Environmental Gene Regulatory Influence Networks in Rice

bioRxiv preprint doi: https://doi.org/10.1101/042317; this version posted July 5, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Environmental gene regulatory influence networks in rice (Oryza sativa): response to water deficit, high temperature and agricultural environments Olivia Wilkins1,2,10, Christoph Hafemeister1,10, Anne Plessis1,3, Meisha-Marika Holloway- Phillips4, Gina M. Pham1, Adrienne B. Nicotra4, Glenn B. Gregorio5,6, S.V. Krishna Jagadish5,7, Endang M. Septiningsih5,8, Richard Bonneau1,9,11, Michael Purugganan1,11

1 Department of Biology and Center for Genomics and Systems Biology, New York University, New York, New York, USA, 11225 2 Present address: Department of Plant Science, McGill University, Montréal, Québec, Canada, H9X 3V9 3 Present address: School of Biological Sciences, Plymouth University, Drake Circus, Plymouth, UK 4 Research School of Biology, Australian National University, Canberra, Australian Capital Territory, Australia 5 International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines 6Present address: East-West Seed Company, Sampaloc, San Rafael, Bulacan, Philippines 7Present address: Department of Agronomy, 3706 Throckmorton Plant Sciences Center, Kansas State University, Manhattan, Kansas, 66506 8Present address: Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas, USA 9Simons Center for Data Analysis, Simons Foundation, New York, New York, USA

Contact Information

Richard Bonneau: [email protected] @RichBonneauNYU

Michael Purugganan: [email protected]

Additional Footnotes

10 these authors contributed equally to this work 11 these authors are the senior and corresponding authors

ABSTRACT Environmental Gene Regulatory Influence Networks (EGRINs) coordinate the timing and rate of gene expression in response to environmental and developmental signals. EGRINs encompass many layers of regulation, which culminate in changes in the level of accumulated transcripts. Here we infer EGRINs for the response of five tropical Asian rice cultivars to high temperatures, water deficit, and agricultural field conditions, by systematically integrating time series transcriptome data (720 RNA-seq libraries), patterns of nucleosome-free chromatin (18 ATAC-seq libraries), and the occurrence of known cis-regulatory elements. First, we identify 5,447 putative target genes for 445 transcription factors (TFs) by connecting TFs with genes with known cis-regulatory motifs in nucleosome-free chromatin regions proximal to transcriptional start sites (TSS) of genes. We then use network component analysis to estimate the regulatory activity for these TFs from the expression of these putative target genes. Finally, we inferred an EGRIN using the estimated TFA as the regulator. The EGRIN included regulatory interactions between 4,052 target genes regulated by 113 TFs. We resolved distinct regulatory roles for members of a large TF family, including a putative regulatory connection between abiotic stress and the circadian clock, as well as specific regulatory functions for TFs in the drought response. TFA estimation using network component analysis is an effective way of incorporating multiple genome-scale measurements into network inference and that supplementing data from controlled experimental conditions with data from outdoor field conditions increases the resolution for EGRIN inference.

INTRODUCTION

Plants alter the expression levels of different sets of genes to coordinate physiological and developmental responses to environmental changes (Nagano et al., 2012; Plessis et al., 2015). The ability to respond to environmental signals is the hallmark of adaptation and underlies tolerance to biotic and abiotic stresses. For domesticated crop species, like Asian rice (Oryza sativa) adaptive gene expression patterns associated with environmental changes can ensure high yields under a range of climatic conditions (Mickelbart et al., 2015; Olsen and Wendel, 2013).

Rice is a staple food for more than half of the world’s population (Khush, 2005). Changes in rice yield caused by climate change have major implications for global food security (Pachauri et al., 2014). An estimated 45% of rice growing lands are at risk of drought because they are not irrigated and depend entirely on rainfall for water (Tuong and Bouman, 2003). Moreover, many rice-growing regions have temperatures bordering critical limits for optimal grain production (Peng et al., 2004; Prasad et al., 2006; Wassmann et al., 2009). Current climate models predict marked reductions in rice yield due to changes in the frequency and intensity of extreme climate events (Pachauri et al., 2014; Redfern et al., 2012). Understanding the mechanisms that permit growth in fluctuating environments is a critical step towards identifying the molecular processes that could be targeted through traditional breeding or genetic engineering for developing stress tolerant plants.

Plants rely on gene regulatory networks to orchestrate dynamic adaptive changes that enable them to survive in growth limiting environments. Gene regulatory networks are the core information processing mechanism of the cell; they coordinate the timing and rate of genome-wide gene expression in response to environmental and developmental signals (Bonneau, 2008; Huynh-Thu and Sanguinetti, 2015; Imam et al., 2015; Roy et al., 2013; Schulz et al., 2012). Environmental gene regulatory influence networks (EGRINs) are defined by the environmentally-modulated interactions of protein transcription factors (TFs) with conserved regulatory elements in genomic DNA (Sullivan et al., 2014; Zhang et al., 2015) to effect the organization of the chromosome and the transcription of RNAs, including miRNAs and lncRNAs, that in turn have regulatory potential (Mercer and Mattick, 2013). Plant genomes encode an expanded repertoire of TFs relative to other organisms (Shiu et al., 2005) that is consistent with their sessile lifestyles and their dependence on gene response mechanisms to cope with variable environments.

The need to map out and dissect gene regulatory networks has led to the development of various experimental and computational approaches to infer their structure and composition (Bonneau, 2008; Greenfield et al., 2013; Koryachko et al., 2015; Roy et al., 2013). The simplest large scale EGRINs are based on transcriptome data, measured by high-throughput sequencing or array-based technology, without regard

for other post-transcriptional and translational regulatory events that are known to influence transcriptional regulation (Koryachko et al., 2015). These methods assume that the expression of genes across environmental conditions, perturbations and genotypes can be used to predict regulatory relationships; however, many TF proteins exist in an inactive form in the cytosol or nucleus until they are activated by environmental or developmental signals (Fu et al., 2011; Ohama et al., 2016). It is not feasible to measure all of the complex and varied factors contributing to the regulation of gene expression; as such, network inference algorithms have been developed to predict regulatory interactions in the absence of complete data by incorporating additional complementary data types or prior knowledge of the network structure to estimate the effects of unmeasured regulatory layers (Arrieta-Ortiz et al., 2015; Bonneau, 2008; Fu et al., 2011; Greenfield et al., 2013; Misra and Sriram, 2013; Roy et al., 2013).

In model prokaryotic and eukaryotic systems, where much of the true architecture of many regulatory networks is known, methods that combined expression data and additional data types that define structure priors were able to infer more accurate regulatory networks that methods based on gene expression data alone (Arrieta-Ortiz et al., 2015; Fu et al., 2011; Greenfield et al., 2013). For rice, where only a minority of regulatory relationships are known, several EGRINs have been published that use additional knowledge of network regulators to inform transcriptome-based network inference models. For example, Obertello et al., (2015) included knowledge of regulatory interactions in other species, and Nigam et al., (Nigam et al., 2015) consider microRNA- mediated TF activity. In both of these instances, the additional data types were used to filter and refine co-expression networks. Sullivan et al. (Sullivan et al., 2014) used changes in chromatin accessibility upstream of the coding regions of genes, a hallmark of gene regulatory activity (Buenrostro et al., 2013; Zhang et al., 2015), and knowledge of cis-regulatory motifs, rather than changes in gene expression, to construct a cis- regulatory network of the response of Arabidopsis to high temperature and during photomorphogenesis. Mapping TFs to target genes in this manner considers putative functional elements, but does not capture the output of the regulatory network, specifically, the regulated changes in transcript abundance that expression based networks measure.

Our method combines the strengths of transcriptome and chromatin accessibility based methods in a novel algorithm that systematically incorporates multiple genome scale measurements into a single model of gene regulation. We use a combination of static (i.e. cis-regulatory motif occurrence) and dynamic (i.e. transcriptome and chromatin accessibility data) genome-scale measurements to define the proximal promoter region for all genes and to estimate the regulatory activity of transcription factors. These analyses were used as a starting point for inferring an EGRIN using an adapted version of the Inferelator, a method for learning networks based on sparse linear models (Arrieta-Ortiz et al., 2015; Bonneau et al., 2006; Greenfield et al., 2013). We incorporated time series gene expression data from controlled experiments where single environmental factors were perturbed and from agricultural field experiments collected over multiple growing seasons with experimentally induced stress responses. Our approach overcomes some of the shortcomings implicit in transcriptome based network prediction (e.g. co-expression as a proxy for regulation), by leveraging a multi-factor experimental design. Using this approach, we have assembled the first high-resolution view of global environmental gene regulation in rice.

RESULTS

For this study, we used multiple genome scale measurements – transcriptome, nucleosome-free chromatin, and cis-regulatory motif occurrence – to learn the gene regulatory networks associated with response to environmental change. Briefly, our method for inferring EGRINs was as follows: (1) for each TF with known cis-regulatory elements (CRE), we identified target genes that had the relevant CRE in a region of open chromatin in their promoters; (2) we then used the expression of the putative target genes to estimate the Transcription Factor Activity (TFA) for each transcription factor using a method called Network Component Analysis (Liao et al., 2003); (3) we then inferred the regulatory network using the Inferelator algorithm (Arrieta-Ortiz et al., 2015; Bonneau et al., 2006; Greenfield et al., 2013), in which regulatory interactions are predicted based on (partial) correlations between TFAs and the expression of target genes.

We carried out two major experiments (Figure 1A) in which we exposed rice plants to a wide range of environmental conditions and monitored their functional and transcriptome responses over time. In the first experiment, we grew rice plants in climate controlled growth chambers for 14 days in hydroponic culture before initiating heat shock (a transfer from 30°C to 40°C) or water deficit treatments (removal of all root available water). We collected samples from the heat shock treatment every fifteen minutes for 4h; however, we collected samples from the water deficit treatment every fifteen minutes for only 2h. Beyond 2h of the water deficit treatment we began to see irreversible tissue damage, including leaf tip death, and we stopped the treatment as that response was not of interest for this study. In the second experiment, we grew three rice cultivars in two agricultural fields, one irrigated and one rain fed, during both the rainy and dry seasons and we monitored weather variables throughout the experimental period. Leaf samples were harvested every other day for 29 days during the vegetative growth phase at the same time of day with respect to sunrise. This experiment is described in detail in Plessis et al. (2015). The controlled experimental treatments differed from agricultural stresses in several critical ways: the heat shock was a near-instantaneous 10°C temperature increase and the water deficit treatment exposed the roots of hydroponically grown plants into the air. While these conditions do not replicate field conditions, they allowed us to apply consistent treatments to a large number of plants simultaneously and because they had previously been demonstrated to elicit relevant changes to both plant functional responses, and importantly for our study, to the expression levels of a number of transcripts that were a priori of interest.

We included five tropical Asian rice cultivars in these experiments, all of which were traditional land races including representatives of two of the major rice subspecies – indica (cultivars Kinandang Puti, and Tadukan), and japonica (cultivars Azucena, Pandan Wangi, and Palawan, Figure 1A). These varieties were traditionally used in either irrigated culture (Pandan Wangi and Tadukan) or in rain fed fields (Azucena, Kinandang Puti, Palawan) and their divergent ecological adaptations allow us to capture a greater breadth of responses to the environmental treatments. Our method systematically incorporates multiple genome-scale measurements, including chromatin accessibility, cis- regulatory motifs, and transcriptome data, to generate predictions of gene regulatory

interactions in rice leaves. We interpreted the regulatory network in the context of the plant functional measurements and weather data (Figure 1B).

Photosynthetic rate is decreased in response to environmental stress

To provide the functional context in which gene expression was measured, we monitored plant physiological status during the heat shock and water deficit treatments, including carbon assimilation rate and stomatal conductance. We observed distinct functional responses for each stress type, with similar responses observed for all four cultivars (Supplemental Figure S1) and so, hereafter, we describe the responses of Azucena as an exemplar. In response to the heat treatment, we observed an initial steep and transient decrease in the carbon assimilation rate (~80% control) of Azucena followed by a recovery to a sustained rate of ~90% control for the length of the heat stress treatment (Figure 2A, Supplemental Figure S1). Upon return to the lower temperature, the carbon assimilation rates remained stable, but below the rate in control conditions for the duration of the measured recovery period. The degree of change is variable across the cultivars, but that the trend is consistent across all.

In response to the water deficit treatment, we observed a continuous decrease in rate of carbon assimilation over the period of the treatment. For example, Azucena’s carbon assimilation rate declines to around 90% of its well-watered rate after only 15 minutes of water deficit stress and to around 15% after 90 minutes, the last measurement before the plants were returned to the water-unlimited treatment. The other three cultivars similarly have decreasing rates of carbon assimilation over the period of the water-deficit treatment. In all four rice cultivars, we observe a gradual increase in the carbon assimilation rate when the plants are given ample water for recovery, though none of them returned to pre-treatment rates in the monitored recovery period.

For neither treatment did we observe functional differences that could be associated with the rice subspecies (indica v. japonica) or the type of field (irrigated v. rain fed) in which the rice was traditionally grown. Because the functional measures of the sampled leaves showed partial recovery upon return to the initial experimental conditions, and after four days post-treatment we did not observe leaf mortality on the

stress treated plants, we are confident that the treatments did not cause irreversible damage to the sampled leaves.

Fast responses to heat, slow responses to water deficit

A comparison of the four cultivars indicated that they had similar transcriptomes as shown by the high correlation of the gene expression between cultivars across the genome (Figure 2B, Supplemental Figure S2). The correlation of gene expression was highest within each subspecies (cor=0.98 for japonica cultivars; cor=0.97 for the indica cultivars); correlation between the transcriptomes of different subspecies was also high (cor > 0.95). Given this similarity, there should only be very low bias introduced by having aligned all cultivars to the same Nipponbare reference genome. We identified differentially expressed genes in the controlled chamber experiments in response to single-factor environmental perturbations. Results indicate that the two types of treatments (heat and water deficit) lead to responses with distinct temporal patterns of expression regulation (Figure 2C) that paralleled the leaf functional responses to stress. During the heat treatment, there is a rapid increase in the expression of ~300 genes, but after 90 minutes only few genes remain perturbed (Figure 2C, Supplementary File S1). In contrast, the response to water deficit is much slower, with almost no differentially expressed genes detected before 60 minutes of treatment after which a large number of differentially expressed genes were observed for the remainder of the stress period. Even 90 minutes after the plants were returned to water-unlimited conditions many genes remain perturbed. The dynamics of the stress responses are summarized in a multi dimensional scaling plot based on Euclidean distance of log-fold-change of 2,097 genes that are differentially expressed in at least one condition (Figure 2D, Supplemental Figure S3). We note that, in contrast to the response to heat shock where the response of all four genotypes was similar, the response to the water deficit stress is stronger (as measured by the number of differentially expressed genes) in the Japonica cultivars (Azucena, Pandan Wangi) than in the Indica cultivars (Kinandang Puti, Tadukan); we did not observe these differences in the plant functional measurements. Most of the differentially expressed genes in this analysis were more highly expressed in the treated than in the control conditions. The relatively small number of

down-regulated genes identified in this analysis is consistent with other published abiotic stress studies in Arabidopsis (Kilian et al., 2007; Matsui et al., 2008; Seki et al., 2002) and Rice (Zhou et al., 2007) that applied severe and short term treatments, much like the one we used in this study. This imbalance is characteristic of samples collected soon after the treatment is initiated (within hours). In their 2008 study in Arabidopsis, Matsui et al.(Matsui et al., 2008) found that after 2h of water deprivation, a much larger number of genes were up regulated than down regulated in response to the treatment; whereas, after 10h, the number of up- and down-regulated genes was effectively equivalent. Similarly, the early time points (0.5h, 1h, 3h, 6h) in the osmotic stress treatment in the AtGenExpress Abiotic Stress Dataset(Kilian et al., 2007), had a much larger number of up-regulated than down-regulated genes; at the later time points (12h, 24h), the number of up- and down-regulated genes were approximately equal.

ATAC-seq interrogation of rice leaves under multiple conditions reveals stable accessible regulatory regions

Nucleosome-free regions of the genome are strongly associated with active sites of transcription. We used Assay of Transposase Accessible Chromatin (ATAC)-seq to identify nucleosome-free regions of the genome (Figure 3A). According to our paired- end sequencing results the majority of DNA fragments are short, 55 bps to 65 bps long, and there is an exponential decline in the distribution of longer fragments (Supplemental Figure S4). A fragment is defined as the DNA region bounded by the forward and reverse read. To call chromosomal regions “open”, we count the number of ATAC cut sites (first base of an aligned forward read and first base after an aligned reverse read) in its proximity. We called a base open if more than half of the libraries contained at least one cut site in the 72-bp window centered on the base (Supplemental File S2). These requirements for calling open sites were chosen because of the high conservation of open sites identified across experimental conditions (Supplemental Figures S5 and S6) and because of the relatively low number of reads per ATAC-seq library which led to a low signal to noise ratio. If two open bases are fewer than 72 bps apart, we call all intermediate bases open. Based on this definition, the average open region length was 268 bps, median 206 bps (Figure 3B). In rice, the distance between nucleosome centers

(dyads) ranges from ~175 bps in promoter proximal regions to ~191 bps in intergenic regions (Wu et al., 2014). In the human lymphoblastoid cell line in which the ATAC-seq method was developed, the fragment length distribution had a clear periodicity of ~200 bps, with the largest peak corresponding to the length of a single nucleosome, and multiple smaller peaks corresponding to integer multiples of up to six nucleosomes (Buenrostro et al., 2013). We did not detect peaks with lengths corresponding to multiple nucleosomes in rice, which may reflect the more compact structure of plant promoters. In Arabidopsis, more than half of DNase I hypersensitivity sites within 1Kb of TSS were located within the first 200 bps upstream of the TSS (Zhang et al., 2012a), indicating that the displacement of a single nucleosome may be sufficient to permit regulated gene expression in both rice and Arabidopsis.

Designating open regions in this manner, we identified 29,978 open regions covering ca. 8 Mbp (~2% of the genome). The open regions were distributed through out the genome, in genic and intergenic regions, with a frequency similar to open regions identified using DNase I hypersensitivity sites (Zhang et al., 2012b) (Supplementary Figure S7). As expected nucleosome-free regions were non-randomly distributed in the genome with respect to genomic features. We examined their distribution and calculated the enrichment of bases belonging to open regions for the following features: 500 bps upstream of TSSs, 5’ UTR, coding sequence, exon, intron, 3’ UTR, 500 bps downstream of gene (Figure 3C). We observed an almost 5.6 fold enrichment of bases belonging to open regions in the 500 bp upstream of the TSS of genes and an 8.5 fold enrichment in the 5’ UTRs of genes with 15% of all bases occurring in open regions (Figure 3C). Introns (~0.3 fold) and coding sequences (~0.2 fold) were depleted of open regions.

To get a better idea of where in the promoter region open chromatin was located for rice, we aligned all 56,000 genes in the genome with respect to their TSS. For every base from 1000 bp before the TSS to 500 bp after the TSS, we calculated the fraction of genes that were covered by an open region as described above (Figure 3D). The resulting distribution shows a sharp peak around 50 bp before the TSS where ~15% of all genes have a region of open chromatin. The distribution quickly falls off to both sides almost reaching the background level of 2.1% at -1000 and +500 bp. We used this curve to

define the promoter boundaries of -453 and +137 bp based on the coverage threshold of 4% (more than 4% of genes have open chromatin between -453 and +137 bp) as this was twice the background level. To test the effects of promoter openness on gene expression, we compared the expression of genes with the number of ATAC cut sites that fall within each gene’s promoter (Figure 3E). For this analysis we considered only Azucena growth- chamber samples and summed the ATAC results of all libraries. Of the ~33K genes whose transcripts were not detected (FPKM of 0), 27K had 30 or fewer cut sites in their promoter. In contrast, 74% of the expressed genes had more than 30 cuts (13-fold enrichment, p < 1e-15) and we observed a significant correlation between the number of cuts and expression (Pearson correlation of 0.42).

TF binding motif occurrence in open promoters used to derive priors on network structure

We mapped known TF binding motifs (cis-regulatory motifs) to open chromatin regions in the promoter regions of expressed genes to organize the existing knowledge of putative regulatory interactions. We called this collection of regulatory hypotheses the network prior. For 666 of the more than 1800 TFs in the rice genome (Jin et al., 2014), the cis-regulatory binding motifs have been determined (Matys et al., 2003; Weirauch et al., 2014). We searched for these motifs in open promoter regions defined by the ATAC- seq analysis (Figure 4A), and found significant motif matches for 445 of the TFs. By limiting the search to the open promoter regions we greatly reduced the sequence space for finding motif occurrences, which lowered the number of random motif matches. Open promoter regions made up ~2.3 Mbp (~0.6% of the genome) distributed among ~9,000 genes, compared to ~14.7 Mbp of the promoters of all expressed genes. In this way, we mapped 445 TFs to 5,447 target genes via 77,071 interactions. The median out-degree (the number of regulatory edges initiating from a TF) of TFs with targets was 75; the median in-degree of targets with TFs was 8. For example, the known binding site of Heat Shock Factor A2a (OsHSFA2a - LOC_Os03g53340) is the Heat Shock Element (HSE). Fifty-three expressed genes had the HSE in an open region of their promoter, and were mapped as targets of OsHSFA2a in the open-chromatin network prior (Figure 4B).

We used the expression data to identify interactions in the network prior that were likely to be regulatory, based on coherent expression profiles. This step was critical, as a substantial fraction of interactions in the network prior was not expected to be related to regulatory TF binding in our experimental conditions. To identify and remove non- functional interactions from the network prior, we assumed that for a given TF, true prior targets should show coordinated expression across at least some of our experimental conditions, and that the targets of the TF should be enriched for that particular expression pattern with respect to background. An example output of this step for the OsHSFA2a is shown in Figure 4B, sample order for this heatmap and all other plots is given in Supplemental File S3. Each target gene was given a score indicating our confidence that it was a true target of the TF based on the criteria described above (see Method section for details); genes with low scores were removed from the network prior. This coherence- filtered network prior had 38,137 interactions (a reduction of 51%); 357 TFs were connected with 3,240 target genes (Figure 4C, Supplemental File S4). Of the TFs with targets, the median out-degree was 44; of the targets with TFs, the median in-degree was 5. Some TFs had identical target gene sets in the network prior. This occurred in cases where the TFs were members of large TF families with identical DNA binding motifs. For example, there are 25 HSFs in the rice genome, all of which are predicted to bind to the same HSE. As a consequence, all HSFs have identical targets in the prior network; we group these TFs together as a TF predictor group. A total of 276 TFs formed 62 TF predictor groups (Supplemental File S5). This generated a final network prior of 13,937 interactions, connecting 143 regulators (individual TFs and predictor groups) with 3,240 target genes (Figure 4C).

Transcription factor activity estimation from known regulatory targets

The regulatory activity of a TF can be derived from the changes in the expression of its target genes across experimental conditions. Our method estimates TF activities (TFAs) based on partial knowledge of TF-target relationships – regulatory interactions in the coherence filtered network prior - with the aim of then using the TFAs to learn better TF-target relationships. Based on the prior network and the expression data, we used network component analysis (NCA) (Liao et al., 2003) to estimate TFAs. The principal

idea of NCA is to use a simplified model of transcriptional regulation and treat the known or putative targets of a TF as reporters of its activity. This approach requires at least some known TF targets, hence we estimated the activities only for the 143 regulators with targets in our network prior (Supplementary File S6). Many TFs had similar TFAs in our experimental conditions that could be associated with the different experimental treatments (Figure 5A). The activities indicate that some TFs primarily regulate their target genes in response to only one of the experimental treatments (i.e. heat or water deficit) while others regulate the expression of their targets in response to multiple experimental treatments (e.g. heat and water deficit). Moreover, we identify TFs with similar activities in the controlled experiments, but with divergent responses in the agricultural setting.

