Single-base resolution maps of cultivated and wild rice methylomes and regulatory roles of DNA methylation in plant gene expression
- Xin Li1, 2,
- Jingde Zhu3, 4,
- Fengyi Hu5,
- Song Ge6,
- Mingzhi Ye2,
- Hui Xiang1,
- Guojie Zhang1, 2,
- Xiaoming Zheng6,
- Hongyu Zhang3,
- Shilai Zhang5,
- Qiong Li5,
- Ruibang Luo2, 7,
- Chang Yu2,
- Jian Yu3,
- Jingfeng Sun3,
- Xiaoyu Zou3,
- Xiaofeng Cao8,
- Xianfa Xie9Email author,
- Jun Wang2, 10Email author and
- Wen Wang1Email author
© Li et al.; licensee BioMed Central Ltd. 2012
Received: 18 October 2011
Accepted: 29 May 2012
Published: 2 July 2012
DNA methylation plays important biological roles in plants and animals. To examine the rice genomic methylation landscape and assess its functional significance, we generated single-base resolution DNA methylome maps for Asian cultivated rice Oryza sativa ssp. japonica, indica and their wild relatives, Oryza rufipogon and Oryza nivara.
The overall methylation level of rice genomes is four times higher than that of Arabidopsis. Consistent with the results reported for Arabidopsis, methylation in promoters represses gene expression while gene-body methylation generally appears to be positively associated with gene expression. Interestingly, we discovered that methylation in gene transcriptional termination regions (TTRs) can significantly repress gene expression, and the effect is even stronger than that of promoter methylation. Through integrated analysis of genomic, DNA methylomic and transcriptomic differences between cultivated and wild rice, we found that primary DNA sequence divergence is the major determinant of methylational differences at the whole genome level, but DNA methylational difference alone can only account for limited gene expression variation between the cultivated and wild rice. Furthermore, we identified a number of genes with significant difference in methylation level between the wild and cultivated rice.
The single-base resolution methylomes of rice obtained in this study have not only broadened our understanding of the mechanism and function of DNA methylation in plant genomes, but also provided valuable data for future studies of rice epigenetics and the epigenetic differentiation between wild and cultivated rice.
KeywordsCultivated and wild rice Methylomes Transcriptional termination regions (TTRs) Gene expression
DNA methylation is an epigenetic modification mechanism that plays essential roles in diverse biological processes . It has also been proposed to be an alternative inheritance system playing an important role in evolution [2, 3], as many case studies in plants and animals have revealed that differentially methylated alleles could create heritable phenotypic changes across generations [4–10], including some agronomically important traits in rice . Recently, single-base resolution methylome maps of a dicot plant (Arabidopsis thaliana), human and silkworm have been successfully generated by whole-genome sequencing bisulfite-treated genomic DNA using next-generation sequencing technology (BS-Seq), which revealed more elaborate patterns and functional effects of DNA methylation at the whole-genome level [12–15].
Rice is not only one of the most important crops as the primary food source for more than half of the world’s population, but also an important model system for the evolutionary study of cereals and the molecular study of monocot plants. DNA methylation serves various important functions and thus has been of great interest to rice geneticists and breeders. Pioneer studies of epigenetic modifications in rice, including DNA and histone methylation, using traditional methylated DNA enrichment method suggested possible functional roles of DNA methylation in rice [16, 17], but this approach is difficult to discriminate major genomic elements including promoters, gene bodies, transposons, and repeats. Recently two other studies comparing methylation patterns among many species using BS-Seq technology briefly reported genome methylation patterns for japonica rice strain Nipponbare[18, 19]. However, the two Nipponbare methylomes had relatively low sequencing coverage (< 4× per base for each strand). Furthermore, one of the studies  used different tissues to obtain methylomic (from leaves) and transcriptomic (from shoots) profiles and the other did not obtain gene expression data at all, which made it difficult to accurately analyze the regulatory effects of DNA methylation in rice. As acknowledged by Feng et al. , such a low genome-wide sequencing depth permitted good assessment of the level of methylation of major genomic elements including genes, transposons, and repeats, but was not sufficient for quantifing the methylation level at individual cytosines. Furthermore, to what extent and how the cultivated rice has evolved divergent DNA methylation pattern from its wild relative species still need to be addressed.
