Foundation, the US Department of Energy and
the Rockefeller Foundation. Work at CSHL was supported by grants from the
USDA Cooperative State Research, Education and Extension Service–National
Research Initiative and from the National Science Foundation. Work at the
ASPGC was supported by Academia Sinica, National Science Council, Council of
Agriculture, and Institute of Botany, Academia Sinica. The IIRGS acknowledges
the Department of Biotechnology, Government of India, for financial assistance
and the Indian Council of Agricultural Research, New Delhi, for support. Work at
Rice Gene Discovery was supported by BIOTECH and the Princess Sirindhorn’s
Plant Germplasm Conservation Initiative Program. Work at PGIR was supported
by Rutgers University. The BRIGI was supported by Coordenac¸a
˜
ode
Aperfeic¸oamento de Pessoal de Nı
´
vel Superior (CAPES), Conselho Nacional de
Desenvolvimento Cientı
´
fico e Tecnolo
´
gico (CNPq), Financiadora de Estudos e
Projetos - Ministe
´
rio de Cie
ˆ
ncia e Tecnologia (FINEP-MCT), Fundac¸a
˜
ode
Amparo a Pesquisa do Rio Grande do Sul (FAPERGS) and Universidade Federal
de Pelotas (UFPel). Work at McGill and York Universities was supported by the
National Science and Engineering Research Council of Canada and the Canadian
International Development Agency. Funding for H.H. at the National Institute of
Agrobiological Sciences was from the Ministry of Agriculture, Forestry , and
Fisheries of Japan, and the Program for Promotion of Basic Research Activities
for Innovative Biosciences. Funding at Brookhaven National Laboratory was from
The Rockefeller Foundation and the Office of Basic Energy Science of the United
States Department of Energy. We would like to thank G. Barry and S. Goff for
their help in negotiating agreements that permitted the sharing of ma terials and
sequence with the IRGSP. We also acknowledge the work of G. Barry, S. Goff
and their colleagues in facilitating the transfer of sequence information and
supporting data.
Author Information The genomic sequence is available under accession
numbers AP008207–AP008218 in international databases (DDBJ, GenBank and
EMBL). Reprints and permissions information is available at npg.nature.com/
reprintsandpermissions. The authors declare no competing financial interests.
Correspondence and requests for materials should be addressed to Takuji
Sasaki (tsasaki@nias.affrc.go.jp).
NATURE|Vol 436|11 August 2005 ARTICLES
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© 2005 Nature Publishing Group
International Rice Genome Sequencing Project (Participants are arranged by area of contribution and then by ins titution.)
Physical Maps and Sequencing: Rice Genome Research Program (RGP) Takashi Matsumoto
1
, Jianzhong Wu
1
, Hiroyuki Kanamori
1
, Yuichi
Katayose
1
, Masaki Fujisawa
1
, Nobukazu Namiki
1
, Hiroshi Mizuno
1
, Kimiko Yamamoto
1
, Baltazar A. Antonio
1
, Tomoya Baba
1
, Katsumi Sakata
1
,
Yoshiaki Nagamura
1
, Hiroyoshi Aoki
1
, Koji Arikawa
