- Research article
- Open Access
Exploring molecular backgrounds of quality traits in rice by predictive models based on high-coverage metabolomics
- Henning Redestig†1, 2,
- Miyako Kusano†1,
- Kaworu Ebana3,
- Makoto Kobayashi1,
- Akira Oikawa1,
- Yozo Okazaki1,
- Fumio Matsuda1, 4,
- Masanori Arita1, 5,
- Naoko Fujita6 and
- Kazuki Saito1, 7Email author
© Redestig et al; licensee BioMed Central Ltd. 2011
- Received: 16 May 2011
- Accepted: 28 October 2011
- Published: 28 October 2011
Increasing awareness of limitations to natural resources has set high expectations for plant science to deliver efficient crops with increased yields, improved stress tolerance, and tailored composition. Collections of representative varieties are a valuable resource for compiling broad breeding germplasms that can satisfy these diverse needs.
Here we show that the untargeted high-coverage metabolomic characterization of such core collections is a powerful approach for studying the molecular backgrounds of quality traits and for constructing predictive metabolome-trait models. We profiled the metabolic composition of kernels from field-grown plants of the rice diversity research set using 4 complementary analytical platforms. We found that the metabolite profiles were correlated with both the overall population structure and fine-grained genetic diversity. Multivariate regression analysis showed that 10 of the 17 studied quality traits could be predicted from the metabolic composition independently of the population structure. Furthermore, the model of amylose ratio could be validated using external varieties grown in an independent experiment.
Our results demonstrate the utility of metabolomics for linking traits with quantitative molecular data. This opens up new opportunities for trait prediction and construction of tailored germplasms to support modern plant breeding.
- Quality Trait
- Metabolite Profile
- Kernel Size
- Metabolite Abundance
Modern crop breeding techniques such as wide crossing and marker-assisted selection have been highly successful in improving the quality traits of rice [1, 2]. However, as slow selection processes and narrow germplasms  have raised doubts on how much further current strategies can take us , we must diversify the used genetic material and develop novel breeding technologies.
While the germplasm that is actively used for rice breeding may be narrow, the total number of rice varieties is enormous due to its very long domestication history . The broader use of available genetic variance has great potential, both to improve crops directly  and to elucidate molecular determinants behind quality traits (see e.g. ). Unfortunately, the necessary molecular characterization is often prohibitively expensive for large seed collections.
Genetic core collections of relatively small size have been developed in several rice genebanks to obtain manageable but still representative selections, e.g., the Rice Germplasm Core Set (RGCS) from the International Rice Research Institute (623 accessions) , the GCore collections (16 × ~120 accessions) , the EMBRAPA Rice Core Collection (ERiCC, 242 accessions)  and the rice diversity research set (RDRS) . Of these, the RDRS is particularly interesting because its restriction fragment length polymorphism (RFLP) marker diversity is highly representative of cultivated rice (Oryza sativa L.); yet with only 67 varieties, it is small enough to allow comprehensive molecular profiling.
Direct relationships between metabolic composition and genotype and phenotype have been shown for the model plant Arabidopsis thaliana using both recombinant inbred lines  and natural varieties [12, 13]. Metabolomics has emerged a key technology for characterizing crop germplasms; it has the potential to provide a breakdown of complex high-level traits by expressing them as a sum of correlated quantitative molecular features. Such molecular factorization may increase the physiological understanding of quality traits and provide clues for possible implications associated with selecting for them. This is highly relevant since metabolic composition is itself an important quality trait as it is tightly connected to the taste and the nutritional and physical characteristics of the harvested material .
With these considerations in mind, we aimed to (i) chart the metabolic diversity of kernels from the RDRS and (ii) investigate the covariance between metabolite profiles and quantitative quality traits. A previous study of 18 of the RDRS varieties using 1H-NMR did not reveal any relationship between metabolomic and overall genetic diversity . As this finding may be attributable to the small sample size and insufficient resolution of the applied technique, we aimed to obtain metabolomic coverage as high as possible and decided to profile the complete RDRS. Because no current single technology can separate all compounds equally well , we chose to integrate data from 4 complementary mass spectrometry (MS) -based platforms, and thereby reducing bias towards any particular chemical subclass of metabolites . The resulting data showed clear compositional differences among the 3 genetic subtypes Indica I, Indica II and Japonica. Using a novel extension of orthogonal projection to latent structures (OPLS)  that facilitates the handling of multi-block data (MB-OPLS), we found that given the metabolic composition, 10 of the 17 studied traits, including the important kernel size , ear emergence day , and amylose ratio (abundance amylose/total starch content), could be predicted indicating robust trait-metabolite covariance.