In this study, we generated single-base-resolution DNA methylomic maps as well as transcriptomic profiles for young panicles of the two Asian cultivated rice subspecies, Oryza sativa ssp. japonica and O. sativa ssp. indica, and their wild relatives, Oryza rufipogon and Oryza nivara. The panicle is an important organ showing strong differentiation between cultivated and wild rice and directly affects the major yield components including the number of spikelets and the percentage of filled grains . The high-resolution DNA methylomes of cultivated and domestic rice will not only serve as references for future molecular studies of rice epigenetics but also shed new lights into epigenetic mechanisms of plant domestication.
Results and discussions
Methylation landscapes in rice
To investigate the general methylation patterns of rice as well as the DNA methylation divergence between cultivated and wild rice, we included in our samples both subspecies of Asian cultivated rice, Oryza sativa spp. japonica (represented by Dianjingyou1, bred by the Yunnan Academy of Agricultural Sciences, China, which is a typical japonica rice type mainly suitable to be planted in Yunnan) and indica (IR64, from the International Rice Research Institute, IRRI), and their wild relatives, Oryza rufipogon (Accession 105327, originally collected from Sri Lanka and provided by IRRI) and Oryza nivara (Accession 105426, originally collected from India and provided by IRRI).
Data description of BS-Seq reads for the four rice samples
Raw reads number/data production (Gb)
Effective reads number/data production (Gb)
Average read depth per base per strand
Conversion rate and methylation pattern for the four rice samples
error rate *
Average methylation level of all cytosines
Average methylation level of all cytosines
Average methylation level of all cytosines
Average methylation level of all cytosines
Average methylation level of all cytosines
Difference of methylation landscapes between rice and A. thaliana
In addition, we found a positive correlation between sequence length and methylation density for genes (Figure 2i), but not for TEs (Figure 2j), which is different from the case in A. thaliana wherein both genes and TEs showed positive correlation between sequence length and methylation . This might be due to the fact that TEs in rice genome are almost saturated with mCs regardless of their length. All the above results are consistently found in all the four rice samples (see Additional files 1 and 2 for results of other samples), showing these are general patterns in both cultivated and wild rice. These differences between rice and Arabidopsis suggest that differences in genomic organization and TE composition among plants could result in different epigenomic landscapes.
Regulatory roles of promoter and gene-body DNA methylation in gene expression
DGE data description for the four rice samples
Raw/distinct tag number
Total/distinct tag number used in analysis
Genes with CATG sites
Tags with perfect match to unique gene
Genes with perfect match to unique tags
Although some previous studies have also explored the relationship between DNA methylation and gene expression in rice [16, 18], their low resolution of genome-wide methylated cytokines (mCs)  or utilization of different tissues in generating transcriptomic and methylomic profiles  created the need for more elaborate methylomic studies to comprehensively unveil high resolution rice methylomes and detailed functional effects of rice DNA methylation. Here we shall mainly use the japonica data to present the detailed patterns in rice, which are similar in all four samples (see Additional file 3 for results of the other three samples).
In contrast to promoter methylation, gene-body methylation generally appears to be positively correlated to gene expression as body-methylated genes have significantly higher expression level than body-unmethylated genes (p < 2.2e-16, Wilcoxon rank sum test). However, further analysis revealed complicated relationships between gene-body methylation and gene expression. At first gene expression levels increase with methylation levels, but after a certain point, heavy gene-body methylation appears to repress gene expression; and consequently genes with moderate levels of body methylation tend to have the highest expression levels (Figure 4b, see Additional file 4 for each sequence context). These observations are consistent with previous studies in A. thaliana[25, 27] and in rice . It has been proposed that gene-body methylation can prevent transcriptional initiation from cryptic sites within genes but at the cost of impeding transcriptional elongation . This trade-off may have led to the observation that moderately body-methylated genes have the highest level of expression.