1
, Kohei Arita
1
, Takahito Bito
1
, Yoshino Chiden
1
, Nahoko Fujitsuka
1
, Rie Fukunaka
1
, Masao
Hamada
1
, Chizuko Harada
1
, Akiko Hayashi
1
, Saori Hijishita
1
, Mikiko Honda
1
, Satomi Hosokawa
1
, Yoko Ichikawa
1
, Atsuko Idonuma
1
, Masumi
Iijima
1
, Michiko Ikeda
1
, Maiko Ikeno
1
, Kazue Ito
1
, Sachie Ito
1
, Tomoko Ito
1
, Yuichi Ito
1
, Yukiyo Ito
1
, Aki Iwabuchi
1
, Kozue Kamiya
1
, Wataru
Karasawa
1
, Kanako Kurita
1
, Satoshi Katagiri
1
, Ari Kikuta
1
, Harumi Kobayashi
1
, Noriko Kobayashi
1
, Kayo Machita
1
, Tomoko Maehara
1
,
Masatoshi Masukawa
1
, Tatsumi Mizubayashi
1
, Yoshiyuki Mukai
1
, Hideki Nagasaki
1
, Yuko Nagata
1
, Shinji Naito
1
, Marina Nakashima
1
, Yuko
Nakama
1
, Yumi Nakamichi
1
, Mari Nakamura
1
, Ayano Meguro
1
, Manami Negishi
1
, Isamu Ohta
1
, Tomoya Ohta
1
, Masako Okamoto
1
, Nozomi
Ono
1
, Shoko Saji
1
, Miyuki Sakaguchi
1
, Kumiko Sakai
1
, Michie Shibata
1
, Takanori Shimokawa
1
, Jianyu Song
1
, Yuka Takazaki
1
, Kimihiro
Terasawa
1
, Mika Tsugane
1
, Kumiko Tsuji
1
, Shigenori Ueda
1
, Kazunori Waki
1
, Harumi Yamagata
1
, Mayu Yamamoto
1
, Shinichi Yamamoto
1
,
Hiroko Yamane
1
, Shoji Yoshiki
1
, Rie Yoshihara
1
, Kazuko Yukawa
1
, Huisun Zhong
1
, Masahiro Yano
1
, Takuji Sasaki (Principal Investigator)
1
;
The Institute for Genomic Research (TIGR) Qiaoping Yuan
2
, Shu Ouyang
2
, Jia Liu
2
, Kristine M. Jones
2
, Kristen Gansberger
2
, Kelly Moffat
2
,
Jessica Hill
2
, Jayati Bera
2
, Douglas Fadrosh
2
, Shaohua Jin
2
, Shivani Johri
2
, Mary Kim
2
, Larry Overton
2
, Matthew Reardon
2
, Tamara Tsitrin
2
,
Hue Vuong
2
, Bruce Weaver
2
, Anne Ciecko
2
, Luke Tallon
2
, Jacqueline Jackson
2
, Grace Pai
2
, Susan Van Aken
2
, Terry Utterback
2
, Steve
Reidmuller
2
, Tamara Feldblyum
2
, Joseph Hsiao
2
, Victoria Zismann
2
, Stacey Iobst
2
, Aymeric R. de Vazeille
2
, C. Robin Buell (Principal
Investigator)
2
; National Center for Gene Research Chinese Academy of Sciences (NCGR) Kai Ying
3
, Ying Li
3
, Tingting Lu
3
, Yuchen
Huang
3
, Qiang Zhao
3
, Qi Feng
3
, Lei Zhang
3
, Jingjie Zhu
3
, Qijun Weng
3
, Jie Mu
3
, Yiqi Lu
3
, Danlin Fan
3
, Yilei Liu
3
, Jianping Guan
3
, Yujun
Zhang
3
, Shuliang Yu
3
, Xiaohui Liu
3
, Yu Zhang
3
, Guofan Hong
3
, Bin Han (Principal Investigator)
3
; Genoscope Nathalie Choisne
4
, Nadia
Demange
4
, Gisela Orjeda
4
, Sylvie Samain
4
, Laurence Cattolico
4
, Eric Pelletier
4
, Arnaud Couloux
4
, Beatrice Segurens
4
, Patrick Wincker
4
,
Angelique D’Hont
5
, Claude Scarpelli
4
, Jean Weissenbach
4
, Marcel Salanoubat
4
, Francis Quetier (Principal Investigator)
4
; Arizona
Genomics Institute (AGI) and Arizona Genomics Computational Laboratory (AGCol) Yeisoo Yu
6
, Hye Ran Kim
6
, Teri Rambo
6
, Jennifer
Currie
6
, Kristi Collura
6
, Meizhong Luo
6
, Tae-Jin Yang
6
, Jetty S. S. Ammiraju
6
, Friedrich Engler
6
, Carol Soderlund
6
, Rod A. Wing (Principal
Investigator)
6
; Cold Spring Harbor Laboratory (CSHL) Lance E. Palmer
7