Starch composition is a major determinant of the taste and texture of cooked rice . The packing characteristics of starch also determine the proportion of desired translucent kernels to kernels with chalky white cores that are prone to breakage during processing . Our metabolomics model confirmed previously observed strong negative associations between fatty acids/lipids and amylose ratios [23, 24]. Furthermore, the same model accurately predicted the amylose ratio for an independent set of varieties grown in a remote field. However, starch synthase IIIa knock-out lines (ssIIIa) with white-core phenotypes had very high amylose ratios without the accompanying expected fatty acid/lipid composition, suggesting an important role of fatty acids in starch packing. Taken together, our results demonstrate the usefulness of metabolomic profiling of genetically diverse varieties for linking quality traits with molecular features.
Multi-platform metabolomics of the RDRS
Metabolite profiles show a fine-grained correlation with genetic variation
Our results show a substantial overlap between metabolite profiles and the underlying genetic backgrounds (Figure 2c). Although of interest for comparing subtypes, this type of large-scale correlation between genotype and phenotype (metabotype) is obstructive when searching for functional associations with high-level traits . Using the Mantel test  with 10,000 permutations, we examined whether the Euclidean distances in metabolite space between different varieties were correlated with their corresponding genetic distances both for the whole RDRS, and for the 3 subtypes separately. As expected, the highest significance was observed for the whole dataset (P = 0.0001) but Japonica (P = 0.0047), Indica I (P = 0.0064), and Indica II (P = 0.0001) were also significant on their own, indicating the presence of a fine-grained correlation between genetic diversity and metabolite abundances (Additional File 1, Figure S4).
MB-OPLS regression predicts quality traits from metabolic composition
Before investigating trait-metabolite correlations we removed the covariance between the trait data and the population membership Q-matrix from the STRUCTURE program by means of multiple linear regression. As confirmed by PCA, the resulting data showed no clustering of the 3 subtypes (Additional File 1, Figure S3). Furthermore, the pre-processed traits exhibited highly individual variations, except for kernel size-weight and hull- and kernel width (Additional File 1, Figure S5).
The OPLS regression framework, and therefore also MB-OPLS, provide correlation loadings, P C , that can be used to interpret the relevance of each metabolite for the corresponding prediction. However, this value does not assign any statistical significance in terms of comparison with a postulated null-hypothesis (no trait-metabolite associations) and the variance of the observed sampling distribution of P C . To address this problem we define a probabilistic statistic for feature selection, log B; it scores how many times more likely the alternative hypothesis is over the null-hypothesis.
When screening for trait-associated metabolites we used both the model-based log B statistic and the nominal Spearman's correlation, ρ S , as a complementary bivariate method. We extracted the annotated metabolites with log B >0 and ρ S with an associated false discovery rate (FDR) less than 0.05. We visualized the correlation loadings for all annotated metabolites as word clouds, and listed the top 10 selected metabolites in Additional file 3, Table 1. The model for amylose ratio is characterized by high negative loadings for several fatty acids as well as choline and putrescine. For ear emergence day, tryptophan and putrescine have large positive loadings. Succinate, glucose-6-phosphate, and glycine are all positively correlated with kernel size whereas 3 lipids (18:1-lysophosphatidyl cholines (lysoPC), 18:2-lysoPC and 14:0-lysoPC) are negatively correlated. A complete list of trait-metabolite associations in given Additional File 2, Supplementary Data 2.
Independent experiment demonstrates robustness of the model of amylose ratio
We profiled the metabolomic composition of kernels from the RDRS and investigated trait-metabolite correlations by means of a multi-platform approach. Using our multi-block extension of the OPLS algorithm we found a population structure-independent correlation between metabolite abundances and 10 of the 17 examined traits. With the majority of these traits being only weakly dependent on each other (Figure 5), this indicates a rich correlation structure and high a information content in the metabolomics data. Our study thus confirms, and widely extends, the results shown for Arabidopsis thaliana grown under tightly controlled conditions [11, 12], for an important crop species grown under field conditions.