Methylation in transcriptional termination region (TTR) can significantly repress gene expression
Most interestingly, our study revealed that methylation in the transcriptional termination region (TTR) is also highly correlated with gene expression. In a pattern similar to promoter methylation, the TTR-unmethylated genes are expressed at significantly higher level than TTR-methylated genes (p < 2.2e-16, Wilcoxon rank sum test). Moreover, an approximately monotonic negative correlation exists between TTR methylation and gene expression (Figure 4c, see Additional file 4 for each sequence context). Surprisingly, the correlation coefficient is even higher than that of promoters, especially for CG methylation (Figure 4d), suggesting that TTR methylation may play an even more important role in gene expression regulation than promoter methylation. To exclude the possibility that the negative correlation between TTR methylation and gene expression is an indirect effect caused by promoter methylation if promoter-methylated genes are also prone to have methylated TTRs, we repeated the correlation analysis for both TTR and promoter regions using genes without promoter methylation and those without TTR methylation respectively. The strong negative correlation between TTR methylation and gene expression and the higher correlation coefficients than that for promoter methylation could still be observed (Figure 4f and 4 g).
DNA methylation, together with associated histone modification, has been suggested to influence the binding of RNA polymerase to DNA and thus affect the initiation, elongation and termination of the gene transcription process . It has been established that promoter methylation can repress gene expression and promoter hypomethylation may be required for genes to express efficiently [1, 25]. Gene-body methylation has also been proposed to inhibit transcriptional noise in actively transcribed genes and consequently body-methylated genes usually show moderate to high level of expression [22, 27, 28]. It is plausible that TTR methylation could have significant effect on gene expression through interfering with transcriptional termination. Consistent with this hypothesis, active DNA demethylation mediated by the DEMETER (DME) family has been found primarily at both the 5’- and 3’- ends of genes in A. thaliana, suggesting a functional role of methylation in both regions [29, 30].
To further investigate whether such a regulatory mechanism is shared by plants, we also examined the effects of TTR methylation on gene expression in Arabidopsis using existing data . Consistent with the results from rice, our analysis revealed significant negative correlation between TTR methylation and gene expression in Arabidopsis (Figure 4e), suggesting a general regulatory role of TTR methylation both in monocot and dicot plants.
In addition, we found that the 5’-end of gene coding region is another important regulatory region showing significant positive correlation between its methylation and gene expression in Arabidopsis, but not in rice (Figure 4d and 4e), consistent with the Arabidopsis-specific CHH methylation enrichment in this region. Whether this is a dicot-specific regulation mechanism needs further studies using more plant species of both monocots and dicots. It is worth noting that the positive correlation between the 5’-end gene coding region methylation and gene expression in Arabidopsis could be revealed based on both the absolute (total methylation level of mCs divided by sequence length) and the relative (total methylation level of mCs divided by total number of cytosine sites in a region) methylation levels, but the correspondent CHH methylation enrichment in Arabidopsis could only be revealed using the absolute methylation level (Additional file 5). This may explain the failure of previous studies on Arabidopsis to reveal the positive effect of 5’-end gene methylation on gene expression and suggests absolute methylation level may be better than relative methylation level to affect gene expression.
Methylome comparison between wild and cultivated rice
However, the topology of the gene expression tree is different from both genomic DNA and methylation-based trees, with the two cultivated rice subspecies tightly clustering together and the two wild rice species being most similar to each other, a pattern consistent with the phenotypic relationships among the four samples. The genome-wide gene expression divergence of panicles among the four rice subspecies/species obviously departs from the expectation under the neutral evolution model that posits that gene expression differentiation positively correlates with species’ genetic divergence . Instead, it suggests rice domestication may have occurred through changes in a limited number of genes that have pleiotropic and/or cascading effects on gene expression at the whole-genome level. With considerable gene expressional and phenotypic divergence between wild and cultivated rice, the panicle might have been an major target for artificial selection during domestication, a hypothesis consistent with the finding that many yield-related traits are associated with panicles .
