- Open Access
The Nipponbare genome and the next-generation of rice genomics research in Japan
© Matsumoto et al. 2016
Received: 21 January 2016
Accepted: 3 July 2016
Published: 22 July 2016
The map-based genome sequence of the japonica rice cultivar Nipponbare remains to date as the only monocot genome that has been sequenced to a high-quality level. It has become the reference sequence for understanding the diversity among thousands of rice cultivars and its wild relatives as well as the major cereal crops that comprised the food source for the entire human race. This review focuses on the accomplishments in rice genomics in Japan encompassing the last 10 years which have led into deeper understanding of the genome, characterization of many agronomic traits, comprehensive analysis of the transcriptome, and the map-based cloning of many genes associated with agronomic traits.
The elucidation of the rice genome sequence is a major milestone in science as it paves the way for understanding the biology of a major cereal crop that feeds more than half of the world’s population (International Rice Genome Sequencing Project 2005). Although roughly a hundred plant genome sequences have already been published to date, the map-based sequence of Oryza sativa ssp. japonica cv. Nipponbare remains as the only monocot genome that has been sequenced to a high-quality level. It has therefore become a reference for sequencing of other cereal crops with much larger genome sizes such as maize (Schnable et al. 2009), sorghum (Paterson et al. 2009), soybean (Schmutz et al. 2010), barley (International Barley Genome Sequencing Consortium 2012), and wheat (International Wheat Genome Sequencing Consortium 2014). More importantly, the rice genome sequence has become the most powerful tool in agriculture enhancing the ability of breeders to develop new cultivars with highly desirable traits such as high yield, resistance to biotic/abiotic stress, good eating quality, and cultivars that could adapt to an ever changing cultivation environment brought about by global warming. It is expected that subsequent sequencing of a wide array of rice germplasm throughout the world will be the platform for propelling the next green revolution to increase productivity under more sustainable conditions.
Although 90 % of rice is consumed mainly in Asia, it is also a major food source in many African and South American countries. Rice is a main staple in the Japanese diet with the current average per capita consumption of about 60 kg per year. It has been cultivated both as a staple and economic crop for more than 2000 years across the country and has been integrated in many aspects of the culture as well. Thousands of cultivars have been developed as a result of crossbreeding and selection conducted by farmers and breeders to suit the specific local conditions. Therefore, the complete rice genome sequence based on the cultivar Nipponbare led to the large-scale characterization of other japonica cultivars including the widely cultivated and elite cultivar Koshihikari (Yamamoto et al. 2010) known for good eating quality.
This review will focus on the accomplishments in rice genomics in Japan encompassing the last 10 years since the completion of the rice genome sequence. There is no doubt however that a great deal of accomplishments has been achieved not only by the 10 participating countries in the international sequencing consortium but also by many rice researchers worldwide who have continuously engaged in understanding the rice biology based on the map-based Nipponbare genome sequence. In Japan, succeeding efforts in genome analysis from 2005 onwards have led to fine tuning of the genome assembly, deeper understanding of the structure of specific regions of the genome, characterization of many important traits across various cultivars, comprehensive profiling of the transcriptome, and the isolation and map-based cloning of many genes associated with agronomic traits.
Enhancing the genome assembly and annotation
The latest genome assembly was constructed as a joint effort of the Rice Annotation Project Database (RAP-DB) of the National Institute of Agrobiological Sciences (NIAS) and the Michigan State University (MSU) Rice Genome Annotation Project to update and validate the reference IRGSP Nipponbare genome sequence and provide a unified set of pseudomolecules to the rice research community (Kawahara et al. 2013). The genome assembly was revised using the rice optical map (Zhou et al. 2007) to validate the minimal tiling path. The next-generation sequencing (NGS) data obtained by re-sequencing two individual Nipponbare plants using the Illumina Genome Analyzer II/IIx and Roche 454 GS FLX were used to check sequencing errors in the revised assembly. This resulted in the identification of 4886 sequencing errors and five insertions/deletions in the 321 Mb of the assembled genome corresponding to an error rate of 0.15 per 10,000 nucleotides in the original IRGSP assembly. The revised and unified genome assembly, Os-Nipponbare-Reference-IRGSP-1.0 (IRGSP-1.0), is now used to provide a common platform for genome annotation in the RAP-DB (http://rapdb.dna.affrc.go.jp, Rice Annotation Project 2008) and the MSU rice annotation database (http://rice.plantbiology.msu.edu/cgi-bin/gbrowse/rice/).
In line with the revision of the genome assembly, the RAP-DB has been enhanced further with the mapping of 154,579 transcript sequences from the genus Oryza and other monocot species (Sakai et al. 2013). In addition, literature-based manually curated data, transcriptome data, and NGS data of major rice cultivars were also incorporated into the database. The current release of RAP-DB consists of 37,869 loci including 1626 loci that correspond to literature-based manually curated annotation data, commonly used gene names, and gene symbols. Transcription data derived from Illumina RNA-seq analysis of various tissues under normal and stress conditions (Mizuno et al. 2010; Oono et al. 2011; Kawahara et al. 2012, 2016) have been added to enhance the utility of the database in understanding transcriptional regulatory networks. Links to gene families in rice, Sorghum bicolor, Zea mays and Arabidopsis thaliana are provided to facilitate analysis of how genes are conserved and evolved among plant species. An additional feature to RAP-DB is the Short-Read Assembly Browser (S-RAB) that provides a viewer for Illumina reads of the japonica cultivar Koshihikari and indica cultivar Guangluai-4 mapped to the Nipponbare genome, showing the alignments, single nucleotide polymorphisms (SNPs), and gene functional annotations. The RAP-DB is updated on a regular basis so that it can provide researchers with the latest information on characterization of rice genome structure and function. Recent advances in DNA sequencing technologies resulted in generation of massive genome sequencing data in a considerable number of rice cultivars and species. To facilitate efficient visualization of rapidly emerging large-scale sequencing data, a novel web-based browser, Tasuke (http://tasuke.dna.affrc.go.jp/), with various functions to show the variation and read depth of multiple genomes, as well as annotations and SNP data of hundreds of cultivars aligned to a reference genome at various scales, has been developed for efficient utilization of emerging NGS data of rice cultivars (Kumagai et al. 2013).