The MB-OPLS model for amylose ratio indicated very strong negative correlations between the amylose ratio and the abundances of palmitic acid, linoleic acid, glycerol, and putrescine, and positive correlations with 18:2 and 14:0 lysoPC (Figure 4, Additional File 1, Table S1). The two prevalent forms of starch in rice is amylose and amylopectin and a high measured amylose ratio thereby indirectly indicate a low amylopectin ratio. The link between starch-bound fatty acids/lipids has already been observed in rice  and maize , on the metabolic- and gene expression level  the biochemical function of this connection is unclear.
The RDRS-based model was robust enough to give good predictions for kernels from external varieties from an independent experiment despite unaccounted differences between the growth times and locations (Figure 7). Interestingly, the 2 knock-out lines were exceptions to the rule of a negative correlation between amylose ratio and fatty acid content. This indicates that the retro-transposon inserts have broken the association with the metabolite composition, and that the link between amylose ratio and fatty acids is under feed-back control. Analysis of the biochemical or genetical backgrounds of these correlations was not within the scope of this study but we note that fatty acids and lipids are good starch-complexing agents and their presence influences physicochemical properties . In addition, we observed strong differences in kernel phenotype between natural varieties and the two mutants (Figure 6). Grain chalkiness is a complicated trait affected by environmental changes  and genetic background . Our results suggest that also fatty acids/lipids have an important function in modulating the texture and structural properties of the stored starch.
The model for the ear emergence day was also very accurate (Figure 4) and gave high weight to putrescine and tryptophan (Additional file 3, Table 1). Putrescine is a major amine in rice kernels  and has been implicated in the regulation of plant growth and development . However, transgenic rice over-expressing a gene encoding a feedback-insensitive α-subunit of rice anthranilate synthase (OASA1D) had increased levels of tryptophan and indole-3-acetate as well as other amino acids in kernels without a significant difference in the ear emergence day .
For Arabidopsis photosynthetic tissues, it has been shown that biomass is negatively correlated with glucose-6-phosphate and succinate levels . Keeping in mind that the rice kernel is a strong energy sink with very little own photosynthetic activity, it is not surprising that we instead observed a positive correlation between glucose-6-phosphate and kernel size. This result supports the general idea that energy demand during grain-filling plays an important role in determining kernel size . In a brief study of metabolite abundances and kernel sizes using a collection of backcross recombinant inbred lines between Kasalath (Indica I) and Koshihikari variety (Japonica), this pattern was not visible indicating the connection is not generally visible among all genotypes (data not shown). However, detailed dissection of the genetic background of these patterns is left to a future study.
The model for iron content showed a rather low but still significant predictive performance with and PCV = 0.024. However, nicotianamine, known to be involved in iron metabolism , was of the few annotated annotated metabolites with log B >0 (Figure 5, Additional File 2, Supplementary Data 2). These results exemplify how metabolic profiling of genetically diverse varieties can reveal functional relationships between molecular factors and important quality traits.
We summarize the main conclusions as follows.
The overlap between metabolic and genetic profiles in the RDRS was visible with respect to general subtypes (Figure 2b), and fine differences within the more homogeneous populations Indica I, Indica II and Japonica (Additional File 1, Figure S4). This shows that metabotypic- and genotypic-covariance could be detected in a field-grown collection of natural rice cultivars of relatively limited size.
The metabolic diversity was furthermore found to be associated with 10 of the 17 studied quality traits (Figure 4) showing that trait-metabolite associations are common, and that they can be uncovered by profiling natural varieties. The resulting network of the trait-associated metabolites provided an overview of the molecular backgrounds of the traits (Figure 5) highlighting known (e.g. fatty acids and amylose ratio) and novel patterns (e.g. tryptophan and ear emergence day). From a technical point of view, we conclude that the applied metabolomics platforms were complementary and that integrating the datasets gave overall better prediction performance than achievable with data from any single platform.
The amylose ratio model showed that trait-metabolite associations can be robust enough to allow for prediction across independent sets of cultivars grown on different occasions in remotely separated fields (Figure 7). A contributing reason for this robustness maybe that the mature kernel has little metabolic activity on its own and is less influenced by environmental factors than e.g. the leaves.
Taken together, these results show that metabolomics may be used to factorize important quality traits into distinct genotype-correlated molecular features. These features can both aid physiological interpretation and potentially be used as bridges to identify trait-(metabolite)-associated loci. This concept is similar to the current advancements in plant phenomics. There, complex high-level traits are being modeled using sets of simpler traits that have tighter relationships with genetic determinants than the high-level trait itself . With metabolomics, traits can be factorized to an even higher resolution that may point directly to underlying genetically-dependent molecular determinants. As genetic data of adequate resolution are currently not available for RDRS, that analysis was not within the scope of our study. However, as such data are anticipated, the value of the dataset presented here is expected to increase.