Because the network prior was constructed from predicted TF-target interactions based on cis-regulatory motifs determined by in vitro TF binding to protein binding microarrays we anticipated that it could contain many incorrect or irrelevant interactions. Therefore, we investigated the impact that simulated changes in the network prior had on the estimated TFAs. To do so, we subsampled the prior matrix with replacement (keeping only 63% of the interactions on average) 201 times. In this way we obtained 201 TFA estimates for every TF; we called the mean pairwise Pearson correlation ‘TFA stability’ (Supplemental Figure S8, Supplemental File S7). TFA stability ranged from 0.15 (Figure 5B) to greater than 0.96 (Figure 5C). As expected, predictors (TFs and TF predictor groups) with few targets in the network prior (fewer than eight) show low stability. In the remaining set of predictors, we see 64 (52%) with very stable activities (> 0.75), which shows that at least parts of the prior network are self-consistent and estimated activities for those TFs are systematic rather random. For TFs with greater than four targets, there does not appear to be a relationship between the number of targets and stability score (Supplementary Figure S8). We found that for the majority of TFs activities and expression profiles were poorly correlated, with 85% of correlation values between -0.5 and 0.5 (Figure 5D), consistent with the fact that posttranscriptional and posttranslational mechanisms are important for TF activity and localization.

Very few validated regulatory interactions exist for rice; thereby, making it impossible to benchmark accuracy of the estimated TFAs. For OsNAP (LOC_Os03g21060), a transcription factor associated with chlorophyll degradation, nutrient transport and other senescence-related genes, seven direct regulatory targets have been experimentally validated by chromatin immunoprecipitation (ChIP) PCR (Liang et al., 2014). We estimated the TFA of OsNAP from the ChIP-validated regulatory targets (regulatory targets from (Liang et al., 2014) and expression data from the present experiment) and observed a 0.77 correlation with the TFA estimated from our distinct network prior (Figure 5E). While this result increases our confidence in our estimated TFA for OsNAP, there is not sufficient ChIP data available to generalize the concordance between our method for predicting regulatory targets and the gold-standard ChIP data.

EGRIN: a dynamic model of transcriptional regulation in response to environmental changes

In a second step, we used the observed gene expression and the estimated TF activities to infer the EGRIN (Figure 1C). Inference is based on the Inferelator algorithm which was most recently used to infer an experimentally supported model of the Bacillus subtilis transcriptional network (Arrieta-Ortiz et al., 2015). The underlying idea of the method is to model the observed expression of every gene as a linear combination of the activities of a small number of TFs regulating it. For a given target gene, we constructed all models that corresponded to the inclusion and exclusion of TFs from the network prior in addition to those TFs that show a high mutual information with the target. From this large set of models, we select the one with the lowest Bayesian Information Criterion (implementing a trade-off between model complexity and goodness of fit) while slightly favoring models that also agree with the network prior (Greenfield et al., 2013) (see Method section). The inferred network is not a coexpression network, but a network where regulatory interactions are inferred from the activity of transcription factors (estimated from the expression of a subset of putative target genes) and the transcript profiles of target genes. We have evaluated this method in Bacillus subtilis and yeast and have shown that it is robust to false interactions in the prior network and that the inferred

network was consistent with genome-wide expression levels following TF knock-out (Arrieta-Ortiz et al., 2015).

To estimate the error incurred by our EGRIN inference and to better rank regulatory interactions, we bootstrapped the expression data by subsampling from the conditions with replacement. Additionally, to control for the observed variability in TFAs with respect to small changes in our prior network (i.e. TFA stability), we also subsampled the network prior at every bootstrap as described above. Our approach then only chose interactions for the final consensus network if the predictor had a stable activity that predicted the target expression across a broad range of conditions. Given multiple bootstraps we kept those interactions that were present in more than 50% of the bootstrap networks. TFs with low stability were less likely to recall the same regulatory interaction in a large number of the bootstraps and as a consequence were less likely to be assigned target genes in the consensus network. As we increased the number of bootstraps, the network converges to a core set of interactions quickly — after 150 bootstraps more than 98% of the interactions remain stable after adding more bootstraps (Supplemental Figure S9). To obtain our final rice EGRIN, we performed 201 bootstraps. The final EGRIN contains 4,151 nodes (TFs, TF predictor groups and target genes) and 4,498 interactions (Figure 6A, Supplemental Files S8 and S9). Of all predicted interactions, 18% (796) were also in the network prior. TFs regulated between 1 and 355 target genes (Figure 6B); targets genes were regulated by 1 to 3 TFs. TFs that were grouped in the network prior because they had identical targets were included individually in the network inference as potential target genes, but only as one potential regulator.

Known and novel regulatory control of coordinated biological processes The predicted rice regulatory network consists of 113 TFs, around half of those (62) are TF predictor groups that were created by grouping TFs with identical targets in the prior. In total, 4,498 interactions connect the TFs to 4,052 genes. The number of targets for each regulator, and the number of regulators per target genes are shown in Figure 5B and 5C, respectively. Fifteen predictors (11 TFs and 4 TF predictor groups) had more than 100 targets in the EGRIN. For these 15 predictors, 62% to 96% of the

predicted targets were novel, i.e. not in the network prior. Many of the genes in the network were differentially expressed in response to some of our experimental treatments. We marked groups of genes on the inferred network (Figure 6A) based on the treatments in which they were differentially expressed, including heat shock and water deficit. We also marked genes as ‘circadian’ if time of day was a good predictor of their expression in the chamber experiments or as ‘field’ if the field conditions contributed more to the overall variance than the chamber data. This notation reveals that most TFs and TF groups regulate target genes primarily in response to one treatment, while a small number of TFs regulate targets in multiple conditions. For example, TF group 5, which includes two MYB TFs (LOC_Os01g09640 and LOC_Os05g10690), had the largest number of targets of any regulator in the inferred network (355 target genes). These targets are enriched for genes involved in photosynthesis, and many of them are differentially expressed in response to both the heat and water deficit treatments – stresses that altered the photosynthetic rate in our experimental conditions (Figure 2A).

We performed Gene Ontology (GO) term enrichment analyses on the target genes of each regulator and found that the targets of 27 regulators in the network were functionally enriched for a biological process or molecular function (each p < 0.0001, Supplemental File S10). Based on these analyses, we identified regions of the network whose genes were enriched for particular functions and which respond to particular environmental signals. For example, we identified a region of the network that was enriched for genes involved in RNA binding and for ribosomal structural components whose expression varied primarily as a function of the time of day. We also identified regions of the network enriched for genes with functions associated with kinases and transporter functions whose expression varied primarily in response to water deficit. Finally, we also identified regions of the network that were enriched for genes associated with photosynthesis, cell wall biosynthesis, and cellulose synthase functions whose expression vary in response to diverse environmental stimuli.

Recapitulating and extending the known functions of HSFs

Resolving large families of transcription factors with similar binding sites is a critical problem in genome-wide regulatory studies. Here we initially grouped TFs with

identical binding sites, and thus identical targets in the network prior, because their estimated activities were identical following the first step in our procedure (TFA estimation). Although we learned a regulatory model for the control of each TF in large TF groups separately, we had limited resolution to distinguish different outgoing edges for these large TF-family members (the in-degree in our network is an individual TF property and the out degree is instead an aggregate property of the members of the TF group). This limitation suggests that future experiments aimed at investigating TFs in large families with nearly identical binding sites are needed. Thus for the largest TF protein-families we lack the resolution to determine which of the TFs in the predictor group are regulating the expression of the target genes.

For the most well studied predictor group in our network, the 25 Heat Shock Factors (HSFs), we wanted to determine if post hoc we could identify which of the HSFs were the most likely regulators of the inferred target genes. All HSFs were connected to 46 potential target genes in the network prior via the canonical Heat Shock Element (HSE) TTCnnGAAnnTTC. The estimated TFA for the predictor group increases rapidly after the onset of the heat shock treatment, peaking after 30 minutes and then slowly decreasing over the course of the stress period; upon return to the pre-stress temperature, there is a rapid decrease in TFA – lower than the activity in control conditions – that persists for the remainder of the measured time points (Figure 7A top panel). In the EGRIN, the HSF predictor group regulates the expression of 240 target genes. The targets include a number of genes involved in the unfolded protein response, including Heat Shock Proteins (HSPs) – HSP70, HSP110, DnaJ, and DnaK - and other classes of chaperone proteins (T-complex proteins, chaperonins etc.).

While our method is based on the assumption that the expression of a TF will often not be indicative of its activity, TFs whose expression is highly correlated with the group's activity are prime candidates for being the true regulators of the group's targets. We therefore examined the expression profiles of the HSFs and compared them to the TFA for the HSF predictor group (Figure 7A bottom panel). The transcripts of four HSFs were undetectable in all of our samples; the remaining 21 formed three distinct subgroups based on their expression profiles: HSFs in subgroup one were characterized by increased

expression in response to heat and water deficit; HSFs in subgroup two were primarily induced in response to water deficit; and, the expression of the HSFs in subgroup three was not clearly associated with any experimental treatment. The TFA for the HSF predictor group was embedded in subgroup one, suggesting that the true predictor could be found amongst them.

Seven of the ten HSFs in subgroup one are targets of the HSF predictor group in the EGRIN. Three of them have the canonical HSE in their promoter regions, as defined above. OsHSFA2d and OsHSFA6 each have one upstream HSE, and OsHSFA2a has three, indicating that they are potentially regulatory targets of HSFs in addition to their potential role as regulators. The other five HSFs in this group (OsHSFA2c, OsHSFA4b, OsHSFB2b, OsHSFB2c, OsHSFA9) do not have HSE in their promoters, and so are likely not direct targets of HSFs. We reasoned that these HSFs might be the regulators of the inferred target genes of the HSF predictor group. OsHSFA2c is the TF with the highest correlation between transcript abundance and TFA in our entire data set (cor=0.92). Moreover, the binding of OsHSFA2c is temperature regulated in vitro (Mittal et al., 2011) supporting its role as a heat responsive transcriptional regulator acting through the HSE.

The over-expression of OsHSFA7, a member of HSF subgroup two, enhances growth and survival in response to salt stress and water deficit by unknown mechanisms (Liu et al., 2013). Consistent with a role in the water deficit response, we find that the expression of all HSFs in subgroup two were induced in response to water deficit. However, the TFA, which is based on the occurrence of the HSE in open promoters, appears to be exclusively responsive to the high temperature treatment in our data set. We therefore hypothesize that the role of HSFs as regulators of the water deficit response are mediated by an unknown regulatory element other than the canonical HSE.

Dissimilar activity and expression hint at novel functional roles of TFs

The estimated activity of a TF is not typically correlated with the expression of that TF itself; it depends only on the expression of its targets in the network prior and which other regulators are assigned to those targets. As a result, we observe a wide range of relationships between TF expression and TFA specific to each TF. One interesting

example is Early Phytochrome Responsive 1 (EPR1, LOC_Os06g51260), a MYB gene that is a core component of the circadian oscillator (Filichkin et al., 2011) that binds to the canonical Evening Element, AAAATATCT, in vitro (Weirauch et al., 2014). In our data, we estimate that the activity for EPR1 increases over the experimental period in the growth chamber – a 4.5h period – regardless of the experimental treatments across all four genotypes, suggesting that most of its 114 target genes in the network prior were under circadian control as expected (Figure 7B top panel). We examined the expression of these target genes in an independent data set that was designed to monitor gene expression at many time points throughout the day in rice (Nagano et al., 2012) and we found that their expression was indeed oscillating daily (Supplemental Figure S10). In our inferred network, EPR1 is the regulator of 107 target genes, of which 44 were also in the network prior (Figure 6B bottom panel).

The expression of EPR1 is poorly correlated with its estimated TFA (cor=-0.52). In the control and water deficit treatments, the levels of EPR1 expression are relatively unchanging. However, at the onset of the heat stress treatment, there is a rapid and transient increase in EPR1 expression, which returns to the levels of the control condition for the remainder of the heat stress treatment; upon return to the pre-stress temperature, there is a steep and transient repression of EPR1 expression which returns to the level of the control conditions over the course of the recovery period. This expression profile is consistent with the activity of the HSF predictor group, and in fact, EPR1 is a target of the HSF predictor group both in the network prior and in the inferred regulatory network. In Arabidopsis, AtHSFB2b binds to the promoter of another circadian clock component, Pseudoresponse Regulator 7 at a canonical HSE and is required for maintaining accurate circadian clock rhythms in high temperature and salt stress conditions (Kolmos et al., 2014). Notably, the better-characterized EPR1 ortholog in Arabidopsis thaliana also responds to heat stress (Kilian et al., 2007), though its role as a regulator of response to heat has not been investigated. These findings suggest that EPR1 may be an entry point for integrating the heat stress response with the circadian clock. The coordination of stress responses with the circadian clock is thought to be an adaptive strategy to maintain plant growth during periods of stress (Seo and Mas, 2015; Wilkins et al., 2009, 2010)

Dynamic agricultural environments increase resolution of EGRIN

Upon closer examination of the network prior and the inferred network, we found evidence of the additional value of combining growth chamber and field experiments. Because these experiments perturbed different parts of the regulatory network (e.g. different time scales and treatment duration, complexity of environments, age of plants) we were able to expand the network beyond what simply increasing sample size for any one of the two experimental designs would have afforded. By combining them in a single analysis we were able to resolve parts of the network where target genes expression was similar in response to some environmental conditions, but quite different in response others. The bZIP predictor group (TF predictor group 32 in supplementary material) is one example of a predictor where targets in the network prior showed mostly correlated expression in growth chamber data but could be divided into finer groups based on the field data (Figure 7C). Prior target groups 1, 2, and 3 show distinct expression patterns only in the field; the expression of target group 3 is most similar to the estimated activity of this predictor group. The group is composed of three bZIP TFs (LOC_Os02g49560, LOC_Os08g38020, LOC_Os09g29820) that bind to a five-nucleotide motif CACGT. The estimated TFA for this predictor group shows increasing activity in response to the water deficit treatment in the growth chambers and then decreasing activity in the drought recovery period; moreover, based on the data collected in field, the predictor group’s activity increases over the course of the dry season only in the rain fed fields (Figure 7C). We infer 66 targets of this predictor group, including a number of genes involved in cellular protection in response to water deficit: trehalose-6-phospate, three Late Embryogenesis Abundant proteins involved in osmotic stress response, five dehydrin proteins, and three protein phosphatase 2C genes.

DISCUSSION

The aim of this work was to reconstruct an environmental gene regulatory interaction network in rice leaves in response to two of the most important environmental stresses that impact agricultural productivity – high temperature and water deficit. For this purpose, we generated two genome wide data sets: 1) 720 RNA-seq libraries generated from plants grown in heat and water deficit stress experiments in controlled

environments and from plants grown in agricultural field conditions, and 2) the first ATAC-seq data set that identifies open chromatin sites in rice. We combined these data types with the largest available collection of TF binding motifs to generate a network of regulatory hypotheses for 4052 genes regulated by 113 TFs and TF groups. We have previously published our method for incorporating prior knowledge into network inference using Network Component Analysis and the Inferelator algorithm for the prokaryotes E. coli and Bacillus subtilis (Greenfield and Hafemeister et al., 2014; Ortiz and Hafemeister et al., 2015). The present manuscript is the first use of this method to a multicellular eukaryotic organism and differs from our previous projects in several important ways: the rice EGRIN used novel data types (ATAC-seq cis-regulatory motif occurrence) and lower confidence interactions for learning the network prior; moreover, this was the first use of these methods on a eukaryotic organism with a complex genome. In previous publications, many of the true regulatory interactions in the network were known, and the network prior consisted of bona fide regulatory edges, determined using transcription factor binding assays (e.g. ChIP-seq) and genetic lesions. In the present analyses, we developed the network prior by combining two genome-scale measurements – known and predicted cis-regulatory motif occurrence with a map of transposase accessible chromatin – neither of which directly measure the interaction between a protein transcription factor with its cis-regulatory element in. We also developed new methods for filtering out low confidence edges from the prior network, as well as implementing a more rigorous sampling scheme (bootstrapping the expression data and the network prior) to account for the uncertainty in prior interactions. The final inferred network includes regulatory edges that were not present in the network prior, and discounts many “false positive” edges from the network prior. So, while the network prior was based on ATAC-seq and motif occurrence data, the final inferred network was based on estimated TFA and transcriptome (RNA-seq) data. The regulatory edges present in these networks had limited overlap. It was important to permit the network inference algorithm to learn novel regulatory edges and to exclude some edges in the network prior for several reasons. We expect that the network prior would be incomplete because, we do not believe that our ATAC-seq data saturated the open sites in the genome (ie. more open chromatin may exist than we were able to identify in this

experiment), we anticipate that some relevant cis-regulatory motifs occur outside of the promoter region we defined, and many TFs were not associated with known cis- regulatory motifs and so were excluded from our analyses. Moreover, we expect that false positive edges (connections between TFs and target genes in the network prior that are not truly regulatory) will exist in the network prior because not every cis-regulatory motifs occurence has a regulatory role in the genome. By increasing the number of reads in each ATAC-seq library, it may be possible to identify TF footprints in open chromatin regions (Buenrostro et al. 2013). In this case, the network prior could be defined with greater precision. However, it is unclear what the overall consequences this would have on the estimation of TFA or on the inference of the regulatory network.

One of the challenges of engineering agricultural crops with improved stress tolerance is translating experimental advances from controlled conditions in the growth cabinet into the complex and fluctuating environments found in the field. The disjunction between plant performance in the laboratory and in the field has contributed to the slow improvement of crops with improved resistance to abiotic stress in spite of the massive advances in genomic technologies (Bruce et al., 2015). For this reason, our experimental design did not only query the transcriptional response of rice to heat and water deficit in a controlled environment, but also in an agricultural setting. Because field grown plants must incorporate numerous environmental inputs on multiple time scales, analyses of these plants revealed a greater proportion of the underlying regulatory network. However, identifying the underlying environmental change contributing to these regulatory responses is challenging. By including both types of experiments in our analysis, we were able to leverage the complexity of the agricultural field conditions and the specificity of the controlled experiments to interpret the inferred regulatory network. This was particularly the case for TFs whose activities responded to the water deficit treatment in the controlled experiment, as we had somewhat parallel conditions in the agricultural fields. The rain-fed field became drier over the course of the sampling period, particularly during the dry season, contributing to a water deficit field condition.

With our approach we are able to infer target genes only for TFs that have known binding motifs or otherwise proposed targets and so our present network excludes many

genes. While this will have led to gaps and mis-assignments in our network, we can use new data as it becomes available to update our network prior. For example, ATAC-seq may be combined with data of ChIP-seq, DNA methylation patterns, and novel TF binding motifs in more sophisticated ways than shown here (Hoffman et al., 2012). This would increase the resolution of the inferred EGRIN by breaking up predictor groups, as well as reducing the number of indirect interactions that we predict.

The network and source data provided with this work provide a vital starting point for studying and manipulating stress responses in rice. Because gene regulatory networks define plant response to environmental signals, divergence of EGRINs is hypothesized to be an important source of ecotypic diversity (Thompson et al., 2015; Weirauch and Hughes, 2010). In future analyses, it will be possible to incorporate additional genome scale measurements, including variations in the non-coding parts of the genome, and chromatin immunoprecipitation, and by reprogramming regulatory interactions using gene lesions and manipulations at the transcriptional level (Chavez et al., 2015; Konermann et al., 2014; Maeder et al., 2013), to expand the EGRIN. Learning accurate genome scale EGRINs can be used to identify high-priority targets for plant breeding and biotechnology programs, and will permit the study of the evolution of gene networks and their relation to adaptive diversification of plant species in different environments.

Methods

Plant materials and growth conditions

Seeds of the five rice landraces used in this experiment were obtained from the International Rice Research Institute (IRRI): Azucena (AZ; IRGC#328, Japonica), Kinandang Puti (KP; IRGC#44513, Indica), Palawan (PA; IRGC#4020, Japonica), Pandan Wangi (PW; IRGC#35834, Japonica), and Tadukan (TD; IRGC#9804, Indica). AZ, KP, PW and TD were used in the controlled growth chamber experiments; AZ, PW, and PA were used in the field experiments. All experiments were conducted at the International Rice Research Institute in Los Baños, Philippines.

Single factor perturbation experiments were conducted in walk-in growth rooms. Seed dormancy was broken by incubating seeds for five days at 50°C in a dry convection oven. Seeds were germinated in water in the dark for 48h at 30°C and were then sown on hydroponic rafts suspended in 1X Peters solution (J.R. Peters Inc., Allentown, PA). The pH of the growth media was maintained at 5-5.5 throughout. Plants were grown for 14 days in climate-controlled growth chambers (12h days; 30°C/20°C day/night, 300-500 µmol quanta m-2 s-1 at the leaf surface). Relative humidity was maintained between 50- 70%. Seeds and plants for the field experiments were treated as described previously (Plessis et al. 2015).

Experimental treatments

Single factor perturbation experiments were conducted on 14-day-old seedlings. Treatments began precisely 2h after the chamber lights were turned on. Samples were collected every 15 minutes for up to 4.5h for each of five treatments: control, heat shock, recovery from of heat shock, water deficit, recovery from water deficit. Heat shock was initiated by moving the hydroponic rafts to a 40°C climate controlled growth chamber (RH and light intensity as above). After 2h, some plants were returned to the 30°C chamber (heat shock recovery); the remainder were kept in the 40°C chamber for the duration (heat shock). Water deficit was initiated by removing the rafts from the hydroponic media and allowing the roots to air-dry. After 1.5h, some of the plants were returned to hydroponic tanks containing Peters solution (recovery from water deficit); the remainder were kept in tanks without water (water deficit). Each sample comprised the leaves - from 10 cm above the seed - of 16 juvenile plants. Samples were harvested and flash frozen in liquid nitrogen for each treatment, time point, landrace, and replicate. The entire experiment was replicated on two consecutive days yielding two biological replicates for each condition. Samples from field experiments were harvested as described previously (Plessis et al., 2015). Aliquots of these samples were used for the RNA-seq analyses.

Gas exchange

Instantaneous photosynthetic rate and stomatal conductance of the youngest fully expanded leaf was measured using a portable gas analyzer (Li-6400; Licor, Lincoln, NE,

USA). Conditions in the leaf cuvette were set at a saturating light intensity of 1000 µmol quanta m-1s-1 with a target air temperature of 30°C (40°C in the case of the heat shock -1 measurements), reference CO2 concentration of 400µmol mol and humidity of 70%. The measurements were corrected for leaf area. Time 0 for the heat treatment coincides with minimum functional status observed within 15 min of the start of the stress imposition. Further, the break within the trace coincides with the transition from 40 to 30°C where there was instrument instability.

RNA extraction and library preparation

All protocols were conducted as per the manufacturers’ instructions. Total RNA was extracted using RNeasy Mini Kits (Qiagen). RNA quality was determined by gel electrophoresis. Contaminating DNA was removed from the total RNA samples by treatment with Baseline-Zero DNase (Epicentre, Madison, WI, USA), and ribosomal RNA was removed using the Ribo-Zero rRNA Depletion Kit (Epicentre, Madison, WI, USA). Strand-specific RNA-seq libraries were synthesized using the Plant Leaf ScriptSeq Complete Kit (Epicentre, Madison, WI, USA). The libraries were sequenced – either six or eight libraries per lane – using standard methods for paired-end 51 base pair reads, on an Illumina HiSeq 2000 at the New York University GenCore facility. RNA-seq summary statistics are provided in Supplemental File S11.

RNA-seq processing

The reads were aligned to the O. sativa Nipponbare release 7 of the MSU Rice Genome Annotation Project reference (Kawahara et al., 2013) which consists of 373,245,519 base pairs of non-overlapping rice genome sequence from the 12 rice chromosomes. Also included are the sequences for chloroplast (134,525 bp), mitochondrion (490,520 bp), Syngenta pseudomolecule (592,136 bp), and the unanchored BAC pseudomolecule (633,585 bp). The annotation contains 56,143 genes (loci), of which 6,457 had additional alternative splicing isoforms resulting in a total of 66,495 transcripts.

We used Tophat (Kim et al., 2013; Trapnell et al., 2009) version 2.0.6 to align the reads, discarding low-quality alignments (quality score below 1). To count the number of

reads that uniquely mapped to genes we used HTSeq (Anders et al., 2014) version 0.6.1. We compensated for variable sequencing depth between samples using the median-of- ratios method of DESeq2 (Love et al., 2014) version 1.6.3, and further performed a variance stabilizing transformation provided by the same package. We used the normalized count data for downstream analysis. Replicates were averaged, except for differential expression analysis (see below). We removed all genes that had zero counts in more than 90% of the conditions. We also removed all genes that has a coefficient of variation smaller or equal to 0.05. This left us with 25,499 genes.