Deeper perspectives on rice genetic resources
The reference Nipponbare genome facilitated the sequencing initiatives of rice germplasm aimed at understanding the genetic diversity which led to the domestication of rice as grown today. Foremost among these initiatives are the Oryza Map Alignment Project (OMAP) to clarify the diversity in the twelve wild rice genomes (Wing et al. 2007), and the international effort of resequencing a core collection of 3000 rice accessions from 89 countries to provide a foundation for large-scale discovery of novel alleles for important rice phenotypes (The 3,000 Rice Genomes Project 2014; Huang et al. 2012). To facilitate comprehensive understanding of the genome diversity in rice, we have also sequenced the African rice O. glaberrima known to be more resilient to water shortage as well as fungal or insect diseases than O. sativa (Sakai et al. 2011). The high-quality assembly and annotation of the O. glaberrima genome have also been reported, providing evidence for its independent domestication (Wang et al. 2014).
In contrast, successive efforts in Japan focus on understanding cultivars important to Japanese agriculture particularly those widely grown throughout Japan. To date, whole genome sequences from 16 varieties obtained by next-generation sequencers have been submitted in public databases by NIAS and other Japanese research organizations (Additional file 1: Table S2), and projects for sequencing other varieties and landraces are in progress.
The cultivar Koshihikari developed in 1953 is the most widely grown and favored cultivar in Japan occupying almost 80 % of total rice production including its relative cultivars. Many breeding efforts focus on further improvement of quality depending on the region where it is grown. The genome sequence of Koshihikari is therefore indispensable in breeding and designing rice to meet the demands of Japanese consumers. With the reference Nipponbare sequence, the next development was the sequencing of the Koshihikari genome with the Illumina sequencing technology (Yamamoto et al. 2010). A total of 67,051 SNPs between Koshihikari and Nipponbare, some of which derived from originating landraces and distributed through Koshihikari relatives, and 18 pedigree haplotype blocks which were artificially selected during breeding.
The Nipponbare pseudomolecule sequence was used as the template in the construction of a complete BAC-based physical map of the O. sativa ssp. indica cv. Kasalath (Kanamori et al. 2013). We also sequenced the centromere region of chromosome 8 in Kasalath (Wu et al. 2009). Comparative analysis with Nipponbare Cen8 revealed both collinearity and diversity in each orthologous centromere. Subsequently, deep sequencing (>154-fold coverage) via the Roche GS-FLX Titanium or GS-FLX+ and Illumina GAIIx or HiSeq 2000 platforms and de novo assembly generated the 330.55 Mb Kasalath pseudomolecule sequence representing 91.1 % of the genome with 35,139 expressed loci annotated by RNA-Seq analysis (Sakai et al. 2014). Comparison of the Kasalath pseudomolecule with Nipponbare revealed 2,787,250 SNPs and 7393 large indel sites (>100 bp). On the other hand, comparison with the indica cultivar 93–11 showed 2,216,251 SNPs and 3780 large indels (Sakai et al. 2014). In particular, at least 14.78 Mb of indel sequences and 40.75 Mb of unmapped sequences were identified in the Kasalath genome in comparison with the Nipponbare genome suggesting that ~6.3 % of the total transcript loci in rice genome is presumably involved with gain or loss of genes.
Genotyping of the NIAS Genebank (https://www.gene.affrc.go.jp/index_en.php) rice accessions with 179 RFLP markers led to development of a rice diversity research set of germplasms (RDRS) in indica, aus and japonica accessions available for the detailed genetic studies and rice improvement (Kojima et al. 2005). Based on a result from screening 234 accessions of rice collected in Asia, the Americas, Africa, Europe and Oceania with 169 SSR (simple sequence repeats) markers, moreover, current O. sativa cultivars and landraces can be classified in more detail into five genetically differentiated groups: indica, aus, aromatic, temperate japonica, and tropical japonica because of its deep genetic structure evolved during domestication and adaptation and its autogamous breeding system (Garris et al. 2005).
Elucidating the molecular function of rice genes
The rice genome sequence of Nipponbare has been pivotal in the development of a system for discovering the biological functions of approximately 32,000 genes identified in rice. This has been addressed early on with the development of resources for functional genomics such as the Tos17 insertion mutant panel (Hirochika 2001) and the rice full-length cDNA collection (Kikuchi et al. 2003). The Tos17 mutant collection consisting of approximately 50,000 mutant lines characterized into flanking sequences have been used in full characterization of many genes characterized in Japan and nearly 50 Tos17 mutant lines have been successfully used in tagging specific genes (See reference list at https://tos.nias.affrc.go.jp/doc/references.html).
Similarly, the information from approximately 28,000 fully-sequenced cDNA clones and the KOME Database (hhttps://dbarchive.biosciencedbc.jp/en/kome/desc.html) have been very useful for functional characterization of many genes. Both these resources are still widely used up to the present by researchers around the world particularly in systematically assigning functions to many predicted genes in the genome, in addition to other resources such as T-DNA insertion lines (Jung and An 2013), Ac/Ds tagging lines (Guiderdoni and Gantet 2012), nDART/aDart lines (Takagi et al. 2007). Analysis of the flanking sequences of these insertional mutants resulted in accumulation of nearly 448,000 gene-tagging sequence resources for characterization of gene functions (Wei et al. 2013).