The RDRS and an external set of rice varieties as well as two knockout mutants (e1 and 4019) were used for this study. Plant growth and harvesting were carried out as described in Additional File 1, Supplementary Methods.
All data was log2 transformed and scaled to unit-variance prior to further data analysis. All peaks with more than 30% missing values were excluded.
The multi-platform data was summarized by unifying metabolite identifiers to a common referencing scheme using the MetMask tool . The four matrices were then concatenated and correlated peaks with the same annotation were replaced by their first principal component. Coverage of the chemical diversity was calculated as described by . The summarized dataset is available at http://prime.psc.riken.jp/?action=drop_index and as Additional File 4, Supplementary Data 3. Detailed information of extraction, MS conditions and data processing of GC-MS, LC-MS, CE-MS and IT-MS were performed as described in Chemical analysis metadata in the section of Metabolomics metadata.
All data analyses were performed using R v2.12.1. Network visualization was done using Cytoscape and the GOlorize plug-in . Missing value robust PCA was performed using the pcaMethods package . See Additional File 1, Supplementary Methods for detailed description of the data analysis.
Correction for population structure
where Q is the estimated population membership matrix from the STRUCTURE program and B is the vector of coefficients estimated by least-squares regression.
Top-level models are then estimated by ordinary OPLS regression between XTop,jand Y j . MB-OPLS for a single block is equivalent to ordinary OPLS.
Each MB-OPLS model has j + 1 parameters corresponding to the number of orthogonal components (number of columns in Ti, j, O) used for the block-, and top-level models respectively. We optimize these parameters by seven-fold internal cross-validation (CV).
The diagnostical statistic of the complete model is estimated in an external seven-fold CV where a set of samples is held out to serve a test-set and the remaining are used to construct the internally cross-validated model. This process is repeated for each CV-segment to obtain independent predictions of the complete Y j . In order to test the significance of the model, we shuffle Y j one-thousand times, calculate , and count the number of times, n0, when for the shuffled data is more than or equal to for real data and form the biased P-value estimate PCV = (n0 + 1)/(1000 + 1). This CV approach is computationally intensive and was therefore computed on in parallel using the multicore package . Since the depends on the way the samples are divided in to training and test sets, we calculate 50 times and report the median of these runs.
setting the a priori expected probability of H0 to 0.95. Our statistic log is then greater than zero for metabolites with loadings that are robustly larger than expected given that H0 was true.
We thank M. Kobayashi, N. Hayashi, H. Otsuki, S. Shinoda, R. Niida and M. Suzuki (RIKEN Plant Science Center, Japan) for their technical assistance and K. Akiyama and T. Sakurai (RIKEN Plant Science Center, Japan) for their support with data storage and management. We are grateful to P. Jonsson, H. Stenlund (Umeå University, Sweden) and T. Moritz (Umeå Plant Science Centre) for sharing their software for GC-MS data pre-treatment.