The above results, i.e., the CHH methylation tree’s inconsistency with the genomic tree and the low correlation between non-CG methylation divergence and genetic divergence, imply that high-level methylation in CG context might be more conserved than low-level methylation in CHG and CHH contexts among species. To further test this hypothesis, we calculated the coefficient of variation (CV) using a sliding window of the methylation level for each of the CG, CHG and CHH context to measure their conservation levels among species. Our results show that CG methylation has the lowest variation (8.6%), followed by CHG (11.0%) and CHH (13.7%), consistent with the decrease of their methylation level in the same order. In addition, we calculated the correlation coefficient between methylation level and its CV among four samples in gene regions for each sequence context. We found that gene methylation level showed significant negative correlation with its CV regardless of sequence contexts (Figure 5d). These results suggested that high-level methylation states are more stable during evolution.
To examine the methylation variation among species in different functional elements, we calculated the CVs of methylation level for mCs located in gene, promoter, TTR and TE regions (Additional file 6). We found that TEs’ methylation status is most conserved among species with the smallest CV, followed by genes, promoters and TTRs, consistent with the previous observation in Arabidopsis. The methylation conservation levels of different functional elements also follow their methylation levels, again suggesting that high-level methylation states are more stable among species. Further, we compared the methylation variations among different location and sequence contexts in the same methylation level groups. We found that mCs with similar methylation levels have similar variations regardless of their genomic location or sequence contexts (Additional file 7), demonstrating that methylation level is a consistent indicator of methylation variation among species.
Identification of differentially-methylated genes between cultivated rice and wild rice
To identify the DNA methylation changes that may be associated with rice domestication, we identified in cultivated rice 14/24/49 methylation-upregulated and 21/10/46 methylation-downregulated genes in promoter, TTR and gene body regions respectively, leading to a total of 155 non-redundant differentially-methylated genes between cultivated and wild rice (Additional file 8). We also picked 6 promoter or TTR regions in total to validate such methylation differentiation between cultivated and wild rice using the traditional bisulfite sequencing method for single genes, and the results conform to those from our whole genome BS-Seq analyses (Additional files 910111213,and 14), indicating the reliability of our BS-Seq results. Among the 155 genes, 11 (7.1%) show methylation-correlated 2-fold gene expression changes, but the proportion of genes with such expressional changes among differentially methylated genes is not significantly different from the proportion of all genes showing 2-fold gene expression changes between cultivated and wild rice regardless whether there is DNA methylation difference. Interestingly, a similar conclusion has also been drawn among natural accessions of Arabidopsis thaliana in which only 6% of differentially methylated genes have significantly different expression levels between ecotypes, and the proportion of expression-altered genes is the same as that among all genes . The results from both rice and Arabidopsis suggest that a variety of mechanisms, including genetic changes in genes’ cis- or trans- regulators and chromatin modification, together with DNA methylation, regulate gene expression at the genomic level. However, it is also possible that subtle (less than two-fold) changes in gene expression caused by DNA methylation differences could have important consequences, particularly if those genes are of major or multiple effects. In the case of rice domestication, therefore, it still cannot be ruled out that a few key genes’ epigenetic and correlated expressional changes might have played important roles in the production of some important agronomic traits in cultivated rice . Given the small sample size in this study, methylomic and transcriptomic analysis of more representative wild and cultivated rice is needed to further clarify this important issue.
The high resolution DNA methylation maps for the two cultivated rice subspecies and their wild ancestors obtained in this study and the integrated analysis of genomic, epigenomic, and transcriptomic data have not only broadened our understanding of the mechanisms of gene regulation and the complicated relationships between DNA divergence, DNA methylation, and gene expression variation in plant genomes, but also provided valuable data for future studies on rice epigenetics as well as on epigenetic differentiation between wild and cultivated rice.