With current advances in next-generation sequencing, we have embarked on analysis of gene expression using RNA-seq to measure the presence and quantity of RNA transcripts under specific growth conditions. As a result, unannotated salinity stress-inducible transcripts have been identified using the RNA-seq profile of seedlings treated with NaCl (Mizuno et al. 2010). Through RNA-seq analysis, previously unannotated salinity response genes, some of which might function in phosphate and cadmium stress tolerance were discovered. Simultaneous measurement of rice and rice blast fungus transcripts in infected plants using RNA-seq provided infection-responsive expression profiles for both rice and fungal transcripts. These profiles might indicate genes that may interact at different times and different tissues during infection (Kawahara et al. 2012). The same strategy has been used to characterize the transcriptome profile of japonica cultivar Nipponbare under phosphate starvation (Oono et al. 2011, 2013), as well as the diversity among the transcriptomes of rice cultivars including Nipponbare with low tolerance to phosphate starvation stress, japonica cultivars IAC 25 and Vary Lava 701 with relatively higher tolerance, and indica cultivar Kasalath known to be highly tolerant to phosphate stress (Oono et al. 2013). Recently, we have also found that cadmium stress controls the expression of genes in drought stress signal pathways in rice based on genome wide transcriptome analysis (Oono et al. 2014).
Isolation and utilization of agronomically important genes
The genome sequence coupled with genomics tools and resources such as mutant lines, genetic populations and germplasm collection paved the way for marker-assisted selection, mapping of QTLs and map-based cloning of specific genes having agronomic properties. As a result, many QTLs have been detected as Mendelian factors in rice, including those responsible for increased yield, resistance to various insect pests and diseases, resistance to abiotic stress such as drought, salinity and submergence, good eating quality etc.
Many QTLs involved in heading date, a key determinant of rice adaptation to different cultivation areas and cropping seasons, have been characterized. These include Hd1, Hd2, Hd3a, Hd3b, Hd4, Hd5, Hd6, Hd8, and Hd9 (Yano et al. 2001). Subsequent studies focused on more detailed characterization of these QTLs based on the genome sequence. The Hd1 contains a CCT domain with ~60 % identity to Ghd7, a long day dependent negative regulator of heading date. Hd6 encodes casein kinase 2 alpha, and Hd3a is similar to an Arabidopsis FT-like protein (Takahashi et al. 2001; Kojima et al. 2002). Another major QTL, Early heading date 1 (Ehd1), encodes a B-type response regulator that is suppressed under long-day conditions for which Ghd7 is responsible (Doi et al. 2004). In more recent studies, it has been found that Ehd3, encoding a PHD finger-containing protein, is a critical promoter of rice flowering (Matsubara et al. 2011). Hd17, a homolog of Arabidopsis EARLY FLOWERING 3 (ELF3), is involved in the photoperiodic flowering pathway by regulating the transcription level of a flowering repressor, Grain number, plant height and heading date 7 (Ghd7) gene (Matsubara et al. 2012). The QTL Hd16 is a gene for casein kinase I which is involved in the control of rice flowering time by modulating the day-length response (Hori et al. 2013).
Developing cultivars with broad spectrum of resistance to diseases is a priority for rice breeding in Japan. Among the genes widely characterized for disease resistance, the rice WRKY45 gene has been found to play a crucial role in resistance to bacterial and fungal blast induced by benzothiadiazole (BTH), a so-called plant activator that protects plants from diseases by activating plant innate immune system (Shimono et al. 2007). The Pib gene is the first cloned gene for resistance to blast induced by Magnaporthe oryzae (Wang et al. 1999) and it was reported much later that Pi21 encodes a proline-rich protein with a heavy metal-binding domain and putative protein-protein interaction motifs (Fukuoka et al. 2009).
The Nipponbare genome sequence has been instrumental in deciphering the evolutionary processes in the domestication of rice (Yang et al. 2012). Several genes that play key roles in selection and domestication have been analyzed. The seed shattering qSH1 gene has been found to encode a BEL1-type homeobox gene and a SNP in the 5’ regulatory region caused a loss of seed shattering owing to the absence of abscission layer formation (Konishi et al. 2006). More recently, Kala4, the gene responsible for the black color of rice grains (also referred to as purple rice) has been identified based on an extensive analysis of the genes associated with grain color in about 50 rice cultivars, tracing the origin to tropical japonica (Oikawa et al. 2015).
Map-based cloning of qLTG3-1 which controls low-temperature germination in rice provides useful insights on cultivation in temperate as well as high altitude rice growing areas (Fujino et al. 2008). The molecular mechanism of deepwater response has been clarified through the identification of the genes SNORKEL1 and SNORKEL2, which trigger deepwater response by encoding ethylene response factors involved in ethylene signaling (Hattori et al. 2009). With the molecular cloning of Sdr4, a seed dormancy QTL in rice, the role of the gene as an intermediate regulator of dormancy in the seed maturation program has been clarified (Sugimoto et al. 2010). The DEEPER ROOTING 1 (Dro1) has been recently discovered through screening and genetic analysis of the NIAS rice collection which have a great potential for improvement of rice yield under drought conditions by controlling the root system architecture in rice (Uga et al. 2013).