Differential Expression

For the chamber experiments, we determined differentially expressed (DE) genes using DESeq2 and the raw read counts as reported by HTSeq. Replicates were not averaged and for every cultivar we created a group factor in the design matrix that was treatment and time specific, and accessed the contrasts individually (e.g. "Azucena heat 60min" vs "Azucena control 60min"). This design allowed DESeq2 to estimate gene expression dispersion parameters based on all available samples for a given cultivar. For every gene and cultivar-condition combination this gave us a log-fold-change value and an adjusted p-value. To visualize the similarities and differences between the conditions and the cultivars with respect to differentially expressed genes, we applied Kruskal’s non-metric multidimensional scaling to the fold change matrix. Only genes with at least one cultivar-condition where the adjusted p-value was below 0.0001 and the absolute fold-change was greater than 2 were considered (2097 genes). The distance metric was Euclidean.

ATAC-seq library preparation

We prepared ATAC-seq libraries from leaves at control (30min, 2h, 4h), heat (30min, 2h, 4h), heat-recovery (4h), water deficit (2h), and water deficit-recovery (4h) conditions in an effort to identify the maximum number of open chromatin regions relevant to our experimental conditions. Two biological replicates were prepared for each condition. The second leaves of 2-week old Azucena rice seedlings were harvested and flash frozen in liquid nitrogen. Intact nuclei were isolated using a protocol generously shared with us by Drs. Wenli Zhang and Jiming Jiang (personal communication). Briefly,

ground tissue was suspended in nuclear isolation buffer and washed repeatedly using nuclear wash buffer following a standard nuclear isolation protocol. Chromatin was fragmented and tagged following the standard ATAC-seq protocol (Buenrostro et al., 2015). Libraries were purified using Qiagen MinElute columns before sequencing. Libraries were prepared from control, water deficit and heat-treated plants after 0.5, 2, and 4h. Libraries were sequenced as paired-end 51 base pair reads on an Illumina HiSeq 2500 instrument in the RapidRun mode at the New York University GenCore facility.

ATAC-seq data processing

The average number of reads was 14.8M and the total for all libraries was 266,839,208 reads. We used Bowtie version 2.2.3 to align the reads to the reference genome. The alignment rate was around 92%. For downstream analysis we removed PCR duplicates using samtools rmdup and required alignment quality scores > 30. This step resulted in a marked reduction of reads as many reads originated from redundant regions of the chloroplast genome or from nuclear encoded chloroplast genes. The final number of aligned reads for downstream analysis is 29,312,972 (11% of all reads; 0.078 reads per bp in the reference genome).

To compare the 18 ATAC-seq samples to each other with respect to location and number of ATAC-seq cut sites (first base of an aligned fragment and first base after the fragment), we counted the number of cuts in all non-overlapping windows of 1000 bp in each library. For each pair of libraries, we then calculated Pearson correlations of number of cuts (in log space after adding a pseudo count). Results are shown as Supplementary Figures S5 and S6 illustrating the high reproducibility of the ATAC-seq data, but also how similar the ATAC-seq results are for a given tissue even for samples exposed to different stresses.

In order to define an atlas of accessible regions to be used in network inference we combined the ATAC-seq results from all libraries to maximize the number of identified nucleosome-free regions in the genome relevant to our experimental conditions. To define “open” regions we counted the number of ATAC cut sites that fell into the 72 bp window centered on each base. We considered a base open if its window

contained at least one cut site in more than half of the libraries. If two open bases were less than 72 bp apart, we called all intermediate bases open.

Combining ATAC data and TF motifs as network prior

We used published TF binding motifs and knowledge of open chromatin based on our ATACseq data to generate a prior gene regulatory network for rice. For this, we obtained rice TF binding motifs from the CIS-BP database (Weirauch et al., 2014) dated 2015/05/18, the TRANSFAC Professional database (Matys et al., 2003) version 140805, and manual curation of literature. Since the 5’ UTR upstream gene regions showed a high enrichment for open chromatin, we assumed that cis-regulatory elements have the largest effect. To find relevant motif occurrences, we scanned only the open regions of the rice genome (as determined by ATAC-seq) that were also in the promoter region of a gene (- 453 to +137 bp with respect to transcription start site). We used FIMO (Grant et al., 2011) to find motif matches with a p-value below 1e-4, keeping only the best (lowest p- value) match per motif-gene pair. For every TF we filtered the associated motif matches by adjusting the p-values using Holm’s method (controlling the Family-wise error rate) and only keeping matches with an adjusted p-value of less than 0.01. Any gene with a motif match in the open part of its promoter was then recorded as a target of the current TF.

We observed that different TFs can be associated with different motifs but yet can have almost identical sets of targets in the network prior. To prevent amplification of these miniscule differences (which stem from redundant motifs) in the downstream analysis, we unified the targets of TFs that were less than 5% different (binary distance) and assigned the union of the targets to both.

Removing uninformative edges from the network prior (coherence filtering)

To remove uninformative edges from the network prior, we required an overrepresentation of distinct expression patterns among the prior targets of each TF with respect to all expression patterns observed in the data. We grouped all genes into 160 clusters (square root of 25,499, the number of genes in the data set) to define the set of

expression patterns in our data. We used hierarchical clustering with average linkage and 1 minus Pearson correlation as distance function. Then, for each TF and each expression cluster, we tested whether the TF targets in the network prior were enriched for the members of the cluster. If the Fisher’s exact test p-value was below 0.05 we marked all TF targets in the network prior that were also members of the cluster as high-confidence targets. The above procedure was repeated for 64 bootstraps of the expression data (conditions were sampled with replacement), and only interactions with a high- confidence frequency (coherence score) of at least 50% were kept for the coherence- filtered network prior.

Estimating transcription factor activity

Let X be the matrix of gene expression values, where rows are genes and columns represent experiments/samples. Let P be a matrix of regulatory relationships between TFs (columns) and target genes (rows). The entries in the prior matrix (P, the network prior)

are members of the set {0, 1}. We set Pi,j to one if and only if there is a regulatory interaction between TF j and gene i in the network prior. Auto-regulatory interactions are always set to zero in P. Estimation of TF activities is then based on the following model (Liao et al, 2003; Fu et al, 2011):

!i,j = %∈TFs #i,k$k,j, where the expression of gene i in sample j can be written as the weighted sum of connected TF activities A. In matrix notation this can be written as X = PA, which we solve for the unknown TF activities A. This is an overdetermined system, but we can find Â which minimizes ||P Â − X||2 using the pseudoinverse of P. Special treatment is given to time-series experiments, with the modified model:

) !i,t ( = %∈TFs #i,k$k,t , ' * '

where the expression of gene i at time tn + τ/2 is used to inform the TF activities at time

tn. Here τ is the time shift between TF expression and target expression used when inferring regulatory relationships (see next section). Here we use a smaller time shift of τ/2, because changes in TF activities should be temporarily closer to target gene

expression changes. If there is no expression measurement at time tn + τ/2, we use linear interpolation to fit the values. In cases where there are no known targets for a TF, we

cannot estimate its activity profile, and remove the TF from the set of potential regulators.

Network inference

The main input to the network inference procedure is the expression data X, the estimated TF activity Â, and the known regulatory relationships encoded in the matrix P. The core model is based on the assumption that the expression of a gene i at condition j can be written as linear combination of the activities of the TFs regulating it. Specifically, in the case of steady-state measurements, we assume

!i,j = %∈TFs +i,kÂk,j (1.1). For time-series data, we explicitly model a time-shift between the target gene expression response and the TF activities: ! = + Â (1.2). i,t' %∈TFs i,k k,t'-.

Here, we are modeling the expression of gene i at time tn as the sum of activities at time tn th − τ, where tn is the time of the n measurement in the time-series and τ= 15 minutes is the desired time-shift. In cases where we do not have measurements for Â we use linear k,t'-. interpolation to add missing data points. The goal of our inference procedure is to find a sparse solution to β, i.e. a solution where most entries are zero. The left hand sides of Equation (1.1) and (1.2) are concatenated as response, while the right hand sides are concatenated as design variables. We use our previously described method Bayesian Best Subset Regression (BBSR) (Greenfield et al, 2013) to solve for β. With BBSR we compute all possible regression models for a given gene corresponding to the inclusion and exclusion of each potential predictor. For a given target gene i, potential predictors are those TFs that have a known regulatory effect on i, and the ten TFs with highest mutual information as measured by time-lagged context likelihood of relatedness (CLR) (Greenfield et al, 2010; Madar et al, 2010). Prior knowledge is incorporated by using a modification of Zellner’s g-prior (Zellner, 1983) to include subjective information on the regression parameters. A g-prior equal to 1.1 was used for the combined network described in this study. Sparsity of our solution is enforced by a model selection step based on the Bayesian Information Criterion (BIC) (Schwarz, 1978).

After model selection is carried out, the output is a matrix of dynamical parameters β, where each entry corresponds to the direction (i.e. correlated or anti- correlated) and strength (i.e. magnitude) of a regulatory interaction. To further improve inference and become more robust against over-fitting and sampling errors, we employ a bootstrapping strategy. We resample the input conditions with replacement, as well as the prior network, and run model selection on the new input. This procedure is repeated 201 times and the resulting lists of interactions are filtered to only keep those observed in more than 50% of the bootstraps.

Go enrichment analysis

The reference protein of Oryza Sativa was obtained from the Uniprot Database (http://www.uniprot.org/proteomes/UP000000763). The April, 2013 release of the Gene Ontology (http://archive.geneontology.org/full/2013-04-01/) was then queried by these ids, returning all annotations attributed to genes in the Oryza Sativa reference proteome. These annotations were propagated via the true path rule, whereby any protein with an annotation to a GO term also gains annotations for all terms that are parents of the given term, as specified by the GO hierarchy. Lastly, a separate proteome for Oryza Sativa was obtained from the Rice Genome Annotation Project (RGAP), available at: ftp://ftp.plantbiology.msu.edu/pub/data/Eukaryotic_Projects/o_sativa/annotation_dbs/ pseudomolecules/version_7.0/all.dir/. A blastp was performed, using default parameters, and for each Locus ID from the RGAP, the best-matching Uniprot ID was chosen, and the annotations transferred from that Uniprot ID to the Locus ID. Enrichment analysis of predictor targets was performed using the GOstats R package where all genes present in the network were used as background universe.

Data are available from the Gene Expression Omnibus (www.ncbi.nlm.nih.gov/geo/) under accession numbers GSE73609 (RNA-seq field data), GSE74793 (RNA-seq controlled chamber data), and GSE75794 (ATAC-seq data).

Code and supplementary materials are available from the Open Science Foundation https://osf.io/w6d2n/

AUTHOR CONTRIBUTIONS

Conceptualization: OW, CH, RB, MP; Methodology: OW, CH; Software: CH, RB; Formal Analysis: CH, RB; Investigations: OW, MMHP, ABN, AP, GMP; Resources: EMS, SVKJ, GBG; Data curation: CH, OW; Writing – Original Draft: OW, CH; Writing – Reviewing & Editing: RB, MP, and all authors; Visualization: CH, OW; Supervision: RB, MP; Project Administration: OW, CH, MP, RB; Funding Acquisition: EMS, RB, MP. The authors declare that they have no competing interests.

ACKNOWLEDGEMENTS

We thank Ms. Maria Pokrovskii and Dr. Emily Miraldi for their guidance on the ATAC-seq experimental protocol and analysis, Dr. Noah Youngs for sharing his working Gene Ontology annotations with us, Drs. Wenli Zhang and Jiming Jiang for sharing their nuclear isolation protocol with us, Zennia Jean Gonzaga, John Carlos Ignacio, Josefina Mendoza, Eloisa Suiton, and Junrey Amas for their help in preparation of planting, collecting and preparation of leaf samples, and Drs. Zoé Joly-Lopez and Simon Cornelis Groen for thoughtful comments on the manuscript.

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SUPPLEMENTAL INFORMATION

Supplemental Figure S1: Plant functional measurements Supplemental Figure S2: Distribution of expression values for all genes and control conditions Supplemental Figure S3: Multi-dimensional scaling plots of differentially expressed genes Supplemental Figure S4: ATAC-seq fragment size of aligned reads Supplemental Figure S5: ATAC-seq reproducibility scatter plot Supplemental Figure S6: ATAC-seq reproducibility heatmap Supplemental Figure S7: Comparison of ATAC-seq and DNase-seq data in rice Supplemental Figure S8: TFA stability for all TFs in the network prior Supplemental Figure S9: Convergence on a stable inferred network Supplemental Figure S10: Expression of the targets of EPR1 in a circadian data set Supplemental File S1: Differentially expressed genes Supplemental File S2: Open chromatin regions Supplemental File S3: Sample order for heatmaps and TFA plots Supplemental File S4: The network prior Supplemental File S5: TF predictor groups Supplemental File S6: Mean of estimated TF activities Supplemental File S7: TFA plots Supplemental File S8: Inferred network Supplemental File S9: Network predictor statistics Supplemental File S10: Gene Ontology enrichment results Supplemental File S11: RNA-seq summary statistics

A) B) Single factor perturbations Agricultural field conditions chromatin cis-regulatory accessibility motifs Control Season Field Heat shock - recovery Wet Flooded transcriptome Water deficit - recovery Dry Rain fed prior network data Conditions

estimated TF activity points

Every 15 minutes for 4h Every 2 days for 29 days Time

Azucena (Jap.) inferelator algorithm Azucena (Jap.) Pandan Wangi (Jap.) Pandan Wangi (Jap.) Kinandang Puti (Ind.) Palawan (Jap.) Cultivars Tadukan (Ind.) gene regulatory network

RNA-seq RNA-seq Environment ATAC-seq

Data Climate data environmental response modules Physiology Physiology

Observed expression Estimated TF activities Hundreds of models Inferred regulators of Y of gene Y based on model with lowest BIC

TFA1 TF1 Activity

Expression TFA2 TFA Y y n TF2 Conditions Conditions

FIGURE 1: Schematic overview. A) Experimental design: We queried the response of five tropical Asian rice cultivars to high temperatures, water deficit, and agricultural field conditions. Data collected includes time series transcriptome data (RNA-seq) and chromatin accessibility (ATAC-seq) B) Data analysis: We connect transcription factors with genes via known cis-regulatory motifs in accessible promoters; this prior network together with the transcriptome data is used to estimate transcription factor activities; the Inferelator algorithm is used to construct the final network. C) Inferelator algorithm: For every target gene hundreds of models linking its expression to TF activities are evaluated and one model is selected to define its regulators.

FIGURE 2. Overview of experimental data A) Functional responses: The relative photosynthetic rate of stress treated Azucena plants is presented (n=3 for each treatment; mean of the replicates is presented). Data for other genotypes are in supplementary material (Supplementary Figure S1). The vertical dashed lines indicate the end of the stress treatment and the start of the recovery treatment. Time 0 coincides with minimum functional status observed within 15 min of the start of the stress imposition B) For each cultivar we averaged the expression across control conditions for every gene. Pairwise scatter plots and Pearson correlations for Azucena (AZ) and Pandan Wangi (PW), Kinandang Puti (KP), and Tadukan (TD) are shown. C) Number of differentially expressed genes for each genotype, time point and treatment. Genes with positive fold change are shown as positive counts in purple; genes with negative fold change are shown as negative counts in orange. D) Expression similarity of data points shown as multi-dimensional scaling plot based on Euclidean distance of log-fold-change of 2097 differentially expressed genes (see method section). The number inside each data point indicates the time in minutes since the onset of the treatment at which the sample was measured.

FIGURE 3. Genomic distribution of open chromatin regions identified by ATAC-seq A) The genomic location of LOC_Os10g26620, DOF Zn-finger domain containing protein, is presented as bioRxiv preprint doi: https://doi.org/10.1101/042317; this version posted July 5, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

representative ATAC-seq data. Solid black lines represent regions of genomic DNA; the histograms indicate the sequencing reads that align to a given genomic region; the blue bars indicate regions that were determined to be open; the blue bars of variable width indicate the structure of gene models; the black arrows indicate on which strand the gene is encoded. B) Length distribution of open genomic regions identified by ATAC-seq. C) Location of open chromatin regions relative to gene features, including the regions 500 bp upstream of the TSS and downstream of the 3’ end of the gene model. Numbers next to bars indicate what percentage of bases that belong to a specific feature fall into open regions. All odds ratios are highly significant (p < 1e-13). D) Distribution of open chromatin around TSSs. For all 56k genes (first isoforms only), we determined the bases that are covered by an open region in the -1000 bp to +500 bp window around the TSS. The dashed lines indicate the start (-453 bp) and end (+137 bp) of the promoter region where more than 4% of the genes are open. E) Histogram of number of ATAC-seq cuts in promoter for non-expressed genes (top). Gene expression (median FPKM across all AZ samples) vs number of ATAC-seq cuts in promoter for expressed genes; Pearson correlation 0.42 (bottom). The same y-axis is used in both panels.

FIGURE 4. Prior knowledge of the network structure is used to calculate TF Activity A) Exemplar of network prior inputs. Region of chromosome 1 encoding 3 genes is highlighted to indicate how motif locations gave rise to the network prior. B) Coherence filtering of network prior, showing OsHSFA2a as an example. Heatmap of expression of network prior targets with higher levels of expression indicated with green tones, and lower expression levels indicated with brown tones. Coherence scores are indicated with grey scale bar - darker tones indicate higher confidence. Sample order details available in Supplementary File S3. C) Number of regulators, edges and targets in each stage of the network prior. bioRxiv preprint doi: https://doi.org/10.1101/042317; this version posted July 5, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