BS-Seq libraries construction and sequencing
To make our methylomes from four samples comparable, we carefully collected all samples at the same developmental stage of panicle initiation to booting. This stage is an important time point, at which rice start transitioning from vegetative growth to reproductive growth.
A single young panicle from each of the cultivated rice subspecies and the two wild rice species was ground in liquid nitrogen to fine powder using mortar and pestle. Genomic DNAs were isolated using the Plant Genomic DNA Purification Kit (Tiangen Inc., China) and total RNAs were isolated using the RNeasy Plant Mini Kit (Qiagen Inc., Germany). DNA was fragmented by sonication with the Diagenome sonicator to a mean size of approximately 250 bp, followed by blunting, 3’-end addition of dA, and adaptor ligation according to the manufacturer’s instruction (Illumina). The bisulfite conversion of rice DNA was carried out using a modified (NH4)HSO3-based protocol . Bisulfite-treated DNAs were PCR amplified with 16 cycles. The resultant DNAs were applied to paired-end sequencing with the read length of 44 or 75 nt for each end using the ultrahigh-throughput Illumina Genetic Analyzer (GA 2) as per the manufacturer’s instructions.
Mapping and processing of BS-Seq reads
Because japonica rice has high quality reference genome sequence and gene annotation information, all reads from four rice samples were mapped to the Nipponbare IRGSP genome sequence (build 4 assembly), which was downloaded from RAP-DB (http://rapdblegacy.dna.affrc.go.jp/archive/build4/OsGenome_RAP2.tar.gz). Since DNA methylation has strand specificity, the plus strand and the minus strand of Nipponbare genome should be separated and used as different alignment target sequences for BS-Seq reads. That is, each cytosine in reference genome sequences was converted to thymine, termed T-genome which represents the plus strand. Meanwhile, each guanine in reference genome sequences was converted to adenosine, termed A-genome which represents the minus strand. To map the raw 44 or 75 nt pair-ended BS-Seq reads, the original reads were computationally converted to the alignment forms with the following steps: 1) observed cytosines on the forward read of each read pair were in silico replaced by thymines; 2) observed guanines on the reverse read of each read pair were in silico replaced by adenosines. The converted reads were then mapped to both strands of the A- and T- genome sequence using SOAP2 allowing up to two mismatches for 44 nt reads and four mismatches for 75 nt reads . Reads mapped to the same start position for both ends were regarded as clonal duplicates, which might have been generated during PCR process, and only one of them was kept. Only reads uniquely mapped to either of the strands were then retained for further analysis. After above filtration, for methylcytosine (mC) detection, we transformed each aligned read and the two strands of the Nipponbare genome back to their original forms to build an alignment between the original forms. Cytosines in the BS-Seq reads matching the corresponding cytosines in the plus strand of the reference genome, or guanines in the BS-Seq reads matching the corresponding guanines in the minus strand of the reference genome will be regarded as potential mCs. To exclude the false positive caused by base calling process, we removed those potential mCs with Q scores lower than 20, which means that a base is correctly called at more than 99% probability, a highly conservative criterion for calling reliable bases.
We used the unmethylated chloroplast genome  to calculate the sum of non-conversion rate and T-C sequencing error rate, and then conducted binomial tests using these values with false positive rate below 5% to exclude those mCs that may be the results of non-conversion of cytosines during our bisulfite treatment or T to C sequencing errors during base calling process.