The major components that determine yield in rice have been widely characterized using the sequence information. The qSW5 gene which corresponds to the QTL for seed width on chromosome 5 has been cloned and a deletion in the gene was found to be associated with larger grain size (Shomura et al. 2008). Furthermore, it has also been shown that this variant was selected during rice domestication for increased yields. Characterization of genes associated with productivity has also made significant progress. A loss-of-function mutation of rice DENSE PANICLE 1 causes semi-dwarfness and slightly increased number of spikelets (Taguchi-Shiobara et al. 2011). A natural variant of NARROW LEAF 1 (NAL1) gene selected in high-yield rice breeding programs increased the photosynthesis rate (Takai et al. 2013; Fujita et al. 2013). The THOUSAND-GRAIN WEIGHT 6 (TGW6) gene limits endosperm cell number and grain length. Defective alleles lead to increase in grain size and yield (Ishimaru et al. 2013). Also researchers at Nagoya U. revealed the basic molecular strategy for construction good plant type for ideal yield performance, most of which related to metabolism or biosynthesis of plant hormones (Ueguchi-Tanaka et al. 2005; Ikeda et al. 2013)
In Japan, rice genomics has been a part of major research programs of the Ministry of Agriculture, Forestry and Fisheries (MAFF) that address various issues in sustainable food production and agriculture. Foremost among these issues are the rapid aging of farm workers and depopulation of farming communities which would eventually affect agricultural production in the not so distant future. In breeding programs, various efforts are being initiated to integrate rice genomics technology to the development of novel varieties to reinvigorate Japanese agriculture. The new basic plan for food, agriculture and the rural areas which serve as the guideline for advancing the reform of measures and efforts by the entire nation so as to enable Japan’s agriculture and rural areas to accurately respond to structural and other changes in the economy and society (http://www.maff.go.jp/e/basic_law/basiclaw_agri/basiclaw_agri.html). Rice being an integral part of Japanese agriculture is very much a part of these programs. Several ongoing MAFF-funded research projects focus on using the genome information of various crops for the development of various technologies to boost next-generation agriculture. As a part of that major project, the development of rice genomics resources and informatics tools are expected to contribute in such areas through attempts of various sectors to develop rice cultivars that address the specific needs of various rice producing regions in Japan considering the environmental changes in rice cultivation due to climate change.
As the rice research community embarks on various efforts in rice genomics, more rice genome sequence and transcriptome profiles will be generated in the very near future. Elucidating the molecular mechanisms controlling many biological processes will be supplemented by information to be obtained from other technologies such as proteomics, metabolomics (Okazaki and Saito 2016), epigenomics (Chen and Zhou 2013), and phenomics (Yang et al. 2013). Integration of all these data via advanced bioinformatics will elucidate the gene cascade or network in the whole rice plant that will serve as the platform on how to utilize and improve crop function. And Japanese researchers will continue to be a part of various initiatives for these advancements will most likely revolutionize rice breeding to circumvent future concerns of sustainable agriculture and food security.
IRGSP, International Rice Genome Sequencing Project; NGS, next-generation sequencing technology; RAP-DB, Rice Genome Annotation Project Database
The authors wish to thank Dr. Yukiko Yamazaki (National Institute of Genetics) for providing the data on rice publications. We also thank Dr. Ben Burr for valuable comments and suggestions. The Ministry of Agriculture, Forestry and Fisheries of Japan provided the funding to help defray the costs of publication.
TM and TS developed the overall concept of this review. All authors contributed in organizing the content and writing the different sections of the manuscript. All authors read and approved the final manuscript.
TS was the director of the Japan Rice Genome Research Program (RGP) and chairman of the International Rice Genome Sequencing Project (IRGSP). TM, TI, JW and BA were members of RGP and are currently involved in various research on genomics of rice and other cereal crops.
The authors declare that they have no competing interests.
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- Asano K, Yamasaki M, Takuno S, Miura K, Katagiri S, Ito T, Doi K, Wu J, Ebana K, Matsumoto T, Innan H, Kitano H, Ashikari M, Matsuoka M (2011) Artificial selection for a green revolution gene during japonica rice domestication. Proc Natl Acad Sci U S A 108:11034–11039View ArticlePubMedPubMed CentralGoogle Scholar
- Chen X, Zhou D-X (2013) Rice epigenomics and epigenetics: challenges and opportunities. Curr Opin Plant Biol 16:164–169View ArticlePubMedGoogle Scholar