LOC_Os01g74020 pred.group.4 pred.group.24 LOC_Os04g11830 LOC_Os06g09370 LOC_Os11g13840 LOC_Os01g11550 pred.group.33 TF activities LOC_Os01g50940 pred.group.10 LOC_Os01g65370 A) pred.group.62 pred.group.23 LOC_Os03g51580 LOC_Os02g55320 pred.group.46 Row z-score LOC_Os03g08460 pred.group.47 LOC_Os05g40070 pred.group.48 LOC_Os04g36640 LOC_Os01g07480 LOC_Os05g11380 LOC_Os08g06110 60 6 LOC_Os01g46800 LOC_Os06g07030 Row Z Score LOC_Os12g33070 LOC_Os04g55520 LOC_Os03g45450 -6 0 6 pred.group.25 pred.group.37 pred.group.5 LOC_Os04g52410 LOC_Os05g14370 LOC_Os01g09280 LOC_Os09g21180 LOC_Os01g48170 pred.group.49 LOC_Os11g45850 pred.group.27 pred.group.57 pred.group.38 LOC_Os06g49840 pred.group.56 First time point in pred.group.51 LOC_Os07g48596 LOC_Os09g11460 pred.group.40 pred.group.35 LOC_Os01g08160 LOC_Os05g49700 pred.group.41 pred.group.42 irrigated field LOC_Os04g23550 LOC_Os09g35910 LOC_Os08g43160 pred.group.22 LOC_Os05g41070 LOC_Os03g03164 LOC_Os03g20550 LOC_Os01g68370 pred.group.36 pred.group.59 LOC_Os10g22600 LOC_Os08g04390 pred.group.32 First time point in pred.group.31 pred.group.14 pred.group.39 LOC_Os09g25040 pred.group.30 LOC_Os07g08420 LOC_Os06g01230 pred.group.1 LOC_Os03g56110 pred.group.20 upland field pred.group.17 pred.group.8 pred.group.54 pred.group.44 LOC_Os12g31748 pred.group.2 LOC_Os02g22020 pred.group.58 pred.group.55 pred.group.9 pred.group.43 pred.group.52 LOC_Os09g31300 pred.group.3 LOC_Os02g33430 pred.group.11 Onset of heat LOC_Os01g55580 pred.group.13 pred.group.29 LOC_Os03g12350 pred.group.15 LOC_Os06g06970 LOC_Os03g20900 pred.group.53 LOC_Os02g55380 LOC_Os07g07974 stress treatment LOC_Os12g42190 pred.group.45 LOC_Os04g23910 pred.group.21 pred.group.19 LOC_Os07g47330 LOC_Os03g58250 LOC_Os02g45420 LOC_Os06g51260 LOC_Os02g45670 pred.group.16 treatment Onset of water deficit TADUKAN_HEAT_015 TADUKAN_HEAT_030 TADUKAN_HEAT_045 TADUKAN_HEAT_060 TADUKAN_HEAT_075 TADUKAN_HEAT_090 TADUKAN_HEAT_105 TADUKAN_HEAT_120 TADUKAN_HEAT_135 TADUKAN_HEAT_150 TADUKAN_HEAT_165 TADUKAN_HEAT_180 TADUKAN_HEAT_195 TADUKAN_HEAT_210 TADUKAN_HEAT_225 AZUCENA_HEAT_015 AZUCENA_HEAT_030 AZUCENA_HEAT_045 AZUCENA_HEAT_060 AZUCENA_HEAT_075 AZUCENA_HEAT_090 AZUCENA_HEAT_105 AZUCENA_HEAT_120 AZUCENA_HEAT_135 AZUCENA_HEAT_150 AZUCENA_HEAT_165 AZUCENA_HEAT_180 AZUCENA_HEAT_195 AZUCENA_HEAT_210 AZUCENA_HEAT_225 AZUCENA_dry_upland_01 AZUCENA_dry_upland_02 AZUCENA_dry_upland_03 AZUCENA_dry_upland_04 AZUCENA_dry_upland_05 AZUCENA_dry_upland_06 AZUCENA_dry_upland_07 AZUCENA_dry_upland_08 AZUCENA_dry_upland_09 AZUCENA_dry_upland_10 AZUCENA_dry_upland_11 AZUCENA_dry_upland_12 AZUCENA_dry_upland_13 AZUCENA_dry_upland_14 AZUCENA_dry_upland_15 PALAWAN_wet_upland_01 PALAWAN_wet_upland_02 PALAWAN_wet_upland_03 PALAWAN_wet_upland_04 PALAWAN_wet_upland_05 PALAWAN_wet_upland_06 PALAWAN_wet_upland_07 PALAWAN_wet_upland_08 PALAWAN_wet_upland_09 PALAWAN_wet_upland_10 PALAWAN_wet_upland_11 PALAWAN_wet_upland_12 PALAWAN_wet_upland_13 PALAWAN_wet_upland_14 PALAWAN_wet_upland_15 AZUCENA_wet_upland_01 AZUCENA_wet_upland_02 AZUCENA_wet_upland_03 AZUCENA_wet_upland_04 AZUCENA_wet_upland_05 AZUCENA_wet_upland_06 AZUCENA_wet_upland_07 AZUCENA_wet_upland_08 AZUCENA_wet_upland_09 AZUCENA_wet_upland_10 AZUCENA_wet_upland_11 AZUCENA_wet_upland_12 AZUCENA_wet_upland_13 AZUCENA_wet_upland_14 AZUCENA_wet_upland_15 TADUKAN_CONTROL_015 TADUKAN_CONTROL_030 TADUKAN_CONTROL_045 TADUKAN_CONTROL_060 TADUKAN_CONTROL_075 TADUKAN_CONTROL_090 TADUKAN_CONTROL_105 TADUKAN_CONTROL_120 TADUKAN_CONTROL_135 TADUKAN_CONTROL_150 TADUKAN_CONTROL_165 TADUKAN_CONTROL_180 TADUKAN_CONTROL_195 TADUKAN_CONTROL_210 TADUKAN_CONTROL_225 TADUKAN_CONTROL_240 TADUKAN_CONTROL_270 AZUCENA_dry_lowland_01 AZUCENA_dry_lowland_02 AZUCENA_dry_lowland_03 AZUCENA_dry_lowland_04 AZUCENA_dry_lowland_05 AZUCENA_dry_lowland_06 AZUCENA_dry_lowland_07 AZUCENA_dry_lowland_08 AZUCENA_dry_lowland_09 AZUCENA_dry_lowland_10 AZUCENA_dry_lowland_11 AZUCENA_dry_lowland_12 AZUCENA_dry_lowland_13 AZUCENA_dry_lowland_14 AZUCENA_dry_lowland_15 PALAWAN_wet_lowland_01 PALAWAN_wet_lowland_02 PALAWAN_wet_lowland_03 PALAWAN_wet_lowland_04 PALAWAN_wet_lowland_05 PALAWAN_wet_lowland_06 PALAWAN_wet_lowland_07 PALAWAN_wet_lowland_08 PALAWAN_wet_lowland_09 PALAWAN_wet_lowland_10 PALAWAN_wet_lowland_11 PALAWAN_wet_lowland_12 PALAWAN_wet_lowland_13 PALAWAN_wet_lowland_14 PALAWAN_wet_lowland_15 AZUCENA_CONTROL_015 AZUCENA_CONTROL_030 AZUCENA_CONTROL_045 AZUCENA_CONTROL_060 AZUCENA_CONTROL_075 AZUCENA_CONTROL_090 AZUCENA_CONTROL_105 AZUCENA_CONTROL_120 AZUCENA_CONTROL_135 AZUCENA_CONTROL_150 AZUCENA_CONTROL_165 AZUCENA_CONTROL_180 AZUCENA_CONTROL_195 AZUCENA_CONTROL_210 AZUCENA_CONTROL_225 AZUCENA_CONTROL_240 AZUCENA_CONTROL_270 TADUKAN_DROUGHT_015 TADUKAN_DROUGHT_030 TADUKAN_DROUGHT_045 TADUKAN_DROUGHT_060 TADUKAN_DROUGHT_075 TADUKAN_DROUGHT_090 TADUKAN_DROUGHT_105 TADUKAN_DROUGHT_120 AZUCENA_wet_lowland_01 AZUCENA_wet_lowland_02 AZUCENA_wet_lowland_03 AZUCENA_wet_lowland_04 AZUCENA_wet_lowland_05 AZUCENA_wet_lowland_06 AZUCENA_wet_lowland_07 AZUCENA_wet_lowland_08 AZUCENA_wet_lowland_09 AZUCENA_wet_lowland_10 AZUCENA_wet_lowland_11 AZUCENA_wet_lowland_12 AZUCENA_wet_lowland_13 AZUCENA_wet_lowland_14 AZUCENA_wet_lowland_15 AZUCENA_DROUGHT_015 AZUCENA_DROUGHT_030 AZUCENA_DROUGHT_045 AZUCENA_DROUGHT_060 AZUCENA_DROUGHT_075 AZUCENA_DROUGHT_090 AZUCENA_DROUGHT_105 AZUCENA_DROUGHT_120 PANDAN_WANGI_HEAT_015 PANDAN_WANGI_HEAT_030 PANDAN_WANGI_HEAT_045 PANDAN_WANGI_HEAT_060 PANDAN_WANGI_HEAT_075 PANDAN_WANGI_HEAT_090 PANDAN_WANGI_HEAT_105 PANDAN_WANGI_HEAT_120 PANDAN_WANGI_HEAT_135 PANDAN_WANGI_HEAT_150 PANDAN_WANGI_HEAT_165 PANDAN_WANGI_HEAT_180 PANDAN_WANGI_HEAT_195 PANDAN_WANGI_HEAT_210 PANDAN_WANGI_HEAT_225 TADUKAN_RECOV_HEAT_135 TADUKAN_RECOV_HEAT_150 TADUKAN_RECOV_HEAT_165 TADUKAN_RECOV_HEAT_180 TADUKAN_RECOV_HEAT_195 TADUKAN_RECOV_HEAT_210 TADUKAN_RECOV_HEAT_225 KINANDANG_PUTI_HEAT_015 KINANDANG_PUTI_HEAT_030 KINANDANG_PUTI_HEAT_045 KINANDANG_PUTI_HEAT_060 KINANDANG_PUTI_HEAT_075 KINANDANG_PUTI_HEAT_090 KINANDANG_PUTI_HEAT_105 KINANDANG_PUTI_HEAT_120 KINANDANG_PUTI_HEAT_135 KINANDANG_PUTI_HEAT_150 KINANDANG_PUTI_HEAT_165 KINANDANG_PUTI_HEAT_180 KINANDANG_PUTI_HEAT_195 KINANDANG_PUTI_HEAT_210 KINANDANG_PUTI_HEAT_225 AZUCENA_RECOV_HEAT_135 AZUCENA_RECOV_HEAT_150 AZUCENA_RECOV_HEAT_165 AZUCENA_RECOV_HEAT_180 AZUCENA_RECOV_HEAT_195 AZUCENA_RECOV_HEAT_210 AZUCENA_RECOV_HEAT_225 PANDAN_WANGI_dry_upland_01 PANDAN_WANGI_dry_upland_02 PANDAN_WANGI_dry_upland_03 PANDAN_WANGI_dry_upland_04 PANDAN_WANGI_dry_upland_05 PANDAN_WANGI_dry_upland_06 PANDAN_WANGI_dry_upland_07 PANDAN_WANGI_dry_upland_08 PANDAN_WANGI_dry_upland_09 PANDAN_WANGI_dry_upland_10 PANDAN_WANGI_dry_upland_11 PANDAN_WANGI_dry_upland_12 PANDAN_WANGI_dry_upland_13 PANDAN_WANGI_dry_upland_14 PANDAN_WANGI_dry_upland_15 PANDAN_WANGI_dry_lowland_01 PANDAN_WANGI_dry_lowland_02 PANDAN_WANGI_dry_lowland_03 PANDAN_WANGI_dry_lowland_04 PANDAN_WANGI_dry_lowland_05 PANDAN_WANGI_dry_lowland_06 PANDAN_WANGI_dry_lowland_07 PANDAN_WANGI_dry_lowland_08 PANDAN_WANGI_dry_lowland_09 PANDAN_WANGI_dry_lowland_10 PANDAN_WANGI_dry_lowland_11 PANDAN_WANGI_dry_lowland_12 PANDAN_WANGI_dry_lowland_13 PANDAN_WANGI_dry_lowland_14 PANDAN_WANGI_dry_lowland_15 PANDAN_WANGI_CONTROL_015 PANDAN_WANGI_CONTROL_030 PANDAN_WANGI_CONTROL_045 PANDAN_WANGI_CONTROL_060 PANDAN_WANGI_CONTROL_075 PANDAN_WANGI_CONTROL_090 PANDAN_WANGI_CONTROL_105 PANDAN_WANGI_CONTROL_120 PANDAN_WANGI_CONTROL_135 PANDAN_WANGI_CONTROL_150 PANDAN_WANGI_CONTROL_165 PANDAN_WANGI_CONTROL_180 PANDAN_WANGI_CONTROL_195 PANDAN_WANGI_CONTROL_210 PANDAN_WANGI_CONTROL_225 PANDAN_WANGI_CONTROL_240 PANDAN_WANGI_CONTROL_270 PANDAN_WANGI_DROUGHT_015 PANDAN_WANGI_DROUGHT_030 PANDAN_WANGI_DROUGHT_045 PANDAN_WANGI_DROUGHT_060 PANDAN_WANGI_DROUGHT_075 PANDAN_WANGI_DROUGHT_090 PANDAN_WANGI_DROUGHT_105 PANDAN_WANGI_DROUGHT_120 KINANDANG_PUTI_CONTROL_015 KINANDANG_PUTI_CONTROL_030 KINANDANG_PUTI_CONTROL_045 KINANDANG_PUTI_CONTROL_060 KINANDANG_PUTI_CONTROL_075 KINANDANG_PUTI_CONTROL_090 KINANDANG_PUTI_CONTROL_105 KINANDANG_PUTI_CONTROL_120 KINANDANG_PUTI_CONTROL_135 KINANDANG_PUTI_CONTROL_150 KINANDANG_PUTI_CONTROL_165 KINANDANG_PUTI_CONTROL_180 KINANDANG_PUTI_CONTROL_195 KINANDANG_PUTI_CONTROL_210 KINANDANG_PUTI_CONTROL_225 KINANDANG_PUTI_CONTROL_240 KINANDANG_PUTI_CONTROL_270 TADUKAN_RECOV_DROUGHT_105 TADUKAN_RECOV_DROUGHT_120 TADUKAN_RECOV_DROUGHT_150 TADUKAN_RECOV_DROUGHT_165 TADUKAN_RECOV_DROUGHT_180 TADUKAN_RECOV_DROUGHT_210 TADUKAN_RECOV_DROUGHT_240 TADUKAN_RECOV_DROUGHT_270 KINANDANG_PUTI_DROUGHT_015 KINANDANG_PUTI_DROUGHT_030 KINANDANG_PUTI_DROUGHT_045 KINANDANG_PUTI_DROUGHT_060 KINANDANG_PUTI_DROUGHT_075 KINANDANG_PUTI_DROUGHT_090 KINANDANG_PUTI_DROUGHT_105 KINANDANG_PUTI_DROUGHT_120 AZUCENA_RECOV_DROUGHT_105 AZUCENA_RECOV_DROUGHT_120 AZUCENA_RECOV_DROUGHT_150 AZUCENA_RECOV_DROUGHT_165 AZUCENA_RECOV_DROUGHT_180 AZUCENA_RECOV_DROUGHT_210 AZUCENA_RECOV_DROUGHT_240 AZUCENA_RECOV_DROUGHT_270 treatment PANDAN_WANGI_RECOV_HEAT_135 PANDAN_WANGI_RECOV_HEAT_150 PANDAN_WANGI_RECOV_HEAT_165 PANDAN_WANGI_RECOV_HEAT_180 PANDAN_WANGI_RECOV_HEAT_195 PANDAN_WANGI_RECOV_HEAT_210 PANDAN_WANGI_RECOV_HEAT_225 KINANDANG_PUTI_RECOV_HEAT_135 KINANDANG_PUTI_RECOV_HEAT_150 KINANDANG_PUTI_RECOV_HEAT_165 KINANDANG_PUTI_RECOV_HEAT_180 KINANDANG_PUTI_RECOV_HEAT_195 KINANDANG_PUTI_RECOV_HEAT_210 KINANDANG_PUTI_RECOV_HEAT_225 PANDAN_WANGI_RECOV_DROUGHT_105 PANDAN_WANGI_RECOV_DROUGHT_120 PANDAN_WANGI_RECOV_DROUGHT_150 PANDAN_WANGI_RECOV_DROUGHT_165 PANDAN_WANGI_RECOV_DROUGHT_180 PANDAN_WANGI_RECOV_DROUGHT_210 PANDAN_WANGI_RECOV_DROUGHT_240 PANDAN_WANGI_RECOV_DROUGHT_270 season drywet dry wet KINANDANG_PUTI_RECOV_DROUGHT_105 KINANDANG_PUTI_RECOV_DROUGHT_120 KINANDANG_PUTI_RECOV_DROUGHT_150 KINANDANG_PUTI_RECOV_DROUGHT_165 KINANDANG_PUTI_RECOV_DROUGHT_180 KINANDANG_PUTI_RECOV_DROUGHT_210 KINANDANG_PUTI_RECOV_DROUGHT_240 KINANDANG_PUTI_RECOV_DROUGHT_270 genotype AZ PW PA AZ PW KP TD experimental conditions B) WRKY108 (LOC_Os01g60600) C) PCF2 (LOC_Os08g43160) Activity

Activity

Activity Activity

experimentalExperimental conditions conditions experimentalExperimental conditions conditions stability 0.15, 34 targets in network prior stability 0.96, 375 targets in network prior

D) E) 20 1.5 OsNAP (predictor group 1) Pearson correlation 0.76

15 1.0

10 0.5 count Count 0.0 5

−0.5 0

−0.5 0.0 0.5 1.0 Pearson correlation of TF activity and TF expression −0.2 0.0 0.2 0.4 0.6 TFA estimated targets TFA from ChIP Pearson correlation of TFA and TF expression TFA estimated from network prior

FIGURE 5. Transcription Factor Activities. Sample order is maintained for all heatmaps and TFA plots (Supplementary File S3). A) Heatmap of TFAs for all TFs and TF predictor groups for which activities were calculated. Higher levels of expression are indicated with green tones, and lower expression levels are indicated with brown tones. B) TFA stability (quantified as correlation of TFA estimates between 201 bootstrap subsamples of the network prior) for LOC_Os01g60600 and C) LOC_Os08g41360. In these plots, the light band shows the region between the 5th and 95th percentile of TFAs; the dark line shows the average TFA. D) Pearson correlation between expression and TFA for each TF. E) Correlation of TFA for OsNAP when estimated from network prior (x-axis), or from ChIP- PCR targets (y-axis). bioRxiv preprint doi: https://doi.org/10.1101/042317; this version posted July 5, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

LOC_Os08g09910

LOC_Os01g58740

LOC_Os01g04300

LOC_Os11g27264

LOC_Os04g44750

LOC_Os02g41680 LOC_Os03g40670

LOC_Os03g10200

LOC_Os12g31850

LOC_Os07g25730

LOC_Os01g54860 A) LOC_Os11g34940

LOC_Os04g08550

LOC_Os06g08600

LOC_Os04g50780 LOC_Os01g58750

LOC_Os11g18170 LOC_Os07g46540 LOC_Os02g13100

LOC_Os02g08100

LOC_Os02g43970

LOC_Os06g35940 LOC_Os02g18830

LOC_Os03g46660

LOC_Os07g44730 LOC_Os11g47460 LOC_Os03g13830

LOC_Os05g09704 LOC_Os10g32110

LOC_Os10g36680

LOC_Os05g51040

LOC_Os04g43410 LOC_Os06g05670 LOC_Os06g48600

LOC_Os01g48980

LOC_Os07g01200 LOC_Os06g20040

LOC_Os05g05060 LOC_Os06g16420 LOC_Os03g17780 LOC_Os02g54150

LOC_Os07g06840

LOC_Os06g06960 LOC_Os03g52670 LOC_Os04g42990 LOC_Os06g14370

LOC_Os11g39990 LOC_Os01g01650 LOC_Os03g12110 LOC_Os02g02880 LOC_Os12g22284 LOC_Os07g19444 LOC_Os09g07450

LOC_Os04g36840

LOC_Os08g06100 LOC_Os02g55530

LOC_Os02g29500

LOC_Os07g18162 LOC_Os01g22370 LOC_Os01g70240

LOC_Os09g32670 LOC_Os02g09359 LOC_Os01g05064

LOC_Os03g58040 LOC_Os01g62260 LOC_Os09g25610 LOC_Os08g28790 LOC_Os08g28860 LOC_Os01g09010 LOC_Os01g28030 LOC_Os04g43400

LOC_Os06g39230

LOC_Os11g46000

LOC_Os01g66200

LOC_Os01g42520 LOC_Os02g52930

LOC_Os02g43290 LOC_Os04g41570 LOC_Os02g37800

LOC_Os03g27230 LOC_Os02g09490

LOC_Os09g27820 LOC_Os08g40500 LOC_Os06g30950 LOC_Os06g49660 LOC_Os03g45194

LOC_Os04g35080 LOC_Os04g38680

LOC_Os04g34240 LOC_Os07g08180 LOC_Os05g28520

LOC_Os03g18140

LOC_Os06g35410 LOC_Os05g34500

LOC_Os08g32160 LOC_Os02g57330

LOC_Os04g36820 LOC_Os01g12650 LOC_Os09g27070

LOC_Os10g42690

LOC_Os01g55700 LOC_Os01g65370

LOC_Os01g32460 LOC_Os10g42670

LOC_Os03g07970 LOC_Os06g40120

LOC_Os07g18158 LOC_Os02g31874 LOC_Os11g05660

LOC_Os06g02780 LOC_Os02g15230 LOC_Os04g01280

LOC_Os08g42390 LOC_Os02g41780

LOC_Os11g10800

LOC_Os06g06760

LOC_Os12g09580 LOC_Os01g72430

LOC_Os03g10650 LOC_Os01g04160 LOC_Os02g38950

LOC_Os04g32850 LOC_Os05g48850

LOC_Os07g08460

LOC_Os02g48130

LOC_Os05g03540 LOC_Os11g27370 LOC_Os02g43600 LOC_Os02g03560

LOC_Os12g38920 LOC_Os07g34260

LOC_Os02g46830

LOC_Os11g43860

LOC_Os02g49970 LOC_Os05g49140 LOC_Os03g44710 LOC_Os09g01290

LOC_Os03g58470

LOC_Os01g55690

LOC_Os04g32320

LOC_Os01g01660 LOC_Os11g05860 LOC_Os01g16120 LOC_Os05g45770 LOC_Os04g15690

LOC_Os10g02880

LOC_Os03g52280 LOC_Os07g05380

LOC_Os03g18890 LOC_Os03g13800

LOC_Os03g08100

LOC_Os05g25784 LOC_Os04g32860

LOC_Os06g46460 LOC_Os07g06670 LOC_Os04g37550 LOC_Os01g27160 LOC_Os10g39410 LOC_Os02g46780

LOC_Os02g29210

LOC_Os05g44340 LOC_Os06g40720 LOC_Os12g03750 LOC_Os07g30760 LOC_Os03g17680 LOC_Os06g11400

LOC_Os09g37910 LOC_Os01g70710

LOC_Os06g47920

LOC_Os03g25330

LOC_Os06g06250

LOC_Os01g01870 LOC_Os07g42714

LOC_Os01g57073

LOC_Os02g22020 LOC_Os02g44940 LOC_Os05g11900 LOC_Os06g46799 LOC_Os02g51390

LOC_Os03g58110 LOC_Os05g50800 LOC_Os02g55330 LOC_Os12g13640

LOC_Os10g30600 LOC_Os05g09724

LOC_Os03g45170

LOC_Os12g33070 LOC_Os07g44090

LOC_Os02g52840 LOC_Os02g31030

LOC_Os02g09400

LOC_Os06g21270

LOC_Os12g02490 LOC_Os03g15020 LOC_Os01g61460

LOC_Os07g42370

LOC_Os02g39850

LOC_Os09g27700 LOC_Os12g07580 LOC_Os03g30130

LOC_Os08g42870 LOC_Os01g43580

LOC_Os09g04050

LOC_Os04g36730 LOC_Os07g05470

LOC_Os01g59840 LOC_Os02g52560 LOC_Os02g41590

LOC_Os09g28720 LOC_Os05g45180 LOC_Os08g01510

LOC_Os10g31030

LOC_Os02g39700

LOC_Os08g43180

LOC_Os08g38900 LOC_Os07g36544

LOC_Os01g27750 AP2(Os06g07030) LOC_Os01g11620 LOC_Os03g17060 LOC_Os10g04450 LOC_Os01g56640 LOC_Os10g30200 LOC_Os09g38020 AP2(Os4g55520) LOC_Os07g46870

LOC_Os05g08554

LOC_Os04g42610 LOC_Os03g56960

LOC_Os12g15470 LOC_Os03g64320

LOC_Os11g08300 LOC_Os12g34524

LOC_Os07g44180

LOC_Os10g05970 LOC_Os03g30420

LOC_Os01g02900 LOC_Os09g26660

LOC_Os10g30610

LOC_Os12g01560 LOC_Os09g26300 LOC_Os02g21009

LOC_Os08g30070 LOC_Os05g32170 LOC_Os01g64640

Cell wall biogenesis LOC_Os02g07410

LOC_Os10g40810 Cellulose synthase activity

LOC_Os05g35500 LOC_Os07g46930

LOC_Os03g19310

LOC_Os05g09740

LOC_Os05g01480 LOC_Os05g50990 LOC_Os01g70190 LOC_Os01g19020

LOC_Os05g04820

LOC_Os12g12470

LOC_Os01g59819 LOC_Os01g65690

LOC_Os06g44320

LOC_Os06g17390

LOC_Os02g47420

LOC_Os07g41600

LOC_Os10g12750

LOC_Os05g02820

LOC_Os07g45060 LOC_Os05g30980

LOC_Os01g73790 LOC_Os08g40060 LOC_Os06g42560 LOC_Os01g58870 LOC_Os05g01030 LOC_Os02g22380 LOC_Os01g04370 LOC_Os03g14250 LOC_Os02g56110 LOC_Os06g38960

LOC_Os06g13600

LOC_Os06g14060 LOC_Os01g01160

LOC_Os10g41749 LOC_Os03g61960 LOC_Os03g56660

LOC_Os10g40020

LOC_Os01g65100 LOC_Os05g03190 LOC_Os05g11010 LOC_Os07g46640

LOC_Os03g02380 LOC_Os03g43650

LOC_Os06g34120 LOC_Os06g43170 LOC_Os08g33640 LOC_Os06g46284

LOC_Os02g53200

LOC_Os02g50490

LOC_Os01g69290 LOC_Os03g30250 LOC_Os09g35810

LOC_Os06g49000 LOC_Os05g43860

LOC_Os09g30280 LOC_Os05g32500

LOC_Os07g38730 LOC_Os01g44130 LOC_Os08g25690 LOC_Os04g51120

LOC_Os07g26440 LOC_Os02g47880 LOC_Os03g16980 LOC_Os11g31540

LOC_Os05g12474 LOC_Os06g02540

LOC_Os04g48130

LOC_Os12g43440

LOC_Os01g48130 LOC_Os10g16974

LOC_Os06g36490 LOC_Os08g43270 LOC_Os01g28500

LOC_Os07g06620

LOC_Os07g01600

LOC_Os01g16030 LOC_Os03g56610 LOC_Os08g36150 LOC_Os02g47110 LOC_Os12g41956 LOC_Os04g14150 LOC_Os03g33590 LOC_Os12g44090

LOC_Os05g39350

LOC_Os07g36140 LOC_Os09g32080 LOC_Os04g39150 LOC_Os08g37060 LOC_Os12g37939 LOC_Os03g47022 LOC_Os02g50230

LOC_Os10g37240

LOC_Os01g58670

LOC_Os02g07060 LOC_Os03g56430 LOC_Os02g43410

LOC_Os03g43810 LOC_Os06g36050 LOC_Os06g40150 LOC_Os12g41600

LOC_Os03g02040 LOC_Os05g10210 LOC_Os10g25090 LOC_Os01g67150

LOC_Os02g50550

LOC_Os01g70390 LOC_Os02g48110 LOC_Os03g14180 LOC_Os05g42280 LOC_Os03g45920 LOC_Os04g01740

LOC_Os09g04360 LOC_Os09g04339 LOC_Os01g67330 LOC_Os02g34430

LOC_Os05g33820 LOC_Os02g54030 LOC_Os03g28330 LOC_Os10g11340

LOC_Os06g46500 LOC_Os07g33630 pred.group.52 LOC_Os01g17470 LOC_Os10g11889

LOC_Os04g54810

LOC_Os01g44040

LOC_Os05g44916 LOC_Os01g67420

LOC_Os02g32504 LOC_Os05g30480 LOC_Os06g15620 LOC_Os11g13980 LOC_Os03g06940

LOC_Os03g49430 LOC_Os04g55260 LOC_Os03g43670

LOC_Os03g02190 LOC_Os04g58610 LOC_Os01g70100 LOC_Os02g34260 LOC_Os03g51150 LOC_Os02g12400 LOC_Os10g37190 LOC_Os07g39800

LOC_Os04g44870

LOC_Os05g38500 LOC_Os06g15810 LOC_Os08g29854 LOC_Os05g10830 LOC_Os06g19110 LOC_Os04g46740

LOC_Os10g22750 LOC_Os01g10040 LOC_Os04g52370 LOC_Os11g01570 LOC_Os04g48930 LOC_Os08g35760

LOC_Os05g31160

LOC_Os05g08640 LOC_Os03g17210 LOC_Os02g13870 LOC_Os10g34520 LOC_Os10g38870

LOC_Os02g02500 LOC_Os07g16364 LOC_Os04g45480

LOC_Os03g32470 LOC_Os07g46700 LOC_Os03g58350 LOC_Os08g41890 LOC_Os01g04340 LOC_Os09g23360 LOC_Os11g38650 LOC_Os06g13760 LOC_Os05g32350 LOC_Os04g43490 LOC_Os08g44015

LOC_Os02g57140 LOC_Os02g40900 LOC_Os02g10690 LOC_Os04g48230

LOC_Os01g43540 LOC_Os01g21960 LOC_Os08g38170 LOC_Os12g36194 LOC_Os01g58690 LOC_Os04g41680 LOC_Os05g45210 LOC_Os03g04220

LOC_Os04g39610 LOC_Os08g05590 LOC_Os03g49480 LOC_Os03g17000 LOC_Os02g01280 LOC_Os07g49040 LOC_Os07g04930 LOC_Os12g41500 LOC_Os03g51760