Comparison of mC calling between our pipeline and Bismark
Called by both
Called only by Bismark
Called only by our method
SNPs calling using BS-Seq reads
Because DNA methylation has strand specificity, BS-Seq reads mapped to T-genome contain the DNA sequence information of plus strand, while reads mapped to A-genome contain the information of minus strand. Thus we could use BS-Seq reads to call SNPs directly for both plus and minus strands for each sample. After mapping and processing of BS-Seq reads described in above step, SNPs in both strands were called using Samtools . To get high-quality SNPs, we used strict standards for our results—only bases with base quality (Q score) of at least 30 and positions covered by at least 5 reads were used for our SNP calling process. Although bisulfite treatment will convert C to T in both strands which will affect judgment of some kinds of genotypes during SNPs calling process, we can use SNP information from both strands to solve this problem. Some genotypes (A/G, G/A, C/A, C/G, T/A, T/G, A/A, G/G, the former indicates reference base and latter indicates sample’s base) can be correctly called using results from plus strand, while the other genotypes (A/C, A/T, G/T, G/C, C/T, T/C, T/T, C/C) can be correctly called using results from minus strand. Under this principle, a total of 37,141, 50,340, 65,680, and 83,594 high-quality SNPs in japonica indica O. rufipogon, and O. nivara were obtained respectively and used for further analysis.
Validation of BS-Seq results
To verify the BS-Seq results, we picked 6 regions, including 4 promoter regions (Os12g0264800, Os01g0116800, Os01g0543000, and Os04g0431700 genes) and 2 TTR regions (Os11g0111101 and Os08g0150200 genes), to validate the methylation status in all the four samples using the traditional bisulfite sequencing method for single genes.
Digital Gene Expression (DGE) tag libraries construction
DGE-tag libraries were constructed from the total RNAs isolated from the same four rice panicles used for extracting DNA with the DGE-Tag Profiling NlaIII Sample Prep Kit (Illumina) according to the manufacturer’s instructions.
Mapping and processing of Digital Gene Expression (DGE) tags
Full-length cDNA sequences of rice genes were downloaded from RAP-DB (http://rapdblegacy.dna.affrc.go.jp/archive/build4/rep_RAP2.tar.gz). A total of 24,955 genes supported with full length cDNA were used for DGE analysis. All possible CATG + 17nt tag sequences were created from 24,955 full-length cDNAs and used as the reference tag database. Unique tag sequences and their numbers were extracted from our raw DGE tags and these tags were aligned against the reference tag database using SOAP . Only perfect matches were kept for further analysis. Within genes, most of the DGE tags were mapped to the most 3’-end CATG sites of genes (Additional file 15), suggesting that transcriptional termination sites of most rice genes we used were annotated correctly and that performing DGE analysis for those genes was suitable and reliable. The expression level of a gene was represented by the total number of tags that uniquely aligned to that gene. Gene expression levels were normalized to tag number per million tags for gene expression comparisons among different samples.
Methylation level, TE and smRNA density analyses
Annotation of TEs was downloaded from RAP-DB (http://rapdblegacy.dna.affrc.go.jp/archive/build4/OsNIAS_b4_chromOut.tar.gz) and smRNA sequences were downloaded from rice MPSS database (http://mpss.udel.edu/data-files/rice/small/smallRNA_summary.txt). Sequences of smRNAs were mapped to the rice reference genome using SOAP  without allowing mismatch, and uniquely mapped smRNAs were used for further analysis. Methylation level refers to the proportion of reads showing mC among all reads covering the same cytosine site. It can further be classified as absolute methylation level (total methylation level of mCs divided by the total sequence length of the calculated region) and relative methylation level (total methylation level of mCs divided by total number of cytosine sites in the calculated region), both of which were used for our analysis. TE or smRNA density was defined as the ratio of the number of bases belonging to TEs or smRNAs to the total length of the calculated region.