- Sharma H, Crouch J, Sharma K, Seetharama N, Hash C: Applications of biotechnology for crop improvement: prospects and constraints. Plant Sci. 2002, 163: 381-395. 10.1016/S0168-9452(02)00133-4.View ArticleGoogle Scholar
- Khush GS: Green revolution: the way forward. Nat Rev Genet. 2001, 2 (10): 815-822. 10.1038/35093585.View ArticlePubMedGoogle Scholar
- Kojima Y, Ebana K, Ebana K, Fukuoka S, Nagamine T, Kawase M: Development of an RFLP-based rice diversity research set of germplasm. Breeding Science. 2005, 55: 431-440. 10.1270/jsbbs.55.431.View ArticleGoogle Scholar
- Wang Y, Xue Y, Li J: Towards molecular breeding and improvement of rice in China. Trends Plant Sci. 2005, 10 (12): 610-614. 10.1016/j.tplants.2005.10.008.View ArticlePubMedGoogle Scholar
- Sweeney M, McCouch S: The complex history of the domestication of rice. Ann Bot. 2007, 100 (5): 951-957. 10.1093/aob/mcm128.PubMed CentralView ArticlePubMedGoogle Scholar
- Gur A, Zamir D: Unused natural variation can lift yield barriers in plant breeding. PLoS Biol. 2004, 2 (10): e245-10.1371/journal.pbio.0020245.PubMed CentralView ArticlePubMedGoogle Scholar
- Huang X, Qian Q, Liu Z, Sun H, He S, Luo D, Xia G, Chu C, Li J, Fu X: Natural variation at the DEP1 locus enhances grain yield in rice. Nat Genet. 2009, 41 (4): 494-497. 10.1038/ng.352.View ArticlePubMedGoogle Scholar
- IRRI: 2011, [http://iris.irri.org/germplasm/]
- Li CT, Shi CH, Wu JG, Xu HM, Zhang HZ, Ren YL: Methods of developing core collections based on the predicted genotypic value of rice (Oryza sativa L.). Theor Appl Genet. 2004, 108 (6): 1172-1176. 10.1007/s00122-003-1536-1.View ArticlePubMedGoogle Scholar
- de Oliveira Borba TC, Brondani RPV, Rangel PHN, Brondani C: Microsatellite marker-mediated analysis of the EMBRAPA Rice Core Collection genetic diversity. Genetica. 2009, 137 (3): 293-304. 10.1007/s10709-009-9380-0.View ArticlePubMedGoogle Scholar
- Meyer RC, Steinfath M, Lisec J, Becher M, Witucka-Wall H, Törjék O, Fiehn O, Eckardt A, Willmitzer L, Selbig J, Altmann T: The metabolic signature related to high plant growth rate in Arabidopsis thaliana. Proc Natl Acad Sci USA. 2007, 104 (11): 4759-4764. 10.1073/pnas.0609709104.PubMed CentralView ArticlePubMedGoogle Scholar
- Sulpice R, Pyl ET, Ishihara H, Trenkamp S, Steinfath M, Witucka-Wall H, Gibon Y, Usadel B, Poree F, Piques MC, Korff MV, Steinhauser MC, Keurentjes JJB, Guenther M, Hoehne M, Selbig J, Fernie AR, Altmann T, Stitt M: Starch as a major integrator in the regulation of plant growth. Proc Natl Acad Sci USA. 2009, 106 (25): 10348-10353. 10.1073/pnas.0903478106.PubMed CentralView ArticlePubMedGoogle Scholar
- Sulpice R, Trenkamp S, Steinfath M, Usadel B, Gibon Y, Witucka-Wall H, Pyl ET, Tschoep H, Steinhauser MC, Guenther M, Hoehne M, Rohwer JM, Altmann T, Fernie AR, Stitt M: Network analysis of enzyme activities and metabolite levels and their relationship to biomass in a large panel of Arabidopsis accessions. Plant Cell. 2010, 22 (8): 2872-2893. 10.1105/tpc.110.076653.PubMed CentralView ArticlePubMedGoogle Scholar
- Fitzgerald MA, McCouch SR, Hall RD: Not just a grain of rice: the quest for quality. Trends Plant Sci. 2009, 14 (3): 133-139. 10.1016/j.tplants.2008.12.004.View ArticlePubMedGoogle Scholar
- Mochida K, Furuta T, Ebana K, Shinozaki K, Kikuchi J: Correlation exploration of metabolic and genomic diversity in rice. BMC Genomics. 2009, 10: 568-10.1186/1471-2164-10-568.PubMed CentralView ArticlePubMedGoogle Scholar
- Saito K, Matsuda F: Metabolomics for Functional Genomics, Systems Biology, and Biotechnology. Annu Rev Plant Biol. 2010, 61: 463-489. 10.1146/annurev.arplant.043008.092035.View ArticlePubMedGoogle Scholar