- Cheng Z, Dong F, Langdon T, Ouyang S, Buell R, Gu M, Blattner F, Jiang J (2002) Functional rice centromeres are marked by a satellite repeat and a centromere-specific retrotransposon. Plant Cell 14:1691–1704View ArticlePubMedPubMed CentralGoogle Scholar
- Doi K, Izawa T, Fuse T, Yamanouchi U et al (2004) Ehd1, a B-type response regulator in rice, confers short-day promotion of flowering and controls FT-like gene expression independently of Hd1. Genes Dev 18:926–936View ArticlePubMedPubMed CentralGoogle Scholar
- Ebana K, Shibaya T, Wu J, Matsubara K, Kanamori H, Yamane H, Yamanouchi U, Mizubayashi T, Kono I, Shomura A, Ito S, Ando T, Hori K, Matsumoto T, Yano M (2011) Uncovering of major genetic factors generating naturally occurring variation in heading date among Asian rice cultivars. Theor Appl Genet 122:1199–1210View ArticlePubMedPubMed CentralGoogle Scholar
- Fujino K, Sekiguchi H, Matsuda Y, Sugimoto K, Ono K, Yano M (2008) Molecular identification of a major quantitative trait locus, qLTG3-1, controlling low-temperature germinability in rice. Proc Natl Acad Sci U S A 105:12623–12628View ArticlePubMedPubMed CentralGoogle Scholar
- Fujino K, Wu J, Sekiguchi H, Ito T, Izawa T, Matsumoto T (2010) Multiple introgression events surrounding the Hd1 flowering-time gene in cultivated rice, Oryza sativa L. Mol Genet Genomics 284:137–146View ArticlePubMedGoogle Scholar
- Fujita D, Trijatmiko KR, Tagle AJ, Sapasap MV, Koide Y, Sasaki K, Tsakirpaloglou N, Gannaban RB, Nishimura T, Yanagihara S, Fukuta Y, Koshiba T, Slamet-Loedin IHS, Ishimaru T, Kobayashi N (2013) NAL1 allele from a rice landrace greatly increase yield in modern indica cultivars. Proc Natl Acad Sci U S A 110:20431–20436View ArticlePubMedPubMed CentralGoogle Scholar
- Fukuoka S, Saka N, Koga H, Ono K, Shimizu T, Ebana K, Hayashi N, Takahashi A, Hirochika H, Okuno K, Yano M (2009) Loss of function of a proline-containing protein confers durable disease resistance in rice. Science 325:98–101View ArticleGoogle Scholar
- Garris AJ, Tai TH, Coburn J, Kresovich S, McCouch S (2005) Genetic structure and diversity in Oryza sativa L. Genetics 169:1631–1638View ArticlePubMedPubMed CentralGoogle Scholar
- Guiderdoni E, Gantet P (2012) Ac-Ds solutions for rice insertion mutagenesis. Methods Mol Biol 859:177–187View ArticlePubMedGoogle Scholar
- Hattori Y, Nagai K, Furukawa S, Song X, Kawano R, Sakakibara H, Wu J, Matsumoto T, Yoshimura A, Kitano H, Matsuoka M, Mori H, Ashikari M (2009) The ethylene response factors SNORKEL1 and SNORKEL2 allow rice to adapt to deep water. Nature 460:1026–1030View ArticlePubMedGoogle Scholar
- Hirochika H (2001) Contribution of the Tos17 retrotransposon to rice functional genomics. Curr Opin Plant Biol 4:118–122View ArticlePubMedGoogle Scholar
- Hori K, Ogiso-Tanaka E, Matsubara K, Yamanouchi U, Ebana K, Yano M (2013) Hd16, a gene for casein kinase I, is involved in the control of rice flowering time by modulating the day-length response. Plant J 76:36–46PubMedPubMed CentralGoogle Scholar
- Huang X, Kurata N, Wei X, Wang ZX, Wang A, Zhao Q, Zhao Y, Liu K, Lu H, Li W, Guo Y, Lu Y, Zhou C, Fan D, Weng Q, Zhu C, Huang T, Zhang L, Wang Y, Feng L, Furuumi H, Kubo T, Miyabayashi T, Yuan X, Xu Q, Dong G, Zhan Q, Li C, Fujiyama A, Toyoda A, Lu T, Feng Q, Qian Q, Li J, Han B (2012) A map of rice genome variation reveals the origin of cultivated rice. Nature 490:497–501View ArticlePubMedGoogle Scholar
- Ikeda M, Miura K, Aya K, Kitano H, Matsuoka M (2013) Genes offering the potential for designing yield-related traits in rice. Curr Opin Plant Biol 16:213–220View ArticlePubMedGoogle Scholar
- International Barley Genome Sequencing Consortium (2012) A physical, genetic and functional sequence assembly of the barley genome. Nature 491:711–716Google Scholar
- International Rice Genome Sequencing Project (2005) The map-based sequence of the rice genome. Nature 436:793–800View ArticleGoogle Scholar
- International Wheat Genome Sequencing Consortium (2014) A chromosome-based draft sequence of the hexaploid bread wheat (Triticum aestivum) genome. Science 345:1251788View ArticleGoogle Scholar
- Ishimaru K, Hirotsu N, Madoka Y, Murakami N, Hara N, Onodera H, Kashiwagi T, Ujiie K, Shimizu B, Onishi A, Miyagawa H, Katoh E (2013) Loss of function of the IAA-glucose hydrolase gene TGW6 enhances rice grain weight and increases yield. Nat Genet 45:707–711View ArticlePubMedGoogle Scholar
- Jiang J, Nasuda S, Dong F, Scherrer CW, Woo SS, Wing RA, Gill BS, Ward DC (1996) A conserved repetitive DNA element located in the centromeres of cereal chromosomes. Proc. Natl Acad Sci USA 93:14210–14213View ArticleGoogle Scholar
- Jung KH, An G (2013) Functional characterization of rice genes using a gene-indexed T-DNA insertional mutant population. Methods Mol Biol 956:57–67View ArticlePubMedGoogle Scholar
- Kanamori H, Fujisawa M, Katagiri S, Oono Y, Fujisawa H, Karasawa W, Kurita K, Sasaki H, Mori S, Hamada M, Mukai Y, Yazawa T, Mizuno H, Namiki N, Sasaki T, Katayose Y, Matsumoto T, Wu J (2013) A BAC physical map of aus rice cultivar ‘Kasalath’, and the map-based genomic sequence of ‘Kasalath’ chromosome 1. Plant J 76:699–708View ArticlePubMedGoogle Scholar