LOC_Os05g06720

LOC_Os07g43530 LOC_Os06g10410 LOC_Os01g63910

LOC_Os12g40890 LOC_Os11g07280 LOC_Os09g19830 LOC_Os06g09820 LOC_Os09g20630 LOC_Os01g04710 LOC_Os04g47580 LOC_Os05g46550 LOC_Os04g36750 LOC_Os07g41050 LOC_Os01g25540 LOC_Os10g30080

LOC_Os05g33690 LOC_Os01g62480 LOC_Os01g68300 LOC_Os01g42960 LOC_Os01g51840 LOC_Os05g38270 LOC_Os01g08560 LOC_Os09g14670 LOC_Os01g18100 LOC_Os09g36350 LOC_Os04g28520

LOC_Os07g04150 LOC_Os11g24510 LOC_Os06g36930

LOC_Os04g40730 LOC_Os09g22510 LOC_Os08g43130 LOC_Os08g38270

LOC_Os02g54060

LOC_Os01g04720 LOC_Os01g70200 LOC_Os01g21034 LOC_Os02g20850 LOC_Os01g21630 LOC_Os03g49730 LOC_Os09g30412

LOC_Os01g65550

LOC_Os06g16390 LOC_Os07g27480

LOC_Os04g52320 LOC_Os02g51000 LOC_Os10g37630 LOC_Os07g02200 LOC_Os06g05550 LOC_Os04g33200 LOC_Os09g35790 LOC_Os06g43130 LOC_Os08g40910 LOC_Os01g06580 LOC_Os11g02180

LOC_Os07g43740 LOC_Os11g29680 LOC_Os08g02140

LOC_Os07g43990 LOC_Os04g24220 LOC_Os01g05650 LOC_Os04g42910

LOC_Os11g38010

LOC_Os07g10110 LOC_Os01g22640 LOC_Os05g29880 LOC_Os05g42330 LOC_Os08g39490 LOC_Os05g24520 LOC_Os01g68960

LOC_Os12g06020 LOC_Os03g17690 LOC_Os06g39260

LOC_Os01g47470 LOC_Os04g32950 LOC_Os03g50810 LOC_Os08g34550 LOC_Os03g31730 LOC_Os03g50130 LOC_Os02g14170 LOC_Os04g44100 LOC_Os03g18970 LOC_Os06g04090 LOC_Os03g12250 LOC_Os02g50680

LOC_Os03g62090 LOC_Os10g40620 LOC_Os01g74640 LOC_Os01g40200 LOC_Os08g02370 LOC_Os06g12280 LOC_Os04g37960 LOC_Os04g46730 LOC_Os05g03174 LOC_Os01g07360 LOC_Os01g43851 LOC_Os02g04860

LOC_Os08g41320 LOC_Os01g04360 LOC_Os03g18980

LOC_Os02g48730 LOC_Os02g19640 LOC_Os04g11980

LOC_Os06g07030 LOC_Os04g47290 LOC_Os02g02410 LOC_Os06g13580

LOC_Os02g15930 LOC_Os03g16570 LOC_Os05g01310

LOC_Os02g28980 LOC_Os03g53340

pred.group.9 LOC_Os03g52880 LOC_Os07g28400 LOC_Os01g23630 LOC_Os07g32020

LOC_Os05g32960 LOC_Os03g57460

LOC_Os05g45930 LOC_Os02g21890 LOC_Os06g24404 LOC_Os01g12860 LOC_Os10g31830 LOC_Os01g61230 LOC_Os03g06120 LOC_Os10g25180 LOC_Os03g15720 LOC_Os07g47840 LOC_Os05g12680 LOC_Os02g49460 LOC_Os05g50910 LOC_Os02g16030

LOC_Os01g18050 LOC_Os05g38000 LOC_Os12g38480 LOC_Os08g08970 LOC_Os04g37480 LOC_Os06g30970 LOC_Os08g44360 LOC_Os02g04540 LOC_Os08g44330 LOC_Os01g53640 LOC_Os06g14240

LOC_Os03g10680 LOC_Os02g32110

LOC_Os07g27810

LOC_Os01g68830 LOC_Os11g42960 LOC_Os02g04650 LOC_Os03g08600 LOC_Os05g38530

LOC_Os11g38190

LOC_Os08g41340 LOC_Os03g49830 LOC_Os04g58720 LOC_Os12g33150 LOC_Os08g43400 LOC_Os01g62620 LOC_Os06g49700 LOC_Os05g04380 LOC_Os06g47294 LOC_Os08g13350 LOC_Os06g11990 LOC_Os03g16030 LOC_Os03g06080

LOC_Os01g43910 LOC_Os10g33910 LOC_Os01g66110 LOC_Os10g27340 LOC_Os08g43334

LOC_Os04g58960 LOC_Os09g29840

LOC_Os03g18250 LOC_Os05g03910 LOC_Os09g33520 LOC_Os02g43640

LOC_Os02g30380 LOC_Os04g55520 LOC_Os06g29180 LOC_Os04g59330 LOC_Os08g06630

LOC_Os08g15204

LOC_Os09g30486

LOC_Os04g52890 LOC_Os02g28830 LOC_Os01g73250 LOC_Os12g01530 LOC_Os10g32770 LOC_Os03g02840 LOC_Os03g56810 LOC_Os03g07450 LOC_Os03g17730

LOC_Os10g36880 LOC_Os01g59150 LOC_Os04g59040 LOC_Os07g14270 LOC_Os01g55000 LOC_Os01g64490 LOC_Os07g39920 LOC_Os07g45100 LOC_Os05g23740

LOC_Os11g31740 LOC_Os03g15890 LOC_Os07g31840 LOC_Os02g50630 LOC_Os05g45830

LOC_Os01g09880 LOC_Os05g03610 LOC_Os03g52630 LOC_Os12g35620

LOC_Os04g51090 LOC_Os01g56330 LOC_Os05g46610

LOC_Os06g09560 LOC_Os03g51600 LOC_Os08g41250 LOC_Os08g23754

LOC_Os03g14610 LOC_Os02g52140

LOC_Os02g53790 LOC_Os03g31300 LOC_Os05g05390 LOC_Os01g59060 LOC_Os11g37700 LOC_Os04g55640 LOC_Os08g34750 LOC_Os03g61260 LOC_Os02g57120 LOC_Os05g09550 LOC_Os03g24070 LOC_Os02g35160 LOC_Os07g01860 LOC_Os06g03930

LOC_Os02g49950 LOC_Os01g42510 LOC_Os06g06780 LOC_Os11g18830 LOC_Os02g42660 LOC_Os03g64170 LOC_Os09g33690 LOC_Os09g25314 LOC_Os09g32095 LOC_Os01g65780

LOC_Os04g53760 LOC_Os11g01740

LOC_Os03g05560

LOC_Os07g32620 LOC_Os01g56470 LOC_Os01g73990 LOC_Os02g47640 LOC_Os09g24900 LOC_Os07g44070 LOC_Os01g46350 LOC_Os02g19410 LOC_Os02g02990 LOC_Os06g12940

LOC_Os03g11340 LOC_Os02g43280 LOC_Os03g25770 LOC_Os07g44350 LOC_Os04g58800 LOC_Os01g04380 LOC_Os01g64256

LOC_Os04g47480 LOC_Os09g26730

LOC_Os09g28760 LOC_Os02g12360 LOC_Os05g19030 LOC_Os10g36870

LOC_Os04g05010 LOC_Os05g30140 LOC_Os01g02940

LOC_Os12g10784

LOC_Os07g20100 LOC_Os05g41530 LOC_Os10g39420 LOC_Os03g02260 LOC_Os02g51790 LOC_Os04g35580 LOC_Os05g41910 LOC_Os06g11610

LOC_Os11g13750 LOC_Os06g14490 LOC_Os08g02640 LOC_Os12g32986 LOC_Os03g44780 LOC_Os12g12570 LOC_Os12g35560 LOC_Os05g20050 LOC_Os08g44620 LOC_Os06g05980 LOC_Os02g33230 LOC_Os07g41360 LOC_Os10g40710 LOC_Os05g38720

LOC_Os06g49320 LOC_Os11g26990

LOC_Os09g38239 LOC_Os05g40220 LOC_Os04g58130 LOC_Os10g32980

LOC_Os01g33080 LOC_Os06g02620 LOC_Os03g50520 LOC_Os01g44110 LOC_Os02g52280 LOC_Os01g34620 LOC_Os08g02460 LOC_Os02g18390 LOC_Os09g27830

LOC_Os03g08270

LOC_Os06g46930 LOC_Os09g14680 LOC_Os05g38280 LOC_Os10g05980 LOC_Os08g39870 LOC_Os03g16920

LOC_Os07g12730

LOC_Os12g38810 LOC_Os02g13640 LOC_Os03g15960 LOC_Os04g01300 LOC_Os08g38310 LOC_Os06g15820 LOC_Os10g28370

LOC_Os05g06440 LOC_Os02g08490 LOC_Os01g54620

LOC_Os01g47780 LOC_Os03g03570 LOC_Os12g07160 pred.group.8

LOC_Os08g36170 LOC_Os03g30300 LOC_Os09g31486 LOC_Os01g22230 LOC_Os04g41910 LOC_Os02g14160

LOC_Os03g14654 LOC_Os12g41140 LOC_Os07g29310 LOC_Os01g34080

LOC_Os11g42500 LOC_Os11g14220 LOC_Os11g02389 LOC_Os01g65380

LOC_Os11g09160

LOC_Os05g43560 LOC_Os03g25050

LOC_Os09g24840 LOC_Os02g02040

LOC_Os11g32369 LOC_Os06g47770 LOC_Os03g57340 LOC_Os12g29760 LOC_Os04g30420 LOC_Os03g13750 LOC_Os05g32110 LOC_Os07g31540 LOC_Os01g56630 LOC_Os02g20930 LOC_Os03g03980

LOC_Os10g21130 MYBTFgroup(#5) LOC_Os01g09860

LOC_Os02g38386 LOC_Os11g04520 LOC_Os09g11250 LOC_Os08g23180 LOC_Os02g21700

LOC_Os11g38260 LOC_Os08g35710

LOC_Os04g50204

pred.group.42 LOC_Os03g16150 LOC_Os01g57050 LOC_Os05g48810 LOC_Os05g13370 LOC_Os01g63250 LOC_Os06g06490

LOC_Os02g28720 LOC_Os01g62290 LOC_Os04g54390 LOC_Os04g48750

LOC_Os01g61860

LOC_Os04g52830 LOC_Os01g14090 LOC_Os03g58170 LOC_Os04g43750 LOC_Os06g33200 LOC_Os05g43530

LOC_Os08g39150 LOC_Os01g02150 LOC_Os04g33450 LOC_Os11g07912 LOC_Os03g02240 LOC_Os01g50750

LOC_Os03g56540 LOC_Os06g01200 LOC_Os01g13760 LOC_Os09g25900 LOC_Os08g13920

LOC_Os01g14840

LOC_Os05g11990 LOC_Os09g33780 LOC_Os01g55270 LOC_Os03g58610 LOC_Os02g43340

LOC_Os09g16280

LOC_Os06g04560 LOC_Os05g35480 LOC_Os03g06220

LOC_Os01g05840

LOC_Os02g43020 LOC_Os03g58490 LOC_Os09g07350 LOC_Os08g05520 LOC_Os06g15390 LOC_Os05g39520 LOC_Os08g40150 LOC_Os07g14850

LOC_Os04g51080 LOC_Os02g54890

LOC_Os06g41770 LOC_Os03g60780 LOC_Os02g51860 LOC_Os01g54550

LOC_Os03g38500 LOC_Os04g51320

LOC_Os10g27330 LOC_Os01g72230 LOC_Os02g13360

LOC_Os03g16020 LOC_Os08g39560 LOC_Os07g44740

LOC_Os04g38310 LOC_Os08g09690 LOC_Os05g04500 LOC_Os08g06380 LOC_Os03g12140 LOC_Os11g01530

LOC_Os08g17680

LOC_Os01g14670

LOC_Os03g50560 LOC_Os11g44940

LOC_Os03g08000

LOC_Os08g07080 LOC_Os09g20900 LOC_Os09g09500 LOC_Os05g11140 LOC_Os06g37560 LOC_Os02g01540 LOC_Os01g06660 LOC_Os01g46400 LOC_Os06g04870

LOC_Os03g12370 LOC_Os09g35630 Photosynthesis LOC_Os05g48900

LOC_Os06g36940

LOC_Os02g52150 LOC_Os03g13450 LOC_Os12g41930

LOC_Os01g19220 LOC_Os06g04040 LOC_Os01g64370 LOC_Os10g28340

LOC_Os03g18200 LOC_Os03g21730 LOC_Os09g25490

LOC_Os01g62650 LOC_Os01g62890 LOC_Os02g48140 LOC_Os02g42540 LOC_Os03g04260 LOC_Os12g37870 LOC_Os05g02310 LOC_Os03g22620 LOC_Os05g50850

LOC_Os01g63880 LOC_Os10g38580

LOC_Os10g38610 LOC_Os03g13860 LOC_Os04g44200 LOC_Os05g48270 LOC_Os02g02340

LOC_Os05g04150

LOC_Os12g02050

LOC_Os02g40100 LOC_Os06g04390 LOC_Os02g50600 pred.group.54

LOC_Os03g03600 LOC_Os07g37100

LOC_Os08g01920 LOC_Os04g32580 LOC_Os07g44950 LOC_Os03g15950

LOC_Os05g09400 LOC_Os01g02010 LOC_Os01g53250 LOC_Os07g46570 LOC_Os06g45660

LOC_Os03g52340

LOC_Os08g01274

LOC_Os01g52140

LOC_Os10g20090 LOC_Os04g01780

LOC_Os02g02750

LOC_Os02g02840 LOC_Os02g50640 LOC_Os05g35970 LOC_Os07g33480 LOC_Os09g25810 LOC_Os03g26460 LOC_Os02g54140 LOC_Os02g38392 LOC_Os04g36640 LOC_Os02g05910

LOC_Os08g03380 LOC_Os02g48710

LOC_Os06g02380 LOC_Os03g37864 LOC_Os02g02524 LOC_Os05g40212 LOC_Os08g29710 LOC_Os11g38250 LOC_Os07g49470 LOC_Os12g09590

LOC_Os06g50300

LOC_Os05g26660

LOC_Os09g39700 LOC_Os05g42100 LOC_Os01g08510 LOC_Os06g03640

LOC_Os06g15779 LOC_Os03g47270

LOC_Os07g06680 LOC_Os06g12530 LOC_Os07g28260 LOC_Os01g21420 LOC_Os10g40490 LOC_Os06g30380 LOC_Os05g19670 LOC_Os07g12630

LOC_Os01g73420 LOC_Os03g02670 LOC_Os01g56680 LOC_Os06g41910 LOC_Os11g31480 LOC_Os06g11440

LOC_Os07g10460 LOC_Os01g61830 LOC_Os10g30560 LOC_Os10g32550 LOC_Os08g38086 LOC_Os07g03910 LOC_Os06g12150

LOC_Os01g07410 LOC_Os04g40520 LOC_Os04g49950

LOC_Os09g32250 LOC_Os11g02090 LOC_Os07g48890 LOC_Os11g13630

LOC_Os01g65904 LOC_Os08g33890

LOC_Os05g27100

LOC_Os05g29860

LOC_Os02g03320 LOC_Os10g28040

LOC_Os08g40580 LOC_Os06g07790 LOC_Os02g02860 LOC_Os11g29990 LOC_Os08g41390

LOC_Os08g39140 LOC_Os10g22039 LOC_Os10g40520

LOC_Os03g17840

LOC_Os01g70430 LOC_Os09g38930

LOC_Os01g73540

LOC_Os03g46730 LOC_Os08g31980 LOC_Os03g04970

LOC_Os02g04460 LOC_Os04g30180 LOC_Os02g17780 LOC_Os03g52040 LOC_Os02g55610 LOC_Os12g42430 LOC_Os03g02514

LOC_Os07g48770 LOC_Os05g12380

LOC_Os09g06740 LOC_Os06g11320 LOC_Os05g38370

LOC_Os01g65070 LOC_Os02g53490 LOC_Os06g39240 LOC_Os07g43470 LOC_Os02g33320 LOC_Os12g41650 LOC_Os04g12540

LOC_Os07g43040 LOC_Os04g55220

LOC_Os07g03190 LOC_Os03g03020 LOC_Os01g66680 LOC_Os01g05120 LOC_Os01g47630 LOC_Os08g01220 LOC_Os01g39290 LOC_Os05g42130

LOC_Os01g11250 LOC_Os03g49710

LOC_Os09g17610

LOC_Os02g46473 LOC_Os06g49780

LOC_Os02g41630 LOC_Os12g23760 LOC_Os10g41820

LOC_Os03g19220 LOC_Os01g31040 LOC_Os10g25040 LOC_Os02g56880 LOC_Os12g06740

LOC_Os01g32380 LOC_Os06g47760 LOC_Os02g57800 LOC_Os10g35110 LOC_Os04g54320 LOC_Os12g39860

LOC_Os10g37640

LOC_Os06g14350 LOC_Os01g02440 LOC_Os05g02040

LOC_Os06g49030 LOC_Os02g06890

LOC_Os05g49060 LOC_Os03g15870 LOC_Os03g51030 LOC_Os07g34598 LOC_Os09g27920 LOC_Os05g36186 LOC_Os05g04120 LOC_Os04g01540 LOC_Os10g02490 LOC_Os04g38920 LOC_Os03g29540 LOC_Os02g29620 LOC_Os09g37930 LOC_Os11g15700 LOC_Os07g48420

LOC_Os06g37450 LOC_Os01g63190

LOC_Os03g18020 LOC_Os01g66600 LOC_Os03g63999

LOC_Os11g17014 LOC_Os01g70220 LOC_Os07g38590 LOC_Os01g59930

LOC_Os09g26810 LOC_Os05g37160 LOC_Os05g34950

LOC_Os06g09679

LOC_Os11g34210 LOC_Os12g18410 LOC_Os11g29900

LOC_Os08g44650 LOC_Os04g39350

LOC_Os02g05530 LOC_Os03g10220

pred.group.35 LOC_Os05g32270 LOC_Os07g17210 LOC_Os12g01370 LOC_Os09g21450 LOC_Os10g35810 LOC_Os04g49210 LOC_Os02g47930 LOC_Os12g44110

LOC_Os12g02550 LOC_Os03g14669 LOC_Os05g04960 LOC_Os05g06920 LOC_Os07g17390 LOC_Os01g16130 LOC_Os10g37230 LOC_Os07g07300 pred.group.16

LOC_Os12g37590

LOC_Os04g32110

LOC_Os06g49550 LOC_Os12g40540 LOC_Os02g05920 LOC_Os07g07340 LOC_Os09g15284

LOC_Os04g49530 LOC_Os09g28280 LOC_Os04g21350

LOC_Os10g02040

LOC_Os04g58200

LOC_Os12g16490 LOC_Os04g47906

LOC_Os02g08380 LOC_Os03g06710 LOC_Os11g32260

LOC_Os01g46720 LOC_Os03g12020 LOC_Os07g36950

LOC_Os02g37690 LOC_Os03g63950 LOC_Os02g20210 LOC_Os06g04530 LOC_Os02g46930 LOC_Os01g32090 LOC_Os01g54540 LOC_Os05g49320 LOC_Os10g42830 LOC_Os03g55330 LOC_Os02g47920 LOC_Os01g40820 LOC_Os01g70520 LOC_Os12g12580 LOC_Os04g33390 LOC_Os12g23280 LOC_Os01g24070 LOC_Os03g50970

LOC_Os11g37130 LOC_Os02g42020 LOC_Os04g52130 LOC_Os12g41230 LOC_Os10g42410 LOC_Os08g14860 LOC_Os05g40384 LOC_Os07g38150 LOC_Os12g27070 LOC_Os06g29700

LOC_Os02g37700 LOC_Os03g10860 HSFTFgroup(#8) LOC_Os12g32280 LOC_Os01g18880

LOC_Os02g26294

LOC_Os04g52950 LOC_Os08g29530 LOC_Os03g03360 LOC_Os07g09140 LOC_Os12g07480 LOC_Os09g35890

LOC_Os02g38130 LOC_Os03g64280 LOC_Os03g21230 LOC_Os03g55210

LOC_Os06g27800 LOC_Os04g41040 LOC_Os08g16914

LOC_Os01g40760 LOC_Os02g33944

LOC_Os03g03470 LOC_Os11g27799 LOC_Os01g06310 LOC_Os10g33770 LOC_Os06g22080 LOC_Os04g32980 LOC_Os01g68290 LOC_Os06g15330 LOC_Os06g06440 LOC_Os04g44850

LOC_Os01g03940 LOC_Os09g28710 LOC_Os01g32730 LOC_Os03g41229 LOC_Os10g37490 LOC_Os07g38000 LOC_Os04g36058 LOC_Os03g04550 LOC_Os07g09800

LOC_Os04g38750 LOC_Os01g05470

LOC_Os04g38220 LOC_Os08g12780 LOC_Os03g05049 LOC_Os01g42280

LOC_Os05g41795 LOC_Os08g33700 LOC_Os03g03140 LOC_Os02g52450 LOC_Os03g46550 LOC_Os05g10730 LOC_Os02g02320 LOC_Os03g51110 LOC_Os01g61070 LOC_Os06g11290 LOC_Os01g48690

LOC_Os01g49219

LOC_Os02g03580 LOC_Os02g01510

LOC_Os12g16690 LOC_Os08g40919 LOC_Os03g62690 LOC_Os05g41800 LOC_Os02g45120 LOC_Os12g13350

pred.group.38

LOC_Os05g37520 LOC_Os04g35000

LOC_Os12g39440 LOC_Os06g40818 LOC_Os10g42550 LOC_Os01g32170 LOC_Os10g39810 LOC_Os05g44210 LOC_Os01g52710 LOC_Os12g03800 LOC_Os10g37180 LOC_Os03g01110 LOC_Os04g32590 LOC_Os09g16760 LOC_Os04g46170 LOC_Os07g41260

LOC_Os01g07370 LOC_Os01g06600

LOC_Os01g03660 LOC_Os05g38170

LOC_Os02g17330 LOC_Os02g07230 LOC_Os03g60100 LOC_Os03g56320 LOC_Os02g57670 LOC_Os04g02780 LOC_Os07g37830

LOC_Os10g25210 LOC_Os08g07330 LOC_Os09g28580 LOC_Os09g12300 LOC_Os07g12390 LOC_Os11g13620

LOC_Os02g37090 LOC_Os04g48800 LOC_Os05g07140 LOC_Os06g07000 LOC_Os06g12320

LOC_Os01g66000 LOC_Os07g47470 LOC_Os09g04720 LOC_Os02g58120 LOC_Os09g27050

LOC_Os11g26150 LOC_Os02g36550 Unfolded protein binding

LOC_Os04g31760 LOC_Os08g03570 LOC_Os01g15320 LOC_Os09g32330

LOC_Os02g36530

LOC_Os10g19970 LOC_Os05g19954 LOC_Os02g53580 LOC_Os01g72690 LOC_Os09g26620

LOC_Os10g10175 LOC_Os03g49250 LOC_Os06g51110 LOC_Os02g49880 LOC_Os02g40664 LOC_Os03g34040 LOC_Os07g34950 LOC_Os02g51910 LOC_Os02g04160

LOC_Os01g05080 LOC_Os12g01880 LOC_Os06g33549 LOC_Os03g14280 LOC_Os01g13120 LOC_Os03g24020

LOC_Os09g34320

pred.group.5 LOC_Os03g17580 LOC_Os08g25880 LOC_Os01g37510 LOC_Os03g28960

LOC_Os08g32540 LOC_Os12g13340

LOC_Os03g47260 LOC_Os09g31506 LOC_Os01g63690 LOC_Os04g39950 LOC_Os06g42020 LOC_Os08g40610 LOC_Os01g70570

LOC_Os09g07200 LOC_Os03g47980

LOC_Os06g09180 LOC_Os05g22930 LOC_Os04g53230 LOC_Os08g39050 LOC_Os12g27994

LOC_Os03g53860 LOC_Os04g33490 LOC_Os06g12660 LOC_Os02g15900 LOC_Os11g29970 LOC_Os11g26920 LOC_Os03g16780 LOC_Os08g33630 LOC_Os05g06340 LOC_Os05g51160

LOC_Os04g35760 LOC_Os02g05692

LOC_Os06g43510 LOC_Os04g29090 LOC_Os01g24090 LOC_Os07g42880 LOC_Os12g34390 LOC_Os05g50654