, Melissa de la Bastide
7
, Lori Spiegel
7
, Lidia Nascimento
7
, Theresa
Zutavern
7
, Andrew O’Shaughnessy
7
, Sujit Dike
7
, Neilay Dedhia
7
, Raymond Preston
7
, Vivekanand Balija
7
, W. Richard McCombie (Principal
Investigator)
7
; Academia Sinica Plant Genome Center (ASPGC) Teh-Yuan Chow
8
, Hong-Hwa Chen
9
, Mei-Chu Chung
8
, Ching-San
Chen
8
, Jei-Fu Shaw
8
, Hong-Pang Wu
8
, Kwang-Jen Hsiao
10
, Ya-Ting Chao
8
, Mu-kuei Chu
8
, Chia-Hsiung Cheng
8
, Ai-Ling Hour
8
, Pei-Fang
Lee
8
, Shu-Jen Lin
8
, Yao-Cheng Lin
8
, John-Yu Liou
8
, Shu-Mei Liu
8
, Yue-Ie Hsing (Principal Investigator)
8
; Indian Initiative for Rice Genome
Sequencing (IIRGS), University of Delhi South Campus (UDSC) S. Raghuvanshi
11
, A. Mohanty
11
, A. K. Bharti
11,13
, A. Gaur
11
, V. Gupta
11
,D.
Kumar
11
, V. Ravi
11
, S. Vij
11
, A. Kapur
11
, Parul Khurana
11
, Paramjit Khurana
11
, J. P. Khurana
11
, A. K. Tyagi (Principal Investigator)
11
; Indian
Initiative for Rice Genome Sequencing (IIRGS), Indian Agricultural Research Institute (IARI) K. Gaikwad
12
, A. Singh
12
, V. Dalal
12
,S.
Srivastava
12
, A. Dixit
12
, A. K. Pal
12
, I. A. Ghazi
12
, M. Yadav
12
, A. Pandit
12
, A. Bhargava
12
, K. Sureshbabu
12
, K. Batra
12
, T. R. Sharma
12
,T.
Mohapatra
12
, N. K. Singh (Principal Investigator)
12
; Plant Genome Initiative at Rutgers (PGIR) Joachim Messing (Principal Investigator)
13
,
Amy Bronzino Nelson
13
, Galina Fuks
13
, Steve Kavchok
13
, Gladys Keizer
13
, Eric Linton Victor Llaca
13
, Rentao Song
13
, Bahattin Tanyolac
13
,
Steve Young
13
; Korea Rice Genome Research Program (KRGRP) Kim Ho-Il
14
, Jang Ho Hahn (Principal Investigator)
14
; National Center for
Genetic Engineering and Biotechnology (BIOTEC) G. Sangsakoo
15
, A. Vanavichit (Principal Investigator)
15
; Brazilian Rice Genome
Initiative (BRIGI) Luiz Anderson Teixeira de Mattos
16
, Paulo Dejalma Zimmer
16
, Gaspar Malone
16
, Odir Dellagostin
16
, Antonio Costa de
Oliveira (Principal Investigator)
16
; John Innes Centre (JIC) Michael Bevan
17
, Ian Bancroft
17
; Washington University School of Medicine
Genome Sequencing Center Pat Minx
18
, Holly Cordum
18
, Richard Wilson
18
; University of Wisconsin–Madison Zhukuan Cheng
19
, Weiwei
Jin
19
, Jiming Jiang
19
, Sally Ann Leong
20
Annotation and Analysis: Hisakazu Iwama
21
, Takashi Gojobori
21,22
, Takeshi Itoh
22,23
, Yoshihito Niimura
24
, Yasuyuki Fujii
25
, Takuya
Habara
25
, Hiroaki Sakai
23,25
, Yoshiharu Sato
22
, Greg Wilson
26
, Kiran Kumar
27
, Susan McCouch
26
, Nikoleta Juretic
28
, Douglas Hoen
28
,
Stephen Wright
29
, Richard Bruskiewich
30
, Thomas Bureau
28
, Akio Miyao
23
, Hirohiko Hirochika
23
, Tomotaro Nishikawa
23
, Koh-ichi
Kadowaki
23
& Masahiro Sugiura
31
Coordination: Benjamin Burr
32
Affiliations for participants:
1
National Institute of Agrobiological Sciences/Institute of the Society for Techno-innovation of Agriculture, Forestry and Fisheries, 2-1-2 Kannondai,
Tsukuba, Ibaraki 305-8602, Japan.