- Kawahara Y, Oono Y, Kanamori H, Matsumoto T, Itoh T, Minami E (2012) Simultaneous RNA-seq analysis of a mixed transcriptome of rice and blast fungus interaction. PLoS One 7–11, e49423View ArticleGoogle Scholar
- Kawahara Y, de la Bastide M, Hamilton J, Kanamori H, McCombie R, Ouyang S et al (2013) Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Rice 6:4View ArticlePubMedGoogle Scholar
- Kawahara Y, Oono Y, Wakimoto H, Ogata J, Kanamori H, Sasaki H, Mori S, Matsumoto T, Itoh T (2016) TENOR: Database for Comprehensive mRNA-Seq Experiments in Rice. Plant Cell Physiol 57(1), e7View ArticlePubMedGoogle Scholar
- Kikuchi S, Satoh K, Nagata T, Kawagashira N, Doi K, Kishimoto N et al (2003) Collection, mapping, and annotation of over 28,000 cDNA clones from japonica rice. Science 301:376–379View ArticlePubMedGoogle Scholar
- Kojima S, Takahashi Y, Kobayashi Y, Monna L, Sasaki T, Araki T, Yano M (2002) Hd3a, a rice ortholog of the Arabidopsis FT gene, promotes transition to flowering downstream of Hd1 under short-day conditions. Plant Cell Physiol 43:1096–1105View ArticlePubMedGoogle Scholar
- Kojima Y, Ebana K, Fukuoka S, Nagamine T, Kawase M (2005) Development of an RFLP-based rice diversity research set of germplasm. Breeding Sci 55:431–440View ArticleGoogle Scholar
- Komiya R, Ikegami A, Tamaki S, Yokoi S, Shimamoto K (2008) Hd3a and RFT1 are essential for flowering in rice. Development 135:767–774View ArticlePubMedGoogle Scholar
- Konishi S, Izawa T, Lin SY, Ebana K, Fukuta Y, Sasaki T, Yano M (2006) An SNP caused loss of seed shattering during rice domestication. Science 312:1392–1396View ArticlePubMedGoogle Scholar
- Kumagai M, Kim J, Itoh R, Itoh T (2013) Tasuke: a web-based visualization program for large-scale resequencing data. Bioinformatics 29:1806–1808View ArticlePubMedPubMed CentralGoogle Scholar
- Kurata N, Yamazaki Y (2006) Oryzabase. An integrated biological and genome information database for rice. Plant Physiol 140:12–17View ArticlePubMedPubMed CentralGoogle Scholar
- Matsubara K, Yamanouchi U, Nonoue Y, Sugimoto K, Wang ZX, Minobe Y, Yano M (2011) Ehd3, encoding a plant homeodomain finger-containing protein, is a critical promoter of rice flowering. Plant J 66:603–612View ArticlePubMedGoogle Scholar
- Matsubara K, Ogiso-Tanaka E, Hori K, Ebana K, Ando T, Yano M (2012) Natural variation in Hd17, a homolog of Arabidopsis ELF3 that is involved in rice photoperiodic flowering. Plant Cell Physiol 53:709–716View ArticlePubMedGoogle Scholar
- Mizuno H, Wu J, Kanamori H, Fujisawa M, Namiki N, Saji S, Katagiri S, Katayose Y, Sasaki T, Matsumoto T (2006) Sequencing and characterization of telomere and subtelomere regions on rice chromosomes 1S, 2S, 2L, 6L, 7S, 7L and 8S. Plant J 46:206–217View ArticlePubMedGoogle Scholar
- Mizuno H, Wu J, Katayose Y, Kanamori H, Sasaki T, Matsumoto T (2008a) Characterization of chromosome ends on the basis of the structure of TrsA subtelomeric repeats in rice (Oryza sativa L.). Mol Genet Genomics 280:19–24View ArticlePubMedGoogle Scholar
- Mizuno H, Wu J, Katayose Y, Kanamori H, Sasaki T, Matsumoto T (2008b) Chromosome-specific distribution of nucleotide substitutions in telomeric repeats of rice (Oryza sativa L.). Mol Biol Evol 25:62–68View ArticlePubMedGoogle Scholar
- Mizuno H, Kawahara Y, Sakai H, Kanamori H, Wakimoto H, Yamagata H, Oono Y, Wu J, Ikawa H, Itoh T, Matsumoto T (2010) Massive parallel sequencing of mRNA in identification of unannotated salinity, stress-inducible transcripts in rice (Oryza sativa L.). BMC Genomics 11:683View ArticlePubMedPubMed CentralGoogle Scholar
- Mizuno H, Kawahara Y, Wu J, Katayose Y, Kanamori H, Ikawa H, Itoh T, Sasaki T, Matsumoto T (2011) Asymmetric distribution of gene expression in the centromeric region of rice chromosome 5. Front Plant Sci 2:16View ArticlePubMedPubMed CentralGoogle Scholar
- Mizuno H, Wu J, Matsumoto T (2014) Characterization of chromosomal ends on the basis of chromosome-specific telomere variants and subtelomeric repeats in rice (Oryza sativa L.). Subtelomeres 10:187–194View ArticleGoogle Scholar
- Nagaki K, Cheng Z, Ouyang S, Talbert PB, Kim M, Jones KM, Henikoff S, Buell CR, Jiang J (2004) Sequencing of a rice centromere uncovers active genes. Nat Genet 36:138–145View ArticlePubMedGoogle Scholar
- Nagano AJ, Sato Y, Mihara M, Antonio BA, Motoyama R, Itoh H, Nagamura Y, Izawa T (2012) Deciphering and prediction of transcriptome dynamics under fluctuating field conditions. Cell 151:1358–1369View ArticlePubMedGoogle Scholar
- Ogiso-Tanaka E, Matsubara K, Yamamoto S, Nonoue Y, Wu J, Fujisawa H, Ishikubo H, Tanaka T, Ando T, Matsumoto T, Yano M (2013) Natural variation of the RICE FLOWERING LOCUS T 1 contributes to flowering time divergence in rice. PLoS One 8, e75959View ArticlePubMedPubMed CentralGoogle Scholar
- Oikawa T, Maeda H, Oguchi T, Yamaguchi T, Tanabe N, Ebana K, Yano M, Ebitani T, Izawa T (2015) The birth of a black rice gene and its spread by introgression. Plant Cell 9:2401–2414View ArticleGoogle Scholar