- Kusano M, Redestig H, Hirai T, Oikawa A, Matsuda F, Fukushima A, Arita M, Watanabe S, Yano M, Hiwasa-Tanas K, Ezura H, Saito K: Covering chemical diversity of genetically-modified tomatoes using metabolomics for objective substantial equivalence assessment. PLoS ONE. 2011, 6: e16989-10.1371/journal.pone.0016989.PubMed CentralView ArticlePubMedGoogle Scholar
- Trygg J, Wold S: Orthogonal projections to latent structures (O-PLS). J Chemom. 2002, 16: 119-128. 10.1002/cem.695.View ArticleGoogle Scholar
- Song XJ, Huang W, Shi M, Zhu MZ, Lin HX: A QTL for rice grain width and weight encodes a previously unknown RING-type E3 ubiquitin ligase. Nat Genet. 2007, 39 (5): 623-630. 10.1038/ng2014.View ArticlePubMedGoogle Scholar
- Xue W, Xing Y, Weng X, Zhao Y, Tang W, Wang L, Zhou H, Yu S, Xu C, Li X, Zhang Q: Natural variation in Ghd7 is an important regulator of heading date and yield potential in rice. Nat Genet. 2008, 40 (6): 761-767. 10.1038/ng.143.View ArticlePubMedGoogle Scholar
- Zhou Z, Robards K, Helliwell S, Blanchard C: Composition and functional properties of rice. Int J Food Sci Technol. 2002, 37 (8): 849-868. 10.1046/j.1365-2621.2002.00625.x.View ArticleGoogle Scholar
- Ashida K, Iida S, Yasui T: Morphological, Physical, and Chemical Properties of Grain and Flour from Chalky Rice Mutants. Cereal Chem. 2009, 86: 225-231. 10.1094/CCHEM-86-2-0225.View ArticleGoogle Scholar
- Choudhury N, Juliano B: Effect of amylose content on the lipids of mature rice grain. Phytochemistry. 1980, 19 (7): 1385-1389. 10.1016/0031-9422(80)80179-8.View ArticleGoogle Scholar
- South J, Morrison W, Nelson O: A relationship between the amylose and lipid contents of starches from various mutants for amylose content in maize. J Cereal Sci. 1991, 14 (3): 267-278. 10.1016/S0733-5210(09)80045-3.View ArticleGoogle Scholar
- Kusano M, Fukushima A, Kobayashi M, Hayashi N, Jonsson P, Moritz T, Ebana K, Saito K: Application of a metabolomic method combining one-dimensional and two-dimensional gas chromatography-time-of-flight/mass spectrometry to metabolic phenotyping of natural variants in rice. J Chromatogr B Analyt Technol Biomed Life Sci. 2007, 855: 71-79. 10.1016/j.jchromb.2007.05.002.View ArticlePubMedGoogle Scholar
- Okazaki Y, Shimojima M, Sawada Y, Toyooka K, Narisawa T, Mochida K, Tanaka H, Matsuda F, Hirai A, Hirai M, Ohta H, Saito K: A Chloroplastic UDP-Glucose Pyrophosphorylase from Arabidopsis Is the Committed Enzyme for the First Step of Sulfolipid Biosynthesis. Plant Cell. 2009, 21: 892-909. 10.1105/tpc.108.063925.PubMed CentralView ArticlePubMedGoogle Scholar
- Redestig H, Fukushima A, Stenlund H, Moritz T, Arita M, Saito K, Kusano M: Compensation for systematic cross-contribution improves normalization of mass spectrometry based metabolomics data. Anal Chem. 2009, 81: 7974-7980. 10.1021/ac901143w.View ArticlePubMedGoogle Scholar
- Redestig H, Kusano M, Fukushima A, Matsuda F, Saito K, Arita M: Consolidating metabolite identifiers to enable contextual and multi-platform metabolomics. BMC Bioinformatics. 2010, 11: 214-10.1186/1471-2105-11-214.PubMed CentralView ArticlePubMedGoogle Scholar
- NIAS: 2011, [http://www.gene.affrc.go.jp/databases-core_collections_wr_en.php]
- Pritchard JK, Stephens M, Donnelly P: Inference of population structure using multilocus genotype data. Genetics. 2000, 155 (2): 945-959.PubMed CentralPubMedGoogle Scholar
- Pritchard JK, Stephens M, Rosenberg NA, Donnelly P: Association mapping in structured populations. Am J Hum Genet. 2000, 67: 170-181. 10.1086/302959.PubMed CentralView ArticlePubMedGoogle Scholar
- Mantel N: The detection of disease clustering and a generalized regression approach. Cancer Res. 1967, 27: 209-220.PubMedGoogle Scholar
- Bylesjö M, Eriksson D, Sjödin A, Jansson S, Moritz T, Trygg J: Orthogonal projections to latent structures as a strategy for microarray data normalization. BMC Bioinformatics. 2007, 8: 207-10.1186/1471-2105-8-207.PubMed CentralView ArticlePubMedGoogle Scholar