2
The Institute for Genomic Research, 9712 Medical Center Drive, Rockville, Maryland 20850, USA.
3
Shanghai Institutes for Biological
Sciences, Chinese Academy of Sciences (CAS), 500 Caobao Road, Shanghai 200233, China.
4
Centre National de Se
´
quenc¸age, INRA-URGV, and CNRS UMR-8030, 2, rue Gaston
Cre
´
mieux, CP 5706, 91057 EVRY Cedex, France.
5
UMR PIA, Cirad-Amis, TA40-03 avenue Agropolis, 34398 Montpellier Cedex 05, France.
6
Department of Plant Sciences, BIO5
Institute, The University of Arizona, Tucson, Arizona 85721, USA.
7
Cold Spring Harbor Labora tory, Cold Spring Harbor, New York 11723, USA.
8
Institute of Botany, Academia
Sinica, 128, Sec. 2, Yen-Chiu-Yuan Rd, Nankang, Taipei 11529, Taiwan.
9
National Cheng Kung University, No. 1, Ta-Hsueh Road, Tainan 701, Taiwan.
10
National Yang-Ming
University, 155, Sec. 2, Li-Nong St, Peitou, Taipei 112, Taiwan.
11
Department of Plant Molecular Biology, University of Delhi South Campus, New Delhi 110021, India.
12
National
Research Centre on Plant Biotechnology, Indian Agricultural Research Institute, New Delhi 110012, India.
13
Waksman Institute, Rutgers University, Piscataway, New Jersey
08854, USA.
14
National Institute of Agricultural Science and Technology, RDA, Suwon, 441-707 Republic of Korea.
15
Rice Gene Discovery Unit, Kasetsart University, Nakron
Pathom 73140, Thailand.
16
Centro de Genomica e Fitomelhoramento, UFPel, Pelotas, RS, l 96001-970, Brazil.
17
John Innes Centre, Norwich Research Park, Colney, Norwich NR4
7UH, UK.
18
Washington University Genome Sequencing Center, 33 33 For est Park Boulevard, St. Louis, Missouri 63108, USA.
19
University of Wisconsin, Department of
Horticulture, Madison, Wisconsin 53706, USA.
20
University of Wisconsin, Department of Plant Pathology, Madison, Wisconsin 53706, USA.
21
Center for Information Biology and
DNA Data Bank of Japan, National Institute of Genetics, Mishima 411-8540, Japan.
22
Biological Information Research Center, N ational Institute of Advanced Industrial Science
and Technology, Koto-ku, Tokyo 135-0064, Japan.
23
National Institute of Agrobiological Sciences, Tsukuba, Ibaraki 305-8602, Japan.
24
Medical Research Institute, Tokyo
Medical and Dental University, Bunkyo-ku, Tokyo 113-8510, Japan.
25
Japan Biological Information Research Center, Japan Biological Informatics Consortium, Koto-ku, Tokyo 135-
0064, Japan.
26
Plant Breeding Dept, Cornell University, Ithaca, New York 14850-1901, USA.
27
Cold Spring Harbor Laborato ry, PO Box 100, 1 Bungtown Road, Cold Spring Harbor,
New York 11724, USA.
28
Department of Biology, McGill University, 1205 Dr Penfield Avenue, Montreal, Quebec H3A 1B1, Canada.
29
Department of Biology, York University,
4700 Keele Street, Toronto, Ontario M3J 1P3, Canada.
30
Biometrics and Bioinformatics Unit, International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines.
31
Graduate School of Natural Sciences, Nagoya City University, Nagoya 467-8501, Japan.
32
Biology Department, Brookhaven National Laboratory, Upton, New York 11973, USA.
ARTICLES NATURE|Vol 436|11 August 2005
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© 2005 Nature Publishing Group