- Okazaki Y, Saito K (2016) Integrated metabolomics and phytochemical genomics approaches for studies in rice. GigaScience 5:11View ArticlePubMedPubMed CentralGoogle Scholar
- Oono Y, Kawahara Y, Kanamori H, Mizuno H, Yamagata H, Yamamoto M, Hosokawa S, Ikawa H, Akahane I, Zhu Z, Wu J, Itoh T, Matsumoto T (2011) mRNA-seq reveals a comprehensive transcriptome profile of rice under phosphate stress. Rice 4:50–65View ArticleGoogle Scholar
- Oono Y, Kawahara Y, Yazawa T, Kanamori H, Kuramata M, Yamagata H, Hosokawa S, Minami H, Ishikawa S, Wu J, Antonio B, Handa H, Itoh T, Matsumoto T (2013) Diversity in the complexity of phosphate starvation transcriptomes among rice cultivars based on RNA-Seq profiles. Plant Mol Biol 83:523–537View ArticlePubMedPubMed CentralGoogle Scholar
- Oono Y, Yazawa T, Kawahara Y, Kanamori H, Kobayashi F, Sasaki H, Mori S, Wu J, Handa H, Itoh T, Matsumoto T (2014) Genome-wide transcriptome analysis reveals that cadmium stress signaling controls the expression of genes in drought stress signal pathways in rice. PLoS One 9, e96946View ArticlePubMedPubMed CentralGoogle Scholar
- Paterson A, Bowers J, Bruggmann R, Dubchak I, Grimwood J, Gundlach H et al (2009) The Sorghum bicolor genome and the diversification of grasses. Nature 457:551–556View ArticlePubMedGoogle Scholar
- Rice Annotation Project (2008) The Rice Annotation Project Database (RAP-DB): 2008 update. Nucleic Acids Res 36:D1028–D1033View ArticleGoogle Scholar
- Sakai H, Ikawa H, Tanaka T, Numa H, Minami H, Fujisawa M, Shibata M, Kurita K, Kikuta A, Hamada M, Kanamori H, Namiki N, Wu J, Itoh T, Matsumoto T, Sasaki T (2011) Distinct evolutionary patterns of Oryza glaberrima deciphered by genome sequencing and comparative analysis. Plant J 66:796–805View ArticlePubMedPubMed CentralGoogle Scholar
- Sakai H, Lee SS, Tanaka T, Numa H, Kim J, Kawahara Y, Wakimoto H, Yang CC, Iwamoto M, Abe T, Yamada Y, Muto A, Inokuchi H, Ikemura T, Matsumoto T, Sasaki T, Itoh T (2013) Rice Annotation Project Database (RAP-DB): an integrative and interactive database for rice genomics. Plant Cell Physiol 54(2), e6View ArticlePubMedPubMed CentralGoogle Scholar
- Sakai H, Kanamori H, Arai-Kichise Y, Shibata-Hatta M, Ebana K, Oono Y, Kurita K, Fujisawa H, Katagiri S, Mukai Y, Hamada M, Itoh T, Matsumoto T, Katayose Y, Wakasa K, Yano M, Wu J (2014) Construction of pseudomolecule sequences of the aus rice cultivar Kasalath for comparative genomics of Asian cultivated rice. DNA Res 21:397–405View ArticlePubMedPubMed CentralGoogle Scholar
- Sato Y, Antonio B, Namiki N, Motoyama R, Sugimoto K, Takehisa H, Minami H, Kamatsuki K, Kusaba M, Hirochika H, Nagamura Y (2011) Field transcriptome revealed critical developmental and physiological transitions involved in the expression of growth potential in japonica rice. BMC Plant Biol 11:10View ArticlePubMedPubMed CentralGoogle Scholar
- Schmutz J, Cannon SB, Jessica Schlueter J, Ma J, Mitros T, Nelson W, Hyten DL et al (2010) Genome sequence of the palaeopolyploid soybean. Nature 463:178–183View ArticlePubMedGoogle Scholar
- Schnable P, Ware D, Fulton RS, Joshua C, Stein JC, Fusheng Wei F, Shiran Pasternak S (2009) The B73 maize genome: complexity, diversity, and dynamics. Science 326:1112–1115View ArticlePubMedGoogle Scholar
- Shimono M, Sugano S, Nakayama A, Jiang CJ, Ono K, Toki S, Takatsuji H (2007) Rice WRKY45 plays a crucial role in benzothiadiazole-inducible blast resistance. Plant Cell 19:2064–2076View ArticlePubMedPubMed CentralGoogle Scholar
- Shomura A, Izawa T, Ebana K, Ebitani T, Kanegae H, Konishi S, Yano M (2008) Deletion in a gene associated with grain size increased yields during rice domestication. Nat Genet 40:1023–1028View ArticlePubMedGoogle Scholar
- Sugimoto K, Takeuchi Y, Ebana K, Miyao A, Hirochika H, Hara N, Ishiyama K, Kobayashi M, Ban Y, Hattori T, Yano M (2010) Molecular cloning of Sdr4, a regulator involved in seed dormancy and domestication of rice. Proc Natl Acad Sci U S A 107:5792–5797View ArticlePubMedPubMed CentralGoogle Scholar
- Taguchi-Shiobara F, Kawagoe Y, Kato H, Onodera H, Tagiri A, Hara N, Miyao A, Hirochika H, Kitano H, Yano M, Toki S (2011) A loss-of-function mutation of rice DENSE PANICLE 1 causes semi-dwarfness and slightly increased number of spikelets. Breeding Sci 61:17–25View ArticleGoogle Scholar
- Takagi K, Ishikawa N, Maekawa M, Tsugane K, Iida S (2007) Transposon display for active DNA transposons in rice. Genes Genet Syst 82:109–122View ArticlePubMedGoogle Scholar
- Takahashi Y, Shomura A, Sasaki T, Yano M (2001) Hd6, a rice quantitative trait locus involved in photoperiod sensitivity, encodes the alpha subunit of protein kinase CK2. Proc Natl Acad Sci U S A 98:7922–7927View ArticlePubMedPubMed CentralGoogle Scholar
- Takahashi Y, Teshima KM, Yokoi S, Innan H, Shimamoto K (2009) Variations in Hd1 proteins, Hd3a promoters, and Ehd1 expression levels contribute to diversity of flowering time in cultivated rice. Proc Natl Acad Sci U S A 106:4555–4560View ArticlePubMedPubMed CentralGoogle Scholar
- Takai T, Adachi S, Taguchi-Shiobara F, Sanoh-Arai Y, Iwasawa N, Yoshinaga S et al (2013) A natural variant of NAL1, selected in high-yield rice breeding programs, pleiotropically increases photosynthesis rate. Sci Rep 3:2149View ArticlePubMedPubMed CentralGoogle Scholar