- Fujita N, Yoshida M, Kondo T, Saito K, Utsumi Y, Tokunaga T, Nishi A, Satoh H, Park JH, Jane JL, Miyao A, Hirochika H, Nakamura Y: Characterization of SSIIIa-deficient mutants of rice: the function of SSIIIa and pleiotropic effects by SSIIIa deficiency in the rice endosperm. Plant Physiol. 2007, 144 (4): 2009-2023. 10.1104/pp.107.102533.PubMed CentralView ArticlePubMedGoogle Scholar
- Fu FF, Xue HW: Co-expression analysis identifies Rice Starch Regulator1 (RSR1), a rice AP2/EREBP family transcription factor, as a novel rice starch biosynthesis regulator. Plant Physiol. 2010, 154: 927-938. 10.1104/pp.110.159517.PubMed CentralView ArticlePubMedGoogle Scholar
- Perez S, Bertoft E: The molecular structures of starch components and their contribution to the architecture of starch granules: A comprehensive review. Stärke. 2010, 62 (8): 389-420.View ArticleGoogle Scholar
- Yamakawa H, Hirose T, Kuroda M, Yamaguchi T: Comprehensive expression profiling of rice grain filling-related genes under high temperature using DNA microarray. Plant Physiol. 2007, 144: 258-277. 10.1104/pp.107.098665.PubMed CentralView ArticlePubMedGoogle Scholar
- Tamaki M, Kurita S, Toyomaru M, Itani T, Tsuchiya T, Aramaki I, Okuda M: Difference in the Physical Properties of White-Core and Non-White-Core Kernels of the Rice Varieties for Sake Brewing is Unrelated to Starch Properties. Plant Production Science. 2006, 9: 78-82. 10.1626/pps.9.78.View ArticleGoogle Scholar
- Bonneau L, Carré M, Martin-Tanguy J: Polyamines and related enzymes in rice seeds differing in germination potential. Plant Growth Regul. 1994, 15: 75-82. 10.1007/BF00024679.View ArticleGoogle Scholar
- Walden R, Cordeiro A, Tiburcio AF: Polyamines: small molecules triggering pathways in plant growth and development. Plant Physiol. 1997, 113 (4): 1009-1013. 10.1104/pp.113.4.1009.PubMed CentralView ArticlePubMedGoogle Scholar
- Wakasa K, Hasegawa H, Nemoto H, Matsuda F, Miyazawa H, Tozawa Y, Morino K, Komatsu A, Yamada T, Terakawa T, Miyagawa H: High-level tryptophan accumulation in seeds of transgenic rice and its limited effects on agronomic traits and seed metabolite profile. J Exp Bot. 2006, 57 (12): 3069-3078. 10.1093/jxb/erl068.View ArticlePubMedGoogle Scholar
- Sabelli PA, Larkins BA: The development of endosperm in grasses. Plant Physiol. 2009, 149: 14-26. 10.1104/pp.108.129437.PubMed CentralView ArticlePubMedGoogle Scholar
- Lee S, Jeon US, Lee SJ, Kim YK, Persson DP, Husted S, Schjørring JK, Kakei Y, Masuda H, Nishizawa NK, An G: Iron fortification of rice seeds through activation of the nicotianamine synthase gene. Proc Natl Acad Sci USA. 2009, 106 (51): 22014-22019. 10.1073/pnas.0910950106.PubMed CentralView ArticlePubMedGoogle Scholar
- Salekdeh GH, Reynolds M, Bennett J, Boyer J: Conceptual framework for drought phenotyping during molecular breeding. Trends Plant Sci. 2009, 14 (9): 488-496. 10.1016/j.tplants.2009.07.007.View ArticlePubMedGoogle Scholar
- Garcia O, Saveanu C, Cline M, Fromont-Racine M, Jacquier A, Schwikowski B, Aittokallio T: GOlorize: a Cytoscape plug-in for network visualization with Gene Ontology-based layout and coloring. Bioinformatics. 2007, 23 (3): 394-396. 10.1093/bioinformatics/btl605.View ArticlePubMedGoogle Scholar
- Stacklies W, Redestig H, Scholz M, Walther D, Selbig J: pcaMethods - a Bioconductor package providing PCA methods for incomplete data. Bioinformatics. 2007, 23 (9): 1164-1167. 10.1093/bioinformatics/btm069.View ArticlePubMedGoogle Scholar
- Urbanek S: multicore: Parallel processing of R code on machines with multiple cores or CPUs. 2011, [R package version 0.1-3]Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.