Methylation-expression correlation analysis for Arabidopsis
The single-base methylation profile and corresponding gene expression profile of Arabidopsis were downloaded from NCBI SRA (Sequence Read Archive) database (http://www.ncbi.nlm.nih.gov/sra, the accession numbers are SRA000284 for BS-Seq, also referred as MethylC-Seq in Lister et. al.’s paper , and SRA000286 for mRNA-seq). The genome sequences and gene annotation information (TAIR9) were downloaded from the ftp site of TAIR (The Arabidopsis Information Resource, ftp://ftp.arabidopsis.org/home/tair/). The method of mapping and processing of BS-Seq reads is the same as that used in rice. Mapping and processing of mRNA-seq reads were conducted using TopHat software . Gene expression levels, measured by reads per kilobase of transcript per million reads (RPKM), was also calculated using TopHat. Methylation-expression correlation analyses for Arabidopsis were performed with the same methods as used for rice.
Construction of methylation, genomic and expression trees
For methylation tree construction, we first calculated the absolute methylation level in sliding windows of 50 kb with steps of 25 kb across the whole genome for the four samples, and then clustered the samples based on pairwise Spearman correlation coefficients estimated from the above whole genome sliding methylation level matrix. Methylation levels of all mCs with ≥ 5 × coverage of the whole genome were also used to calculate the pairwise Spearman correlation coefficients among samples, which in turn produced similar methylation tree. High-quality SNPs called from BS-Seq reads were used to construct genomic tree among the four samples using p distance and neighbor-joining method implemented in MEGA [41, 42]. Finally, an expression tree was constructed using pairwise Spearman expression correlation coefficients among the four samples based on DGE data. Methylation and expression trees were constructed using the hclust function of R statistical software (http://www.r-project.org/).
Examination of the relationship between genetic divergence and methylation divergence
To examine the relationship between genetic and methylation divergence, the average number of nucleotide differences per site among samples for different genomic regions was calculated using SNPs obtained from the above step with 50 kb sliding window and a step of 25 kb through the whole genome, which was used to measure genetic divergence. Then average Spearman correlation coefficients of methylation level of all cytosines with ≥ 5 × coverage among samples were calculated for same sliding windows and used to measure methylation divergence in different genomic regions.
Identification of differentially methylated genes between cultivated and wild rice
Because mCs in promoters, TTRs and gene bodies have significant effects on gene expression, we identified genes with different methylation status in these three regions among the four rice samples. Only regions with above 80% sequencing coverage were used for further analysis. Methylation levels of all Cs within promoters/TTRs/gene-bodies were calculated and used to perform two-sample Wilcoxon rank sum tests between any samples. Differentially-methylated genes were identified for each pairwise comparison using a significance level of alpha < 0.05. Genes that are significantly methylation-upregulated or methylation-downregulated consistently in all cultivated vs. wild rice pairwise comparisons but are not significantly different within cultivated or wild rice comparisons were respectively identified as methylation-upregulated or methylation-downregulated genes between cultivated and wild rice.
The methylome data have been deposited into the NCBI Short Read Archive (SRA, http://www.ncbi.nlm.nih.gov/sra/) under accession number SRA012190 and DGE data have been deposited into the NCBI Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo) under accession number GSE20871.
transcriptional termination regions
bisulfite-treated genomic DNA sequencing
Digital Gene Expression tag profiling
Serial Analysis of Gene Expression
nonredundant full-length cDNAs
coefficient of variation.
We are grateful to Keyan Zhao, Haiyi Wang, Eric Richards, and Peter Lipke for their helpful discussions and comments on the work, and to the International Rice Research Institute (Los Banos, Philippines) for providing the seed samples of the two wild rice species. This work was supported by the Chinese 973 program (2007CB815700, 2005CB121000, 2009CB825606 and 2009CB825607), the Provincial Key Grant of Yunnan Province (2008CC017; 2008GA002), the National Natural Science Foundation of China (U0836605, 30921140312 and 30872963), the Shenzhen Municipal Government and the Yantian District local government of Shenzhen, the Ole Rømer grant from the Danish Natural Science Research Council, a CAS-Max Planck Society Fellowship, the 100 talent program of CAS, a Shanghai Science Foundation grant (07DJ14074), and a European 6th program grant (LSHB-CT-2005-019067) to WW, JW, SG or JZ.
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