- The 3,000 Rice Genomes Project (2014) The 3,000 rice genomes project. GigaScience 3:7View ArticleGoogle Scholar
- Ueguchi-Tanaka M, Ashikari M, Nakajima M, Itoh H, Katoh E, Kobayashi M, Chow TY, Hsing YI, Kitano H, Yamaguchi I, Matsuoka M (2005) GIBBERELLIN INSENSITIVE DWARF1 encodes a soluble receptor for gibberellin. Nature 437:693–698View ArticlePubMedGoogle Scholar
- Uga Y, Sugimoto K, Ogawa S, Rane J, Ishitani M, Hara N, Kitomi Y, Inukai Y, Ono K, Kanno N, Inoue H, Takehisa H, Motoyama R, Nagamura Y, Wu J, Matsumoto T, Takai T, Okuno K, Yano M (2013) Control of root system architecture by DEEPER ROOTING 1 increases rice yield under drought conditions. Nat Genet 45:1097–1102View ArticlePubMedGoogle Scholar
- Wang ZX, Yano M, Yamanouchi U, Iwamoto M, Monna L, Hayasaka H, Katayose Y, Sasaki T (1999) The Pib gene for rice blast resistance belongs to the nucleotide binding and leucine-rich repeat class of plant disease resistance genes. Plant J 19:55–64View ArticlePubMedGoogle Scholar
- Wang M, Yu Y, Haberer G, Marri PR, Fan C, Goicoechea JL, Zuccolo A, Song X, Kudrna D, Ammiraju JS, Cossu RM, Maldonado C, Chen J, Lee S, Sisneros N, Baynast K, Golser W, Wissotski M, Kim W, Sanchez P, Ndjiondjop MN, Sanni K, Long M, Carney J, Panaud O, Wicker T, Machado CA, Chen M, Mayer KF, Rounsley S, Wing RA (2014) The genome sequence of African rice (Oryza glaberrima) and evidence for independent domestication. Nat Genet 46:982–988View ArticlePubMedGoogle Scholar
- Wei FJ, Droc G, Guiderdoni E, Hsing YI (2013) International Consortium of Rice Mutagenesis: resources and beyond. Rice 6:39View ArticlePubMedPubMed CentralGoogle Scholar
- Wing RA, Kim H, Goicoechea JL, Yu Y, Kudrna D, Zuccolo A, Ammiraju J, Luo M, Nelson W, Ma J, Sanmiguel P, Hurwitz B, Ware D, Brar D, Mackill D, Soderlund C, Stein L, Jackson S (2007) The oryza map alignment project (OMAP): A new resource for comparative genome studies within oryza. In: Rice Functional Genomics: Challenges, Progress and Prospects. Springer, New York, pp 395–409View ArticleGoogle Scholar
- Wu J, Yamagata H, Hayashi-Tsugane M, Hijishita S, Fujisawa M, Shibata M, Ito Y, Nakamura M, Sakaguchi M, Yoshihara R, Kobayashi H, Ito K, Karasawa W, Yamamoto M, Saji S, Katagiri S, Kanamori H, Namiki N, Katayose Y, Matsumoto T, Sasaki T (2004) Composition and structure of the centromeric region of rice chromosome 8. Plant Cell 16:967–976View ArticlePubMedPubMed CentralGoogle Scholar
- Wu J, Mizuno H, Sasaki T, Matsumoto T (2008) Comparative analysis of rice genome sequence to understand the molecular basis of genome evolution. Rice 1:119–126View ArticleGoogle Scholar
- Wu J, Fujisawa M, Tian Z, Yamagata H, Kamiya K, Shibata M, Hosokawa S, Ito Y, Hamada M, Katagiri S, Kurita K, Yamamoto M, Kikuta A, Machita K, Karasawa W, Kanamori H, Namiki N, Mizuno H, Ma J, Sasaki T, Matsumoto T (2009) Comparative analysis of complete orthologous centromeres from two subspecies of rice reveals rapid variation of centromere organization and structure. Plant J 60:805–819View ArticlePubMedGoogle Scholar
- Yamamoto T, Nagasaki H, Yonemaru J, Ebana K, Nakajima M, Shibaya T, Yano M (2010) Fine definition of the pedigree haplotypes of closely related rice cultivars by means of genome-wide discovery of single-nucleotide polymorphisms. BMC Genomics 11:267View ArticlePubMedPubMed CentralGoogle Scholar
- Yamane H, Ito T, Ishikubo H, Fujisawa M, Yamagata H, Kamiya K, Ito Y, Hamada M, Kanamori H, Ikawa H, Katayose Y, Wu J, Sasaki T, Matsumoto T (2009) Molecular and evolutionary analysis of the Hd6 photoperiod sensitivity gene within genus Oryza. Rice 2:56–66View ArticleGoogle Scholar
- Yan H, Ito H, Nobuta K, Ouyang S, Jin W, Tian S, Lu C, Venu RC, Wang GL, Green PJ, Wing RA, Buell CR, Meyers BC, Jiang J (2006) Genomic and genetic characterization of rice Cen3 reveals extensive transcription and evolutionary implications of a complex centromere. Plant Cell 18:2123–2133View ArticlePubMedPubMed CentralGoogle Scholar
- Yang CC, Kawahara Y, Mizuno H, Wu J, Matsumoto T, Itoh T (2012) Independent domestication of Asian rice followed by gene flow from japonica to indica. Mol Biol Evol 29:1471–1479View ArticlePubMedGoogle Scholar
- Yang W, Duan L, Chen G, Xiong L, Liu Q (2013) Plant phenomics and high-throughput phenotyping: accelerating rice functional genomics using multidisciplinary technologies. Curr Opin Plant Biol 16:180–187View ArticlePubMedGoogle Scholar
- Yano M, Kojima S, Takahashi Y, Lin H, Sasaki T (2001) Genetic control of flowering time in rice, a short-day plant. Plant Physiol 127:1425–1429View ArticlePubMedPubMed CentralGoogle Scholar
- Zhang Y, Huang Y, Zhang L, Li Y, Lu T, Lu Y, Feng Q, Zhao Q, Cheng Z, Xue Y, Wing RA, Han B (2004) Structural features of the rice chromosome 4 centromere. Nucleic Acids Res 32:2023–2030View ArticlePubMedPubMed CentralGoogle Scholar
- Zhou S, Bechner MC, Place M, Churas CP, Pape L, Leong SA, Runnheim R, Forrest DK, Goldstein S, Livny M, Schwartz DC (2007) Validation of rice genome sequence by optical mapping. BMC Genomics 8:278View ArticlePubMedPubMed CentralGoogle Scholar