LOC_Os07g47110

LOC_Os05g51620 LOC_Os10g35644

LOC_Os09g34000 LOC_Os12g42190 LOC_Os07g47250

LOC_Os06g05440

LOC_Os10g26720

LOC_Os07g12640

LOC_Os02g40454 LOC_Os05g30700

LOC_Os11g05552 LOC_Os03g43930 LOC_Os03g14030 LOC_Os06g43810 LOC_Os05g25480 LOC_Os06g43430 LOC_Os11g14570 LOC_Os12g10640 ATPase regulator activity LOC_Os03g22490 LOC_Os01g18320 LOC_Os06g47840

LOC_Os06g34070 LOC_Os09g03890 LOC_Os04g11390 LOC_Os02g05810 LOC_Os03g62630 LOC_Os12g39180

LOC_Os01g71330

LOC_Os01g12210

LOC_Os03g01180 LOC_Os07g38110 LOC_Os02g02740 LOC_Os04g47180

LOC_Os02g46490 LOC_Os01g12770

LOC_Os03g05430 LOC_Os03g55120 LOC_Os07g46340 LOC_Os11g14020

LOC_Os02g45460 LOC_Os08g28570 LOC_Os07g19030 LOC_Os03g53640 LOC_Os03g48230

LOC_Os05g01970 LOC_Os05g30220 LOC_Os04g55730 LOC_Os09g26260

LOC_Os04g31040 LOC_Os08g03630 LOC_Os02g10190 LOC_Os09g22330 LOC_Os04g36800 LOC_Os01g08440 LOC_Os01g31870 LOC_Os12g13110

LOC_Os02g53150 LOC_Os03g52070

LOC_Os04g02880 LOC_Os06g18010 LOC_Os07g14890 LOC_Os05g34380 LOC_Os11g09680

LOC_Os04g43800

LOC_Os11g26160

LOC_Os11g09474 LOC_Os05g33760 LOC_Os04g48140 LOC_Os01g64660 LOC_Os03g56380

LOC_Os03g31150 LOC_Os05g47780 LOC_Os03g17174 LOC_Os03g43470 LOC_Os10g41170 LOC_Os04g24110 LOC_Os03g02690

LOC_Os05g37530

LOC_Os05g47790

LOC_Os06g10280 LOC_Os08g07970 LOC_Os07g32340 LOC_Os01g10810 LOC_Os01g14580 LOC_Os08g39430 LOC_Os03g57349

LOC_Os04g37490 LOC_Os07g06990 LOC_Os04g37730 LOC_Os12g31370 LOC_Os01g38610 LOC_Os12g02640 LOC_Os01g53150 LOC_Os06g49870 LOC_Os06g37690 LOC_Os01g65400

LOC_Os11g39190 LOC_Os03g51530

LOC_Os07g06644 LOC_Os10g28970

LOC_Os06g28820 LOC_Os01g66720 LOC_Os01g65210

LOC_Os09g25050 LOC_Os07g38550 LOC_Os03g48840 LOC_Os03g61620 LOC_Os11g02440

LOC_Os01g52250 LOC_Os01g39960

LOC_Os06g13100 LOC_Os03g10590 LOC_Os03g59160 LOC_Os05g01910

LOC_Os10g36000 LOC_Os03g22350 LOC_Os08g07840

LOC_Os04g33110 LOC_Os03g54000 LOC_Os10g06610 LOC_Os02g44980

LOC_Os03g51440 LOC_Os06g51330 LOC_Os08g16010

LOC_Os03g07190 LOC_Os11g31900 LOC_Os02g54200

LOC_Os09g02814 LOC_Os10g08474

LOC_Os03g45779 LOC_Os08g37940

LOC_Os02g51570 LOC_Os04g38870

LOC_Os08g15460 LOC_Os04g53460 LOC_Os06g09280 LOC_Os06g10750 LOC_Os03g56840 LOC_Os02g38240 LOC_Os03g63380

LOC_Os04g54380

LOC_Os06g12060

LOC_Os03g59180 LOC_Os01g33540

LOC_Os09g34160

LOC_Os06g45100 LOC_Os03g13600

LOC_Os11g05540 LOC_Os03g03990

LOC_Os08g39850 LOC_Os01g67370

LOC_Os12g40500 LOC_Os02g51810 LOC_Os04g23580 LOC_Os01g07610 LOC_Os01g74030 LOC_Os03g29970 LOC_Os03g14642 LOC_Os11g43320 LOC_Os01g68524

LOC_Os05g50950 LOC_Os07g32900 LOC_Os07g09770 LOC_Os03g06910 LOC_Os09g36990

LOC_Os03g44820 LOC_Os01g34060 LOC_Os05g46770 LOC_Os10g40720 LOC_Os02g05890 LOC_Os04g46940 LOC_Os01g02080 LOC_Os06g12160 LOC_Os09g30410

LOC_Os01g40640

LOC_Os01g41550 LOC_Os10g35840

LOC_Os06g01966 LOC_Os10g40260 LOC_Os01g43895 LOC_Os09g37730 LOC_Os06g12649 LOC_Os09g39034 LOC_Os08g03310

LOC_Os06g44540

LOC_Os03g32499 LOC_Os03g57149

LOC_Os01g44220

LOC_Os08g02130

LOC_Os04g32540 LOC_Os04g31460 LOC_Os09g34150 LOC_Os08g01270 LOC_Os10g35950

LOC_Os10g40830 LOC_Os04g56730 LOC_Os02g50280

LOC_Os06g13050 LOC_Os01g68140

LOC_Os10g42150 pred.group.14 LOC_Os02g53750 LOC_Os06g19980 LOC_Os03g58100

LOC_Os02g42700 LOC_Os09g27580

LOC_Os02g57290 LOC_Os12g09670 LOC_Os04g41229 LOC_Os01g63990 LOC_Os08g10470 LOC_Os12g41390

LOC_Os05g24150

LOC_Os03g47970 LOC_Os07g25390 LOC_Os06g06180 LOC_Os02g17360 LOC_Os05g40780

LOC_Os05g51690 LOC_Os03g24160 LOC_Os10g17260 LOC_Os07g41370 LOC_Os01g67590 LOC_Os07g43370 LOC_Os06g13000 LOC_Os03g56950 LOC_Os06g41360 LOC_Os06g45090 LOC_Os01g66940 LOC_Os03g26210 LOC_Os05g38570

LOC_Os12g15314 LOC_Os12g01540 LOC_Os05g04550 LOC_Os02g39140 LOC_Os03g54040 LOC_Os10g30550

LOC_Os01g49000 LOC_Os08g16600

LOC_Os01g31050 LOC_Os12g13120 LOC_Os03g52910

LOC_Os12g17910 LOC_Os03g58250 LOC_Os03g50530

LOC_Os10g29470

LOC_Os03g32490

LOC_Os02g50374

LOC_Os04g57630 LOC_Os01g18060 LOC_Os04g57410 LOC_Os02g46400 LOC_Os11g11330 LOC_Os08g22852 LOC_Os03g61080 LOC_Os03g28160

LOC_Os03g55970 LOC_Os02g10160

LOC_Os03g16870

LOC_Os02g47744

LOC_Os01g10800

LOC_Os04g45090 LOC_Os08g44340 LOC_Os11g02630

LOC_Os01g43390 LOC_Os01g57410 LOC_Os04g40950 LOC_Os05g25840

LOC_Os04g24140

LOC_Os06g04070 LOC_Os12g33210 LOC_Os06g19095

LOC_Os08g26870 LOC_Os10g39390

LOC_Os05g31140 LOC_Os05g28740 LOC_Os03g06630 LOC_Os06g01400 LOC_Os03g24590

LOC_Os08g45130 LOC_Os10g32970

LOC_Os01g65320 LOC_Os01g39890 LOC_Os01g50370 LOC_Os10g35090

LOC_Os03g55590 LOC_Os02g06360 pred.group.29

LOC_Os09g34010 LOC_Os11g03240

LOC_Os08g44870 LOC_Os05g14270 LOC_Os05g51470 LOC_Os06g45340 LOC_Os02g15540 LOC_Os07g06060

LOC_Os11g34760 LOC_Os02g51480

LOC_Os12g10720 LOC_Os06g38056

LOC_Os06g21580 LOC_Os03g47140 LOC_Os01g49470 LOC_Os06g40740

LOC_Os01g11550

LOC_Os12g18400 LOC_Os04g08350

LOC_Os01g72340 LOC_Os11g29370

LOC_Os02g37654 LOC_Os06g14180

LOC_Os03g51840 LOC_Os09g23660

LOC_Os06g01250 LOC_Os11g10320 LOC_Os02g07044 LOC_Os08g33750 LOC_Os10g34580

LOC_Os12g43340 LOC_Os04g47810

LOC_Os05g45760 LOC_Os03g24880

LOC_Os05g03620 LOC_Os07g07900 LOC_Os05g43770

LOC_Os07g07930

pred.group.57

LOC_Os12g19304 LOC_Os04g34620

LOC_Os05g36270

LOC_Os07g23150

LOC_Os02g46410 LOC_Os10g10180

LOC_Os02g21810 LOC_Os03g12990 LOC_Os07g32630 LOC_Os05g25850 LOC_Os01g43040 LOC_Os11g04020

LOC_Os11g39540

LOC_Os03g63300 LOC_Os11g15624 LOC_Os11g11320

LOC_Os05g38590

LOC_Os12g10630 LOC_Os03g28170 LOC_Os08g10649 LOC_Os11g41710 LOC_Os03g08310

LOC_Os01g08020

pred.group.53

LOC_Os09g25620

LOC_Os05g07940

LOC_Os07g28610

LOC_Os10g21266

LOC_Os03g09060 LOC_Os03g53150

LOC_Os08g37432 LOC_Os07g42770 LOC_Os02g02000 LOC_Os05g32210 LOC_Os10g02340 LOC_Os08g39600

LOC_Os01g13800 LOC_Os05g08170 LOC_Os07g20340 LOC_Os10g25430

LOC_Os08g37650 LOC_Os03g55240 LOC_Os01g53370

LOC_Os06g08080

LOC_Os01g34790

LOC_Os05g49220

LOC_Os07g23810

LOC_Os11g19730 LOC_Os06g06970

LOC_Os10g08580 LOC_Os02g49790 LOC_Os03g04140

LOC_Os03g10850 LOC_Os01g45200

LOC_Os03g37490 LOC_Os11g32650

LOC_Os12g38770 LOC_Os04g54640

LOC_Os02g20360

LOC_Os07g15460

LOC_Os09g21230 LOC_Os08g42720 LOC_Os04g33260 LOC_Os03g27770

LOC_Os02g20490 LOC_Os01g54940 LOC_Os09g21250

LOC_Os04g36070 AP2TFgroup(#21)

LOC_Os01g70580

LOC_Os09g27660 LOC_Os06g48960

LOC_Os06g18670 LOC_Os10g08319

11562.t00032

LOC_Os07g34740

LOC_Os11g07890

LOC_Os05g32474

LOC_Os05g44300

LOC_Os06g06170 LOC_Os06g36170

LOC_Os03g13390

LOC_Os02g51404 LOC_Os12g05230

LOC_Os05g45350

LOC_Os03g20900 LOC_Os01g41240

LOC_Os02g46970

LOC_Os01g47410

LOC_Os02g09590

LOC_Os01g49670

LOC_Os02g46260

LOC_Os09g20240 LOC_Os10g35150

LOC_Os01g63830

LOC_Os06g37570 Ribosome components

pred.group.11 LOC_Os01g25610

LOC_Os05g16660 LOC_Os02g38020 LOC_Os01g10880

LOC_Os01g04800

LOC_Os03g59550 LOC_Os03g63590 LOC_Os02g56960 LOC_Os06g40040

LOC_Os12g42380

LOC_Os08g33050

LOC_Os03g19930 LOC_Os08g39500

LOC_Os01g13080

LOC_Os12g15460

LOC_Os09g19560 LOC_Os04g42200

LOC_Os03g14520

LOC_Os07g02280

LOC_Os03g61790

LOC_Os07g42170 LOC_Os12g07050 LOC_Os03g40550

LOC_Os09g32500

LOC_Os05g17849

LOC_Os04g32150 LOC_Os02g55940

LOC_Os02g56020 LOC_Os05g41610

LOC_Os08g38020

LOC_Os03g18380 LOC_Os10g03660 LOC_Os11g29400

LOC_Os01g12610 LOC_Os07g04170

LOC_Os01g59730 LOC_Os08g01380 LOC_Os05g06630 LOC_Os08g11450 LOC_Os04g51480

LOC_Os05g30340 LOC_Os03g08450 LOC_Os03g51984

LOC_Os08g35630

LOC_Os03g54890

pred.group.44 LOC_Os01g72880 LOC_Os01g27520 LOC_Os08g17390 LOC_Os12g07820 LOC_Os04g32350 pred.group.25

LOC_Os06g51460

LOC_Os12g30620

LOC_Os10g32920

LOC_Os02g44100 LOC_Os12g33240 LOC_Os05g51119 LOC_Os09g31502 LOC_Os08g32690 LOC_Os01g49290

LOC_Os09g18320 LOC_Os02g05950 LOC_Os12g10750

LOC_Os03g55164 LOC_Os01g44050 LOC_Os02g02424

LOC_Os04g40010 pred.group.45

LOC_Os01g73950 LOC_Os12g35270 LOC_Os06g02600 LOC_Os07g08660

LOC_Os02g39410 LOC_Os01g40110 LOC_Os03g56930

LOC_Os05g48320 LOC_Os08g33154 LOC_Os06g16410

LOC_Os08g01780 LOC_Os06g47544

LOC_Os10g13914 LOC_Os07g01870

LOC_Os02g56940 LOC_Os03g58430

LOC_Os08g38880 LOC_Os02g40880

LOC_Os02g46620 LOC_Os02g02850 LOC_Os01g48720 LOC_Os03g14450

LOC_Os09g28480 LOC_Os01g61814 LOC_Os03g62720 LOC_Os01g71990 LOC_Os06g09420

LOC_Os05g32680 LOC_Os11g34160 LOC_Os12g07350 LOC_Os01g16310 LOC_Os07g44330 LOC_Os04g42000 LOC_Os03g31134 pred.group.13 LOC_Os03g10880 LOC_Os01g06280 LOC_Os02g21580

LOC_Os05g41100 LOC_Os02g47940 LOC_Os08g10608

LOC_Os09g34230 LOC_Os03g61240 LOC_Os02g32590 LOC_Os03g55850 LOC_Os04g46960 LOC_Os04g54620 LOC_Os09g32520

LOC_Os03g37970 LOC_Os09g25880

LOC_Os02g26550

LOC_Os07g47040 LOC_Os05g45270 LOC_Os01g13150 LOC_Os04g50990

LOC_Os03g19940 LOC_Os08g15030

LOC_Os01g04030 LOC_Os11g05810

LOC_Os03g63760

LOC_Os01g02860 LOC_Os04g59600

LOC_Os02g04740 LOC_Os09g36830 LOC_Os01g09750 LOC_Os02g57720 LOC_Os04g48484 LOC_Os08g44480 LOC_Os03g29920 LOC_Os01g01330 LOC_Os05g04070 LOC_Os02g32760 LOC_Os01g01350 LOC_Os07g43310

LOC_Os12g42150 LOC_Os02g46956 LOC_Os02g19970 LOC_Os02g54710 LOC_Os10g41930 LOC_Os05g14940

LOC_Os05g39374

LOC_Os11g43620 LOC_Os02g40490

LOC_Os03g53660 LOC_Os01g01060

LOC_Os02g01610 LOC_Os07g22950 LOC_Os10g41410

LOC_Os01g13000 pred.group.19

LOC_Os02g13530

LOC_Os01g14830

LOC_Os10g33760 LOC_Os01g43220 LOC_Os07g33790 LOC_Os02g56990

LOC_Os06g21380 LOC_Os08g03450 LOC_Os06g07540 LOC_Os06g40330 LOC_Os04g35190 LOC_Os01g66379

LOC_Os06g10530 LOC_Os01g55290

LOC_Os03g18570

LOC_Os04g47190 LOC_Os02g01920 LOC_Os05g43460 LOC_Os08g25700 LOC_Os07g14650 LOC_Os02g48400 LOC_Os01g11650 LOC_Os12g24170

LOC_Os02g06820 LOC_Os04g22360 LOC_Os05g03030 LOC_Os02g01332 LOC_Os07g12160 LOC_Os08g41810 LOC_Os03g39760

LOC_Os04g54340 LOC_Os02g45420 LOC_Os07g12320 LOC_Os03g58050 LOC_Os07g47420 LOC_Os07g07709 LOC_Os06g16980 LOC_Os01g57910 LOC_Os01g72930 LOC_Os02g56900

LOC_Os05g01590 LOC_Os02g49780 LOC_Os02g13520 LOC_Os06g07580 LOC_Os03g01900

LOC_Os01g06490 LOC_Os07g05010

LOC_Os06g16290 LOC_Os04g52100

LOC_Os12g36620 LOC_Os04g42420

LOC_Os08g40510 LOC_Os03g29460 LOC_Os12g21798 LOC_Os07g48820 LOC_Os03g44660

LOC_Os11g07930 LOC_Os09g38310

LOC_Os11g14544 LOC_Os05g48220 LOC_Os08g06040

LOC_Os06g37440

LOC_Os05g39960 LOC_Os03g42480 LOC_Os07g45439 LOC_Os05g28750 LOC_Os07g48596 LOC_Os02g44820

LOC_Os03g38260 LOC_Os02g36360 LOC_Os07g40810

LOC_Os07g13170 LOC_Os11g13420 LOC_Os09g01930 LOC_Os03g60400 LOC_Os06g02500

LOC_Os10g41380 LOC_Os02g53050 LOC_Os03g51260 LOC_Os01g05860 LOC_Os06g48500 LOC_Os04g57950

LOC_Os01g13720 LOC_Os06g02510 LOC_Os09g26310

LOC_Os11g24610 LOC_Os11g35320 LOC_Os04g57670 LOC_Os11g02379 LOC_Os01g73160

LOC_Os07g08330 LOC_Os02g18090

LOC_Os07g44140 LOC_Os07g41750

LOC_Os01g12270 LOC_Os01g16890

LOC_Os04g31010 LOC_Os05g31480 LOC_Os08g06885 LOC_Os08g16880 LOC_Os03g10190 LOC_Os02g02590 LOC_Os07g37230 LOC_Os12g38560

LOC_Os05g35290 LOC_Os02g47760

LOC_Os03g53845

LOC_Os12g32230 LOC_Os03g08050

LOC_Os03g11260 LOC_Os07g20420 LOC_Os07g42950 LOC_Os01g68310 LOC_Os04g35090 LOC_Os07g35880 LOC_Os03g57280

LOC_Os06g35730

LOC_Os05g27790

LOC_Os09g26540 LOC_Os04g37470

LOC_Os01g52594 LOC_Os07g10880 LOC_Os03g12890 LOC_Os11g48070 LOC_Os01g26120 LOC_Os07g25440 LOC_Os05g05300

LOC_Os07g37420 LOC_Os11g03400 LOC_Os05g33280 LOC_Os07g36190 LOC_Os02g08110 LOC_Os09g39540

LOC_Os09g30474 LOC_Os01g48500 LOC_Os03g61600

LOC_Os12g05430

LOC_Os03g63260 LOC_Os04g08740 LOC_Os01g16550

LOC_Os02g46440 LOC_Os01g16940 LOC_Os07g40300 LOC_Os02g45150

LOC_Os02g14530 LOC_Os01g71010 LOC_Os08g05750 LOC_Os02g38180

LOC_Os01g18840 LOC_Os12g03899 LOC_Os10g35450 LOC_Os01g47460

LOC_Os07g09330 LOC_Os11g34850 LOC_Os04g53630 LOC_Os05g49230 LOC_Os05g34040 LOC_Os06g22030 LOC_Os03g24950 LOC_Os01g71220

LOC_Os07g22350

LOC_Os11g38959 LOC_Os07g09450 LOC_Os07g33330 LOC_Os06g36080 pred.group.21 LOC_Os03g58204 LOC_Os10g43050 LOC_Os02g46850 LOC_Os05g35580

LOC_Os09g10690

LOC_Os03g59320 LOC_Os07g05580 LOC_Os12g03090

LOC_Os03g14530 LOC_Os07g26740 LOC_Os08g40630

LOC_Os04g51920 LOC_Os01g23990

LOC_Os07g47780 LOC_Os03g55280 LOC_Os03g05910 LOC_Os03g25850 LOC_Os11g01220 LOC_Os12g40115

LOC_Os12g07210 LOC_Os04g52450 LOC_Os05g20304 LOC_Os03g31090 LOC_Os01g09280

LOC_Os01g08770 LOC_Os08g15050 LOC_Os11g34350

LOC_Os03g58720

LOC_Os01g66500 LOC_Os03g52690 LOC_Os03g04690 LOC_Os06g21480 LOC_Os05g50980 LOC_Os10g27190

LOC_Os07g42450 LOC_Os03g21530

LOC_Os06g48510 LOC_Os08g05530 LOC_Os05g23850 LOC_Os05g47870 LOC_Os12g05440

LOC_Os03g22270 LOC_Os11g32500 LOC_Os03g03820 LOC_Os03g28410 LOC_Os09g33930 LOC_Os03g58810 LOC_Os04g47520 LOC_Os01g46070 LOC_Os04g56760

LOC_Os12g41830 LOC_Os05g48070

LOC_Os02g57820 LOC_Os12g05655 LOC_Os03g40180

LOC_Os01g34350 LOC_Os06g39880

LOC_Os09g37270 LOC_Os02g09720 LOC_Os05g43970 LOC_Os05g35460 LOC_Os03g63690

LOC_Os02g57540 LOC_Os09g37006 LOC_Os09g07120 LOC_Os03g63730 LOC_Os03g27280

LOC_Os12g14110 LOC_Os01g11370 LOC_Os04g32460 LOC_Os02g12600 LOC_Os01g62870 LOC_Os04g13430

LOC_Os05g46430 LOC_Os12g42390 LOC_Os07g07490

LOC_Os01g71690 LOC_Os05g30530 LOC_Os03g03164 LOC_Os03g22880 LOC_Os05g12130

LOC_Os08g07290

LOC_Os01g10820 LOC_Os04g58280 LOC_Os08g20130 LOC_Os07g41570

LOC_Os06g10510 LOC_Os05g11710 LOC_Os06g13950 LOC_Os12g05320

LOC_Os03g18840 LOC_Os11g44810 LOC_Os02g49230 LOC_Os01g65350 LOC_Os06g22820 LOC_Os12g10730 LOC_Os01g61710 LOC_Os02g15610 LOC_Os05g28950

LOC_Os07g47710 LOC_Os05g41110

LOC_Os09g15044 LOC_Os01g16340

LOC_Os02g53450 LOC_Os08g20544

LOC_Os06g49140 LOC_Os03g02420 LOC_Os06g35470 LOC_Os04g44190 LOC_Os04g17064 LOC_Os08g09210 LOC_Os03g50380 LOC_Os07g12250

LOC_Os03g25450 LOC_Os07g07974 LOC_Os06g11180 LOC_Os05g42300 LOC_Os04g39060 LOC_Os08g33350 LOC_Os03g49210 LOC_Os07g11490

LOC_Os07g07950

LOC_Os12g17880

LOC_Os05g48260

LOC_Os12g38310 LOC_Os08g16810 LOC_Os02g16650

LOC_Os03g27260 LOC_Os09g31446 LOC_Os09g39500 LOC_Os06g28740

LOC_Os03g41612 LOC_Os06g43760 LOC_Os04g31340 LOC_Os02g01230 LOC_Os03g04750 LOC_Os01g64090 LOC_Os02g38970

LOC_Os02g28810

LOC_Os07g42354 LOC_Os08g19374 LOC_Os08g42550 LOC_Os01g03310 LOC_Os05g33630 LOC_Os04g44400 LOC_Os03g18620 LOC_Os08g15090 LOC_Os06g33170 LOC_Os12g08800 LOC_Os03g62870 LOC_Os03g06680 LOC_Os03g02200

LOC_Os10g05069 LOC_Os04g36890 LOC_Os12g10740 LOC_Os01g67134 LOC_Os03g10520 LOC_Os07g34620 LOC_Os04g58690

LOC_Os09g36240 LOC_Os04g52860 LOC_Os02g14929 LOC_Os01g40070

LOC_Os08g44320

LOC_Os12g41430 LOC_Os08g44450 LOC_Os07g45120 LOC_Os08g04400 LOC_Os03g56241

LOC_Os04g46920 LOC_Os02g40514

LOC_Os03g56470

LOC_Os06g06410 LOC_Os08g14660

LOC_Os09g31180 LOC_Os01g03680 LOC_Os05g34460 LOC_Os11g05370 LOC_Os06g41384

LOC_Os10g30640 LOC_Os11g03680 LOC_Os03g61030 LOC_Os05g45220 LOC_Os06g50070 LOC_Os02g13350 LOC_Os01g28989 LOC_Os05g06310 LOC_Os01g27730 LOC_Os11g05180 LOC_Os07g31550 LOC_Os01g39770 LOC_Os01g62950 LOC_Os02g54780 LOC_Os12g39220 LOC_Os11g09970 LOC_Os02g37870 LOC_Os06g07190 LOC_Os01g37820 LOC_Os07g29320

LOC_Os01g60790 LOC_Os02g04840 LOC_Os07g41564 LOC_Os06g20030 LOC_Os12g12115

LOC_Os12g22030

LOC_Os08g23410 LOC_Os11g07040 LOC_Os02g35950

LOC_Os04g56670

LOC_Os09g19940 LOC_Os11g36050

LOC_Os07g37620 LOC_Os03g60380 LOC_Os01g38970 LOC_Os07g06820 LOC_Os05g49350 LOC_Os10g32820 pred.group.15 LOC_Os06g33530 LOC_Os09g07820

LOC_Os08g44300 LOC_Os01g73450 LOC_Os05g06014 LOC_Os03g63410 LOC_Os05g19370 LOC_Os04g45220

LOC_Os03g10420

LOC_Os07g43350 LOC_Os02g34840 LOC_Os08g14770 LOC_Os08g23710

LOC_Os07g49110 LOC_Os12g43830 LOC_Os02g53070 LOC_Os10g08930 LOC_Os08g42560 LOC_Os08g23730 LOC_Os02g33140 LOC_Os04g51900 LOC_Os09g37446 LOC_Os05g24970 LOC_Os03g17470 LOC_Os04g31000 LOC_Os10g33450 LOC_Os05g47890 LOC_Os02g42210 LOC_Os07g10660 LOC_Os12g07260 LOC_Os01g62350

LOC_Os06g01470

LOC_Os05g36360

LOC_Os02g44810

LOC_Os07g36210 LOC_Os03g63400

LOC_Os01g73220 LOC_Os07g43510 LOC_Os10g22600 LOC_Os04g38620 LOC_Os04g33510 LOC_Os01g32790 LOC_Os03g21560

LOC_Os04g48060 LOC_Os04g40560

LOC_Os07g16950 LOC_Os04g35560 LOC_Os09g37240

LOC_Os01g65730 LOC_Os03g20120 LOC_Os05g50310 LOC_Os03g22320 LOC_Os03g07580

LOC_Os08g27090

LOC_Os06g02580 LOC_Os09g11240 LOC_Os03g63550 LOC_Os04g01480 LOC_Os01g57630

LOC_Os08g42760 LOC_Os06g45390 LOC_Os02g55370 LOC_Os09g32300 LOC_Os03g22180 LOC_Os06g40860 LOC_Os04g38970 LOC_Os04g42020 LOC_Os01g62970 LOC_Os10g27450 LOC_Os02g53080 LOC_Os05g49700 LOC_Os01g13190 LOC_Os07g12600

LOC_Os12g08790

LOC_Os04g50660 LOC_Os01g72780

LOC_Os02g45820 LOC_Os04g39700

LOC_Os02g47140 LOC_Os02g01350 LOC_Os06g46540

LOC_Os02g42585

LOC_Os03g48010 LOC_Os01g29469 LOC_Os01g20830 LOC_Os07g41190 LOC_Os06g04210 LOC_Os01g68390

LOC_Os03g32620 LOC_Os05g51830 LOC_Os06g01640

LOC_Os02g45380 LOC_Os06g13570 LOC_Os03g22740 LOC_Os06g36700 LOC_Os06g18830

LOC_Os11g37510

LOC_Os03g18160 LOC_Os04g39220 LOC_Os12g07060 LOC_Os03g06379 LOC_Os03g22340 LOC_Os07g20580 LOC_Os05g02130 LOC_Os01g05560 LOC_Os09g29790 LOC_Os11g40200 LOC_Os03g38730 LOC_Os05g49880 LOC_Os01g40310 LOC_Os07g08950 LOC_Os03g15540

LOC_Os01g07080 LOC_Os01g54240

LOC_Os12g10184 LOC_Os10g35280

LOC_Os08g17820 LOC_Os11g37080

LOC_Os07g14530

LOC_Os11g03990 LOC_Os12g42280 LOC_Os01g46060 LOC_Os05g05840 LOC_Os07g36450

LOC_Os09g28730 LOC_Os01g09510 LOC_Os02g54110

LOC_Os02g44370 LOC_Os04g40840 LOC_Os01g62040 LOC_Os02g20870 LOC_Os10g41790

LOC_Os07g12650 LOC_Os02g18784 LOC_Os12g04740 LOC_Os10g35690 LOC_Os09g32532 LOC_Os03g63680

LOC_Os07g23244 LOC_Os05g41150 LOC_Os03g26870 LOC_Os05g05820

LOC_Os05g33570 LOC_Os06g04330 LOC_Os07g01920 LOC_Os08g13690 LOC_Os11g13410 LOC_Os02g56014 LOC_Os06g30420 LOC_Os08g41750

LOC_Os01g07200 LOC_Os01g57420 LOC_Os08g04120 LOC_Os02g04970

LOC_Os02g10650

LOC_Os09g39970 LOC_Os07g07220

LOC_Os12g08700 LOC_Os07g44190

LOC_Os11g29520 LOC_Os01g72570 LOC_Os04g23910

LOC_Os02g21660 LOC_Os07g33100 LOC_Os04g25410

LOC_Os08g42920 LOC_Os07g38540

LOC_Os07g31599 LOC_Os08g35880 LOC_Os03g09110 LOC_Os02g20970 LOC_Os02g02360 LOC_Os01g42980

LOC_Os05g48310 LOC_Os06g45020 LOC_Os03g61750

LOC_Os03g11690 LOC_Os03g60910

LOC_Os01g08930

LOC_Os11g34230

LOC_Os12g43120

LOC_Os07g40974 LOC_Os04g53770

LOC_Os01g70330 LOC_Os02g02810 LOC_Os03g05340 LOC_Os03g22610

LOC_Os04g34320 LOC_Os06g01370 LOC_Os03g42770

LOC_Os04g58030 LOC_Os02g35060 LOC_Os01g52490 pred.group.3 LOC_Os11g07500

LOC_Os03g53490

LOC_Os02g49150

LOC_Os12g09290 LOC_Os12g39640 LOC_Os05g48422

LOC_Os02g49360

LOC_Os04g28820 LOC_Os09g24690 LOC_Os05g42270

LOC_Os04g52090 LOC_Os09g39790 LOC_Os03g31110 LOC_Os10g31864 LOC_Os02g33630 LOC_Os08g09350 LOC_Os07g10300 LOC_Os12g44160 LOC_Os12g06910

LOC_Os02g44360 LOC_Os04g33640 LOC_Os03g05220 LOC_Os03g56800

LOC_Os02g58550 LOC_Os03g10930 LOC_Os01g12440

LOC_Os01g61320 LOC_Os01g59320

LOC_Os01g63790

LOC_Os10g28360 LOC_Os09g38980

LOC_Os05g38210 LOC_Os04g11830 LOC_Os10g01820

LOC_Os06g37640 LOC_Os12g19590

LOC_Os07g36230 LOC_Os03g25260

LOC_Os03g62110

LOC_Os02g02550

LOC_Os02g29510

LOC_Os03g05540 LOC_Os06g05920 LOC_Os04g02730 LOC_Os01g73550 LOC_Os02g18940 LOC_Os04g58830 LOC_Os12g10190 LOC_Os01g16510 LOC_Os02g04660

LOC_Os06g08770 LOC_Os01g16910 LOC_Os02g34570

LOC_Os08g20000 LOC_Os04g41340 LOC_Os02g29380 LOC_Os02g10810

LOC_Os04g44070 LOC_Os01g16110

LOC_Os04g55800 LOC_Os05g30420 LOC_Os01g62210 LOC_Os01g40250 LOC_Os07g47330 LOC_Os02g38190

LOC_Os01g52010

LOC_Os12g34854 LOC_Os04g35480 LOC_Os05g49920

LOC_Os09g28100 LOC_Os10g07538

LOC_Os04g40650 LOC_Os01g59510

LOC_Os11g40600 LOC_Os04g55930 LOC_Os01g68330 LOC_Os02g39580 LOC_Os06g30320 LOC_Os02g26300

LOC_Os02g11030 LOC_Os11g11340 LOC_Os10g02650

LOC_Os11g13850 LOC_Os07g27180 LOC_Os02g04270 LOC_Os05g30240

LOC_Os07g44630 LOC_Os01g72240 LOC_Os10g40540 LOC_Os12g03822

LOC_Os03g13760 LOC_Os02g57470

LOC_Os02g49570

LOC_Os01g05010 LOC_Os03g10600 LOC_Os04g24165

LOC_Os03g37100 LOC_Os04g49580 LOC_Os01g04730 LOC_Os03g61019 LOC_Os01g54910 LOC_Os12g31800 LOC_Os02g01550 LOC_Os10g41150 LOC_Os05g35520 LOC_Os01g60280 LOC_Os09g39780 LOC_Os02g09440

LOC_Os07g01020 LOC_Os07g46620 LOC_Os01g32130 LOC_Os04g38600

LOC_Os07g46410 LOC_Os07g41790 LOC_Os07g45300 LOC_Os09g29630 LOC_Os12g01570 LOC_Os06g27970 LOC_Os04g53700

LOC_Os01g50622 LOC_Os02g03820 LOC_Os05g32190 LOC_Os01g08850

LOC_Os10g42100

LOC_Os03g27040 LOC_Os06g47570

LOC_Os05g41630 LOC_Os02g57590 LOC_Os01g58080 LOC_Os03g05720 LOC_Os03g51900 LOC_Os08g34220 LOC_Os01g64960 LOC_Os01g43060

LOC_Os01g56290 LOC_Os11g42420 LOC_Os12g10280 LOC_Os04g46310 LOC_Os09g12270 LOC_Os05g39390 LOC_Os02g20270 LOC_Os07g32350

LOC_Os08g07390 LOC_Os07g37220 LOC_Os03g06400 LOC_Os12g37360 LOC_Os02g47560 LOC_Os01g12540

LOC_Os01g71190 LOC_Os08g03990 LOC_Os04g53214 LOC_Os03g10460

LOC_Os10g25110 LOC_Os08g28180 LOC_Os07g49230 LOC_Os07g29480 LOC_Os05g49130 LOC_Os08g27824

LOC_Os12g41190

LOC_Os05g08480 LOC_Os01g27880 LOC_Os03g17340

LOC_Os06g37010 LOC_Os07g40750

LOC_Os03g60620 LOC_Os06g24190 LOC_Os11g01420 LOC_Os01g45460 LOC_Os06g05250 LOC_Os02g52470

LOC_Os08g06530 LOC_Os10g35290

LOC_Os10g03780 LOC_Os05g05220 LOC_Os06g33520

LOC_Os02g55260 LOC_Os01g74540 LOC_Os02g12370 LOC_Os07g47201 LOC_Os04g40740 LOC_Os10g35490 LOC_Os04g36062 LOC_Os02g12800 LOC_Os08g38700 LOC_Os03g53530 LOC_Os12g41590 LOC_Os02g52420 EPR1 LOC_Os02g01250

LOC_Os09g37070

LOC_Os04g02050

LOC_Os03g60930

LOC_Os07g18240

LOC_Os08g08050 LOC_Os01g01689

LOC_Os10g40824 LOC_Os03g16050

LOC_Os01g23680 LOC_Os03g23970 LOC_Os11g10740 LOC_Os12g01430 LOC_Os04g54630 LOC_Os05g47850 LOC_Os01g50970

LOC_Os07g38850 LOC_Os04g43420 LOC_Os06g01850 LOC_Os03g63720

LOC_Os12g32470 LOC_Os04g35280 LOC_Os07g46780

LOC_Os10g38710 LOC_Os03g11510 LOC_Os09g33480

LOC_Os02g39370

LOC_Os05g49300 LOC_Os03g02480 LOC_Os02g35090 LOC_Os09g26004 LOC_Os03g18270 LOC_Os02g53384 LOC_Os03g04940

LOC_Os06g39040 LOC_Os07g39960 LOC_Os07g40800

LOC_Os01g55730 LOC_Os07g40820

LOC_Os02g41500 LOC_Os01g68790 LOC_Os11g01594 LOC_Os12g31460 LOC_Os11g16480 LOC_Os05g40180 LOC_Os11g38930 LOC_Os01g32830

LOC_Os01g73300 LOC_Os12g06610

LOC_Os02g07900 LOC_Os02g03740 LOC_Os05g42110

LOC_Os07g44640

LOC_Os02g31070 LOC_Os03g20570 LOC_Os01g72205

LOC_Os06g12990

LOC_Os02g05410 LOC_Os03g55530 LOC_Os05g48290 LOC_Os01g51530

LOC_Os06g01980 LOC_Os07g08200 LOC_Os07g42250 LOC_Os10g27470

LOC_Os07g10350 LOC_Os05g41190 LOC_Os12g19549

LOC_Os12g44310 LOC_Os01g66170 LOC_Os01g01670 LOC_Os03g08140

LOC_Os04g52330 LOC_Os11g36060 LOC_Os03g12450 LOC_Os05g44320

LOC_Os05g08360

LOC_Os12g07300 LOC_Os01g01510 LOC_Os04g52960 LOC_Os02g51490 LOC_Os12g19040

LOC_Os06g14510

LOC_Os08g05880 LOC_Os03g04169 pred.group.22 LOC_Os04g47550 LOC_Os05g10630 LOC_Os11g38170

LOC_Os05g48450

LOC_Os04g55180 LOC_Os02g10540 LOC_Os03g62780 LOC_Os10g31856 LOC_Os09g33530 LOC_Os03g09920 Phosphorelay response LOC_Os06g40110 LOC_Os01g02170

LOC_Os07g10490 LOC_Os11g08210 LOC_Os02g33430 LOC_Os02g39820 LOC_Os02g09510 LOC_Os01g53890 LOC_Os03g31690 LOC_Os12g19030

LOC_Os05g49910

LOC_Os02g18450 LOC_Os01g22090

LOC_Os04g45490 LOC_Os05g30880 LOC_Os06g03690 LOC_Os10g42320 LOC_Os02g52744 LOC_Os02g09380 LOC_Os03g58640 LOC_Os01g26039 LOC_Os01g48170 LOC_Os03g18410 LOC_Os03g08810 LOC_Os04g43922 LOC_Os02g39950

LOC_Os09g34910

LOC_Os05g22730 LOC_Os06g10200 LOC_Os06g43690 LOC_Os02g17920 LOC_Os03g47610

LOC_Os01g33030 LOC_Os10g13940 LOC_Os01g69970 LOC_Os03g05800

LOC_Os05g11870 LOC_Os06g50350

LOC_Os12g31660 LOC_Os08g44000 LOC_Os02g33010 LOC_Os04g37600

LOC_Os10g32810

LOC_Os01g49330 LOC_Os07g06960 LOC_Os05g43130

LOC_Os07g28090

LOC_Os02g36710 LOC_Os02g03720

LOC_Os11g24450

LOC_Os07g48340 LOC_Os03g05280 LOC_Os05g34630 LOC_Os02g39180

LOC_Os03g16430

LOC_Os01g15010 LOC_Os07g45360

LOC_Os04g45810

LOC_Os03g33012 LOC_Os02g07050 LOC_Os01g17320

LOC_Os03g14510 LOC_Os01g45830 LOC_Os05g08990 LOC_Os02g09050 LOC_Os04g40050

LOC_Os03g19760 LOC_Os05g05470 LOC_Os03g49630 LOC_Os08g33140 LOC_Os04g57210 LOC_Os02g40460

LOC_Os04g14690 LOC_Os04g54820 LOC_Os09g15820 LOC_Os10g37110 LOC_Os01g47256

LOC_Os02g32290

LOC_Os01g17396

LOC_Os07g11110 LOC_Os07g44410

LOC_Os05g43910

LOC_Os03g29260

LOC_Os07g07540

LOC_Os02g08260 LOC_Os09g31454 LOC_Os11g11210 LOC_Os05g43582 LOC_Os02g57060 LOC_Os01g14790 LOC_Os05g43440

LOC_Os11g42490 LOC_Os02g52650

LOC_Os08g25624

LOC_Os03g52490 LOC_Os11g19250

LOC_Os04g23820 LOC_Os07g02440

LOC_Os01g64670 LOC_Os03g55380 LOC_Os03g04770

LOC_Os11g12530 LOC_Os03g08580 LOC_Os06g03780

LOC_Os04g12499 LOC_Os03g04470

LOC_Os01g45840

LOC_Os06g49310

LOC_Os02g18660 LOC_Os06g44034 LOC_Os11g32880

LOC_Os12g43600

LOC_Os06g43800 LOC_Os10g01540 LOC_Os01g25740

LOC_Os07g34650 LOC_Os02g47370

LOC_Os08g19980

LOC_Os07g42750

LOC_Os03g15920

LOC_Os03g11980 LOC_Os01g54870 LOC_Os11g32270

LOC_Os01g54020 LOC_Os06g44080

LOC_Os07g13634 LOC_Os03g14040 LOC_Os06g34450 LOC_Os11g48080

LOC_Os01g66360 LOC_Os01g06010

LOC_Os03g44430 LOC_Os02g06300 LOC_Os04g30750 LOC_Os08g38570 MADS(Os04g23910) LOC_Os01g59520

LOC_Os05g26194 LOC_Os04g45600

LOC_Os02g37600 LOC_Os05g45920 LOC_Os01g68160

LOC_Os04g09530

LOC_Os12g17320

LOC_Os01g57870 LOC_Os01g69250

LOC_Os02g51070

LOC_Os07g07320

LOC_Os08g09190 LOC_Os02g47620

LOC_Os12g33080 LOC_Os03g17590 LOC_Os03g10080

LOC_Os07g49310 LOC_Os06g09880 LOC_Os01g51980

LOC_Os03g54130

LOC_Os05g50090 LOC_Os10g37330

LOC_Os01g62220

LOC_Os05g14370 LOC_Os10g35800

LOC_Os11g30500 LOC_Os01g52630

LOC_Os10g33630 LOC_Os04g42800 LOC_Os12g34500

LOC_Os09g36800 LOC_Os06g09870 LOC_Os11g09478

LOC_Os02g44300 LOC_Os01g20940 MYB(LOC_Os08g06110) LOC_Os11g42970 LOC_Os03g03720

LOC_Os04g41510

LOC_Os02g56200

LOC_Os03g09940 LOC_Os06g39906

LOC_Os01g36090

LOC_Os05g38290 LOC_Os03g07960 LOC_Os01g19740 LOC_Os02g19530

LOC_Os06g51260 LOC_Os03g42020 LOC_Os06g34400 LOC_Os01g07730 LOC_Os01g65130 RNA binding activity

LOC_Os04g53620 LOC_Os01g63150

LOC_Os01g40094 LOC_Os07g34570 LOC_Os09g11480

LOC_Os05g33520 LOC_Os09g09320

LOC_Os10g17454

LOC_Os09g39740

LOC_Os03g56869

LOC_Os10g01100 LOC_Os06g22919

LOC_Os02g19650 LOC_Os08g38460

LOC_Os01g43380 LOC_Os12g42260

LOC_Os08g04830 LOC_Os06g47560 LOC_Os06g43250 LOC_Os08g32080 LOC_Os03g10478

LOC_Os08g40160

LOC_Os08g04450

LOC_Os01g09620

LOC_Os02g04420 LOC_Os01g59530

LOC_Os08g32930

LOC_Os04g47700

LOC_Os05g51150

Choline biosynthesis LOC_Os08g04390

LOC_Os02g56840

LOC_Os01g72370 LOC_Os03g11700 LOC_Os06g21330 LOC_Os03g14590

LOC_Os05g05140 LOC_Os08g37700 LOC_Os12g41715

LOC_Os03g01300

LOC_Os06g08540

LOC_Os02g50830 LOC_Os11g34460

LOC_Os01g31980 LOC_Os12g05330 LOC_Os01g13570 LOC_Os01g56400

LOC_Os01g68490 LOC_Os01g55580

LOC_Os09g36070

LOC_Os01g09640

LOC_Os02g03710 LOC_Os12g42970 LOC_Os05g40910 LOC_Os07g48830

LOC_Os02g40510

LOC_Os01g02930 LOC_Os08g27030

11562.t00003 LOC_Os09g38320

LOC_Os01g44430

E2F(Os02g33430) LOC_Os01g59690

LOC_Os04g33610 LOC_Os06g04200

LOC_Os11g05930

LOC_Os03g20410 LOC_Os10g28600 LOC_Os04g59480

LOC_Os03g18870 LOC_Os05g42070

LOC_Os05g46040

LOC_Os09g30130

LOC_Os06g08140 LOC_Os08g37456 LOC_Os03g20970 LOC_Os12g32240

LOC_Os01g59980 LOC_Os07g46990 LOC_Os12g42420

LOC_Os09g28310

LOC_Os02g10990 LOC_Os01g41720

LOC_Os11g12810

LOC_Os07g02250 LOC_Os01g59020 LOC_Os12g20150

LOC_Os07g29620 LOC_Os01g57350 LOC_Os04g35170

LOC_Os02g46080

LOC_Os01g55950 LOC_Os06g28194 LOC_Os12g07950 LOC_Os06g11980 11562.t00089 LOC_Os03g01750

LOC_Os09g27010

LOC_Os12g06200 LOC_Os03g17140 LOC_Os04g41120 LOC_Os01g10640 LOC_Os03g38950

LOC_Os04g25570 LOC_Os07g29420

LOC_Os05g12260

LOC_Os11g09940 LOC_Os02g35690

LOC_Os05g41490

LOC_Os10g33700 LOC_Os09g36220

LOC_Os11g16470 LOC_Os02g30410 LOC_Os01g65220

LOC_Os08g36680 LOC_Os01g50030

LOC_Os07g38440

LOC_Os03g22050 LOC_Os10g34310 pred.group.40

LOC_Os11g09590 Photosynthesis LOC_Os03g48920

LOC_Os11g13860 LOC_Os01g53330 LOC_Os01g66150 LOC_Os03g25500 LOC_Os11g36740

LOC_Os03g04340 LOC_Os01g56880

LOC_Os06g17410 LOC_Os02g36740

LOC_Os02g26210 LOC_Os03g15840 LOC_Os04g41560 LOC_Os05g35470 LOC_Os04g50860 LOC_Os01g12810

LOC_Os05g03750

LOC_Os02g42412

LOC_Os10g30850 LOC_Os02g13965

LOC_Os10g30190 LOC_Os12g43550 LOC_Os01g25810 LOC_Os01g48300 LOC_Os11g04894

LOC_Os01g10580

LOC_Os06g47940 LOC_Os02g47400

LOC_Os09g07660

LOC_Os04g57560

LOC_Os01g07800

LOC_Os07g16150

LOC_Os06g09910 LOC_Os01g07600

LOC_Os04g52520 LOC_Os09g25320

LOC_Os10g37430

LOC_Os08g31630 LOC_Os01g11910 LOC_Os04g47770 LOC_Os02g17190

LOC_Os06g18020

LOC_Os01g15900

LOC_Os03g26044 LOC_Os09g29430 LOC_Os01g44394 LOC_Os02g16450

LOC_Os04g54790

LOC_Os12g42980 LOC_Os07g01540 LOC_Os07g11739 Pigment biosynthesis LOC_Os06g19630

LOC_Os02g57010 LOC_Os08g42620 LOC_Os03g09260 LOC_Os04g02120 LOC_Os12g08220 LOC_Os01g08700 LOC_Os01g46970 LOC_Os12g29990 LOC_Os04g55210 LOC_Os01g42460 LOC_Os12g07270 LOC_Os03g62510 LOC_Os06g47200

LOC_Os11g34110 LOC_Os01g02960

LOC_Os07g41810 LOC_Os11g37560 LOC_Os02g39970 LOC_Os09g09944

LOC_Os03g11780

LOC_Os08g27150 LOC_Os10g18370 LOC_Os10g36703 LOC_Os11g42030

LOC_Os01g37130

LOC_Os03g13030

LOC_Os01g59090 LOC_Os03g17634 LOC_Os02g47510

LOC_Os10g21020 LOC_Os02g11720

LOC_Os07g41160 LOC_Os08g36140 LOC_Os04g34590

LOC_Os01g22000 LOC_Os01g06550 LOC_Os12g44030 LOC_Os03g61990 LOC_Os09g08280 LOC_Os04g51450 LOC_Os09g15670 LOC_Os08g34050 LOC_Os08g06110 LOC_Os06g21570 B) LOC_Os09g23540 LOC_Os07g05000 LOC_Os02g32469 LOC_Os05g37350

LOC_Os07g17250

LOC_Os05g12170 LOC_Os09g37620 LOC_Os07g32590 LOC_Os07g12530

LOC_Os07g36390 LOC_Os04g59570 LOC_Os03g11230 LOC_Os01g13320

LOC_Os01g04260 LOC_Os01g43270 LOC_Os02g55320

LOC_Os03g06230 LOC_Os03g62370

LOC_Os09g23110 LOC_Os01g14410 LOC_Os09g21120 LOC_Os01g62910

LOC_Os03g63360

LOC_Os09g13870 LOC_Os01g12200

LOC_Os02g45670

LOC_Os08g40530

LOC_Os07g07670

LOC_Os03g09270 LOC_Os05g51420

LOC_Os06g44840 LOC_Os05g32810 LOC_Os11g07580

LOC_Os04g31690

LOC_Os03g59774

LOC_Os07g42324 LOC_Os02g07360 LOC_Os10g18340 LOC_Os03g44210

LOC_Os03g62170 LOC_Os05g49610 LOC_Os01g66590 LOC_Os02g49820 LOC_Os05g24550 LOC_Os02g09750 LOC_Os04g44924

LOC_Os07g28460

LOC_Os01g49900 LOC_Os03g02020

LOC_Os07g09370

LOC_Os07g29410 LOC_Os01g17240 LOC_Os08g42450 LOC_Os07g10840

LOC_Os02g53400 LOC_Os07g46555

LOC_Os01g18630 LOC_Os05g09600 LOC_Os03g51880 LOC_Os07g43560

LOC_Os08g10630

LOC_Os02g58100

LOC_Os03g21890

LOC_Os07g08390 LOC_Os09g29070 LOC_Os01g08464 LOC_Os01g01340 LOC_Os06g10160 LOC_Os01g66320 LOC_Os03g21600 LOC_Os04g34600 LOC_Os12g08260

LOC_Os03g57900 LOC_Os09g23550 LOC_Os01g53020

LOC_Os04g59130

LOC_Os06g02480

LOC_Os06g08470 LOC_Os03g48390 LOC_Os01g69980 LOC_Os09g33550

LOC_Os12g39110 LOC_Os09g27030

LOC_Os07g43570

LOC_Os12g04204 LOC_Os02g17760 LOC_Os06g04460

LOC_Os01g34050 LOC_Os01g38229

LOC_Os04g41460 LOC_Os05g45840 LOC_Os06g12600 LOC_Os03g37260 LOC_Os01g55650 LOC_Os05g19380 LOC_Os03g16700

pred.group.10

LOC_Os03g62040 LOC_Os07g38790

LOC_Os09g36790 LOC_Os03g20640 10 LOC_Os02g03040

LOC_Os01g04130 LOC_Os01g21990 LOC_Os01g52500 LOC_Os02g07740 LOC_Os06g49250 LOC_Os08g36920

LOC_Os08g43190

LOC_Os01g45900

LOC_Os01g16620 LOC_Os01g62100 LOC_Os03g38540

LOC_Os10g33420

LOC_Os05g06510

LOC_Os09g28354

LOC_Os11g29790 LOC_Os01g74660 LOC_Os10g05088 LOC_Os01g49890

LOC_Os08g31750

LOC_Os03g12790 LOC_Os01g65630 LOC_Os02g33020 LOC_Os02g35100

LOC_Os02g27490

LOC_Os11g40590 LOC_Os06g01790 LOC_Os11g05260 LOC_Os11g36960

LOC_Os04g20474 LOC_Os02g36450

LOC_Os05g39590 LOC_Os01g58890 LOC_Os07g38230 LOC_Os05g40270 LOC_Os03g45720

LOC_Os03g22780 LOC_Os02g48194 LOC_Os09g37330

LOC_Os03g43420

LOC_Os06g04510

LOC_Os05g43490 LOC_Os03g44230

LOC_Os02g03670 LOC_Os08g43230

LOC_Os06g47800

LOC_Os04g53170 LOC_Os01g39020 LOC_Os07g07480 LOC_Os01g43090 LOC_Os07g29240

LOC_Os12g32940 LOC_Os11g03780 LOC_Os05g40700

LOC_Os05g47545 LOC_Os11g37390

LOC_Os01g15120

LOC_Os08g20020

LOC_Os01g63270 LOC_Os08g40180 LOC_Os07g02100 LOC_Os05g41070 LOC_Os07g47100 LOC_Os05g15160

LOC_Os07g45280 LOC_Os02g56170

LOC_Os05g29050

LOC_Os12g01580

LOC_Os02g13560 LOC_Os06g22440

LOC_Os05g39770

LOC_Os06g36400

LOC_Os04g28780

LOC_Os03g38580

LOC_Os06g09320

LOC_Os03g04390 LOC_Os11g13680

LOC_Os02g57980

LOC_Os10g31820

LOC_Os02g49470

LOC_Os03g01200 LOC_Os01g03549

LOC_Os08g12449 LOC_Os04g54430 LOC_Os04g33460

LOC_Os12g03350 LOC_Os05g47540 LOC_Os06g15990

pred.group.47

LOC_Os03g29770 ChrUn.fgenesh.gene.1

LOC_Os01g36220 LOC_Os01g47490

LOC_Os12g31000 LOC_Os03g20949

LOC_Os05g37410

LOC_Os07g08140

LOC_Os12g38750 LOC_Os04g33590

LOC_Os02g52780

LOC_Os03g47050 LOC_Os04g49570

LOC_Os07g42420

LOC_Os07g28040

LOC_Os01g50616 LOC_Os01g44390

LOC_Os04g57720

LOC_Os09g34847

LOC_Os07g36400

LOC_Os03g60580

LOC_Os01g72330

LOC_Os11g09640

LOC_Os10g28200 LOC_Os04g35100

LOC_Os01g47190

LOC_Os05g03830

LOC_Os11g03580

LOC_Os06g25010 LOC_Os03g12350 LOC_Os02g29960 LOC_Os03g49220

LOC_Os02g12680

LOC_Os05g32820 LOC_Os08g40690 LOC_Os06g49220 Count

LOC_Os12g04480 LOC_Os03g53670 LOC_Os02g32780 5

LOC_Os03g49510

LOC_Os02g50210 LOC_Os03g59070 LOC_Os12g40510 LOC_Os04g56939 LOC_Os07g18874 MYB(Os02g45670)

LOC_Os04g56710

LOC_Os07g05370

LOC_Os01g60780 LOC_Os12g08090

LOC_Os12g34890

LOC_Os03g55090

LOC_Os02g39600

LOC_Os09g25550

LOC_Os02g35180

LOC_Os02g40690

LOC_Os07g38400

LOC_Os03g04100 LOC_Os05g11810

LOC_Os05g02450

LOC_Os12g36630

LOC_Os01g42140

LOC_Os02g50350

LOC_Os08g40200 LOC_Os08g39694

LOC_Os06g01230

LOC_Os06g07120

LOC_Os06g04150 LOC_Os07g27880

LOC_Os01g27449

LOC_Os02g48184 LOC_Os05g42350 rhythmic processes LOC_Os01g51310

LOC_Os02g46030

LOC_Os11g41600

LOC_Os01g14440 LOC_Os01g56180

LOC_Os12g43880

LOC_Os10g35070

LOC_Os03g56110

LOC_Os01g67950

LOC_Os09g29970

LOC_Os01g62760 LOC_Os04g46970 bZIPTFgroup(#32) LOC_Os02g45520 LOC_Os12g04500

LOC_Os08g37115

LOC_Os12g39400

LOC_Os02g14480

LOC_Os01g52730 LOC_Os10g05250 LOC_Os04g47640 LOC_Os10g41120

LOC_Os05g04700 LOC_Os03g62760

LOC_Os01g45659

LOC_Os04g12960

LOC_Os05g03920 LOC_Os01g50910 LOC_Os02g51750 LOC_Os09g25690

LOC_Os11g05550 LOC_Os11g02240

LOC_Os09g08370 LOC_Os11g04720 LOC_Os05g46360 LOC_Os02g44770

LOC_Os05g07890 LOC_Os08g02700

LOC_Os03g53550

LOC_Os02g46610

LOC_Os11g34270 LOC_Os05g31020

LOC_Os04g37670

LOC_Os03g64219 LOC_Os01g07120

LOC_Os10g41550 LOC_Os08g01890 LOC_Os02g10520 LOC_Os02g55970 LOC_Os01g67480

LOC_Os03g61150

LOC_Os09g32640 LOC_Os11g40540 ChrSy.fgenesh.gene.37 LOC_Os02g56120 LOC_Os04g25440 LOC_Os08g02850

LOC_Os07g08320 LOC_Os11g26780

LOC_Os05g08620 LOC_Os07g48620 LOC_Os03g18300 LOC_Os10g28680

LOC_Os07g42510 LOC_Os03g17790

LOC_Os12g05690 LOC_Os08g40270 LOC_Os10g39780 LOC_Os02g43560 LOC_Os03g19270

LOC_Os01g68970

LOC_Os05g45320 LOC_Os01g62980

LOC_Os03g13300

LOC_Os12g05420 LOC_Os10g37210

LOC_Os12g12880 LOC_Os05g29810

LOC_Os03g55020 LOC_Os01g60020 LOC_Os04g45940 LOC_Os11g13670 LOC_Os08g34390

LOC_Os02g58150 11562.t00014 LOC_Os02g51450 0

LOC_Os05g33000 LOC_Os10g36180

LOC_Os09g21180

LOC_Os03g16670 LOC_Os03g50160

LOC_Os09g02180 LOC_Os07g46520

LOC_Os01g16200 LOC_Os12g25200 LOC_Os05g11380

LOC_Os04g36630

LOC_Os03g12064 LOC_Os01g12000

LOC_Os04g59540 LOC_Os09g30160 LOC_Os04g53750

LOC_Os02g08140

LOC_Os05g49170 LOC_Os08g43160 LOC_Os07g23520

LOC_Os01g52570

LOC_Os11g04860 LOC_Os03g53080 LOC_Os02g22160 LOC_Os06g51440 LOC_Os03g36790

LOC_Os03g20860

LOC_Os02g49450 LOC_Os01g10140

LOC_Os12g29330 LOC_Os02g46660 LOC_Os12g40920 LOC_Os09g31300 LOC_Os08g32980

LOC_Os03g22790

LOC_Os07g04180 LOC_Os03g62610

LOC_Os04g57739

LOC_Os02g35820 LOC_Os05g10650 LOC_Os03g59040

LOC_Os01g65660 LOC_Os05g46480 LOC_Os06g06080

LOC_Os02g02910

LOC_Os01g62630 LOC_Os05g29750 LOC_Os04g56100

pred.group.32 LOC_Os04g48010 LOC_Os12g13910

LOC_Os08g33710 LOC_Os11g11980 LOC_Os12g37530 LOC_Os12g02200

LOC_Os01g12660 0 10 100 LOC_Os02g57840 LOC_Os05g37200

LOC_Os03g56060

LOC_Os01g22980 LOC_Os03g52010 LOC_Os01g63870

LOC_Os08g19420 LOC_Os07g16970

LOC_Os05g51110 LOC_Os06g48300

LOC_Os04g19740

LOC_Os03g56370 LOC_Os06g49500 LOC_Os04g44790

LOC_Os01g53810 LOC_Os05g34050 LOC_Os08g06280

LOC_Os03g44580

LOC_Os04g57440 LOC_Os12g10320 LOC_Os05g33590 LOC_Os11g37980 LOC_Os11g30430 LOC_Os03g50470

LOC_Os02g51610 LOC_Os02g26400

LOC_Os01g39800 LOC_Os06g46740

LOC_Os01g43410 LOC_Os03g55870 LOC_Os07g09000 LOC_Os10g33900 LOC_Os07g04990 LOC_Os05g32230

LOC_Os10g22050 LOC_Os02g03620 LOC_Os02g15870

LOC_Os06g03560

LOC_Os05g49730

LOC_Os09g32240

LOC_Os05g10690 LOC_Os04g31924 LOC_Os11g26130 LOC_Os02g14980

LOC_Os10g39220 Out-degree

LOC_Os11g05050 LOC_Os09g25600

LOC_Os07g08729

LOC_Os11g26790

LOC_Os01g39860 LOC_Os03g21210 LOC_Os01g15350 LOC_Os06g17970

LOC_Os02g03330

LOC_Os01g68020 LOC_Os11g37260

LOC_Os11g26570 LOC_Os09g38090 LOC_Os03g62620

LOC_Os03g06490 LOC_Os02g43010 LOC_Os02g43920

LOC_Os02g35770 LOC_Os03g19290

LOC_Os01g64750

LOC_Os08g43250

LOC_Os06g17285

LOC_Os07g39430 LOC_Os09g36520 LOC_Os03g62700 LOC_Os08g01140

LOC_Os08g01610

LOC_Os06g13720

LOC_Os05g11210

LOC_Os06g51450

LOC_Os07g01810 LOC_Os02g49440

LOC_Os09g27320

LOC_Os10g30450 LOC_Os12g05210

LOC_Os06g11090 LOC_Os05g04610

LOC_Os03g12700

LOC_Os02g45710

LOC_Os02g56500

LOC_Os01g21160 LOC_Os07g09050

LOC_Os01g45450

LOC_Os03g29960

LOC_Os12g01930

pred.group.2 LOC_Os05g03130

LOC_Os01g53060

LOC_Os09g27420

LOC_Os05g39690 LOC_Os04g51796 LOC_Os02g43330

LOC_Os09g32600

LOC_Os01g58194

LOC_Os11g26760

LOC_Os04g53810

LOC_Os07g48010

LOC_Os03g04490 PCF2(Os08g43160) LOC_Os08g01490

LOC_Os03g29240

LOC_Os12g18770

LOC_Os03g04110 LOC_Os06g12455

LOC_Os11g08100

LOC_Os04g52780 LOC_Os04g39880

LOC_Os04g44710 LOC_Os05g49260 LOC_Os05g29790 In-degree Targets

LOC_Os10g21310 LOC_Os01g54700

LOC_Os09g31430

LOC_Os01g03390

LOC_Os08g37874 LOC_Os02g52130 C) LOC_Os02g13380

LOC_Os06g30370

LOC_Os02g15520

LOC_Os01g64790

LOC_Os04g05650

LOC_Os03g49440 LOC_Os05g39070 LOC_Os02g52380 LOC_Os09g28600

LOC_Os04g01590

LOC_Os03g21380

LOC_Os01g68370 LOC_Os06g50100

LOC_Os02g17000

LOC_Os07g08420 LOC_Os03g61360

LOC_Os01g49820 Field LOC_Os11g36200

LOC_Os02g09830 LOC_Os01g73200

LOC_Os03g28260

LOC_Os02g54600

LOC_Os07g13260

LOC_Os10g37340

LOC_Os01g06460 LOC_Os02g58540

LOC_Os03g01320

LOC_Os05g39840 LOC_Os08g29760

LOC_Os09g34090

LOC_Os02g15980

LOC_Os01g08160

LOC_Os03g51390

LOC_Os03g54200

LOC_Os03g60370 LOC_Os10g35460 LOC_Os05g38460

LOC_Os11g06930 LOC_Os04g52504

LOC_Os03g24390

LOC_Os01g32780

LOC_Os04g45290 LOC_Os03g31510 LOC_Os11g06690

LOC_Os01g19480

LOC_Os12g24150 Circadian LOC_Os09g32840 1 3619

LOC_Os05g05620

LOC_Os04g39430 LOC_Os02g49060

LOC_Os09g08130

LOC_Os10g10360

LOC_Os06g50950 LOC_Os02g39330

LOC_Os01g03950

LOC_Os07g08970

LOC_Os08g37670 LOC_Os05g46240

LOC_Os03g09140

LOC_Os03g02750

LOC_Os01g06560 LOC_Os07g09420

Dehydration LOC_Os05g36260 LOC_Os01g03330

LOC_Os06g16330

LOC_Os07g07990

LOC_Os10g17489

LOC_Os02g14430 Heat LOC_Os02g54254 2 420

LOC_Os09g29510

LOC_Os11g41875

LOC_Os06g35520

LOC_Os07g35340

LOC_Os06g29730

LOC_Os05g40770

Heat and dehydration LOC_Os12g01490

LOC_Os07g17220

LOC_Os07g35380

LOC_Os08g29570 LOC_Os12g14480

LOC_Os03g49380

LOC_Os01g58500 LOC_Os11g10290

LOC_Os05g47770

LOC_Os07g35580

LOC_Os02g38840 LOC_Os10g22510

LOC_Os07g05640 LOC_Os05g34220 LOC_Os01g09800

LOC_Os12g14520

LOC_Os04g57850 3 13

LOC_Os05g43170

LOC_Os11g31950

LOC_Os02g06930 LOC_Os03g29410

LOC_Os03g18030

LOC_Os03g09910 LOC_Os05g34270

LOC_Os07g35180

LOC_Os03g03034

LOC_Os05g46840 LOC_Os09g38910

LOC_Os01g02300

LOC_Os01g71790 LOC_Os04g44900

LOC_Os07g40290 LOC_Os03g53020

LOC_Os03g11900

LOC_Os06g45890

LOC_Os04g33360 LOC_Os10g42960

LOC_Os01g64440 LOC_Os01g50420

LOC_Os04g31610

LOC_Os11g17970 LOC_Os07g48730

LOC_Os03g08320

LOC_Os06g27860

LOC_Os12g16540 LOC_Os11g11960

LOC_Os03g18770

LOC_Os06g44010

LOC_Os02g09960

LOC_Os06g11150

LOC_Os07g28850 LOC_Os01g51570 LOC_Os11g44430

LOC_Os01g43650 LOC_Os06g06360

LOC_Os06g08170

LOC_Os08g01390

LOC_Os08g42910 LOC_Os04g53994

LOC_Os03g18560

LOC_Os02g41670

LOC_Os06g18000 LOC_Os04g32620

LOC_Os05g44770

LOC_Os02g48640

LOC_Os05g47750

LOC_Os03g56250 LOC_Os12g16720

LOC_Os09g28160

LOC_Os01g74370 LOC_Os04g43340

LOC_Os01g64110 LOC_Os08g03350

LOC_Os02g02970

LOC_Os08g31850

LOC_Os01g52790 LOC_Os06g24990 LOC_Os01g19260

LOC_Os01g15340 LOC_Os03g20550

LOC_Os04g52730

LOC_Os10g33040

LOC_Os08g04110 LOC_Os03g15080

LOC_Os08g02030 LOC_Os01g66840

LOC_Os06g19690

LOC_Os08g28710 LOC_Os12g25090 LOC_Os01g50350 LOC_Os11g44380

LOC_Os04g01310

LOC_Os08g10070

LOC_Os02g57350

LOC_Os11g35490

LOC_Os08g07690

LOC_Os08g04630 LOC_Os11g30760

LOC_Os02g45780

LOC_Os04g43680 LOC_Os02g50460

LOC_Os11g47290

LOC_Os06g11450

LOC_Os10g30790 LOC_Os08g24310 LOC_Os12g36220

LOC_Os11g06150

LOC_Os03g04890

LOC_Os06g51060

LOC_Os10g33140

LOC_Os04g10060

LOC_Os02g46650

LOC_Os11g29870 LOC_Os02g50900 LOC_Os02g08420

LOC_Os04g52840

LOC_Os10g35294

LOC_Os03g29930 LOC_Os03g55800

LOC_Os08g06170 LOC_Os07g36560

LOC_Os03g12470

LOC_Os09g39960 LOC_Os03g15780

LOC_Os01g42410

LOC_Os02g11070

LOC_Os04g30930 LOC_Os07g35540

LOC_Os11g10470

LOC_Os07g37730 LOC_Os04g50790 LOC_Os04g55410

LOC_Os03g27960 LOC_Os03g32230 LOC_Os10g25230

LOC_Os01g61610 LOC_Os05g31740 LOC_Os05g33400

LOC_Os03g53200

LOC_Os04g41030 LOC_Os06g46149 LOC_Os01g02130

LOC_Os04g53720

LOC_Os03g57640

LOC_Os01g66010 LOC_Os06g51370

LOC_Os09g12560

LOC_Os03g20840

LOC_Os01g61080

LOC_Os05g44060

LOC_Os02g37590

LOC_Os01g63980

LOC_Os07g39520

LOC_Os11g08370

LOC_Os01g42790

LOC_Os06g38660 LOC_Os02g11859

LOC_Os05g46260 LOC_Os05g34480

LOC_Os06g33970

LOC_Os04g15630

LOC_Os03g47280

LOC_Os11g05380

LOC_Os01g43460

LOC_Os03g61490 LOC_Os07g02990

LOC_Os10g25290

LOC_Os07g48430

LOC_Os03g58740

LOC_Os02g43790

LOC_Os07g34280

LOC_Os02g51730

LOC_Os11g06780

LOC_Os02g41510

LOC_Os01g53920 LOC_Os02g06090

LOC_Os07g40630 LOC_Os06g12030

LOC_Os04g32480

LOC_Os05g50610

LOC_Os05g37190 WRKY (Os03g20550) Protein phosphorylation pred.group.36

Jasmonic acid responseLOC_Os07g23570

LOC_Os11g27540

LOC_Os10g25400 LOC_Os04g43200

FIGURE 6. Gene regulatory network for rice leaves across environmental conditions A) The inferred network. TFs are noted with triangles; other genes are noted with circles. The colours indicate the conditions in which the genes were differentially expressed. B) Histogram of TF out-degree frequency. C) Table of target in-degree frequency.

A) HSF TF group Activity Has HSE upstream HSE Has Binds HSE Binds

OsHsfA2a OsHsfA6 OsHsfA9 OsHsfB1 OsHsfB2a 1 OsHsfA3 OsHsfA2d OsHsfB2c Activity OsHsfA2c OsHsfB2b OsHsfC2a OsHsfC2b OsHsfA2b 2 OsHsfA7 OsHsfA4d OsHsfC1a OsHsfC1b OsHsfA3 3 OsHSFB4b OsHsfB4c OsHsfB4d Experimental conditions

B) EPR1 Expression

Activity

expressed in heat in expressed Differentially Differentially Inferred EPR1 targets EPR1 Inferred

Experimental conditions

C) bZIP TF group

Activity Inferred targets Inferred

First time point in 1 irrigated field 2 First time point in upland field 3

4 Onset of heat targets in network prior network in targets treatment stress treatment season dry wet dry wet Onset of water deficit genotype AZ PW PA AZ PW KP TD treatment Experimental conditions bioRxiv preprint doi: https://doi.org/10.1101/042317; this version posted July 5, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

FIGURE 7. Post-hoc interpretation of inferred regulatory relationships In all TFA plots, the light band shows the region between the 5th and 95th percentile of TFAs; the dark line shows the average TFA. In all heatmaps, higher levels of expression are indicated with green tones, and lower expression levels are indicated with brown tones. Sample order is maintained in all TFA and heatmap figures (Supplementary File S3). A) TFA of the HSF predictor group (top). Heatmap of TF transcript abundance of all group members (bottom). Subgroups are noted to the left of the figure. B) TFA (blue) and transcript abundance (red) of EPR1 (top). Heatmap of abundance of inferred EPR1 targets. C) TFA of bZIP predictor group (top). Heatmap of transcript abundance of targets in network prior (bottom).