and TP309 are japonica cultivars and IR24 is indica. For light treatments seedlings remained in the greenhouse for two weeks. For dark treatments, seedlings were moved after 7 days to a dark incubator (Percival Scientific, Inc., Perry, IA) and maintained at 28uC for another 7 days. Of 365 candidate genes showing at least an 8-fold light induction during our NSF45K light/dark experiment, 161 had T-DNA insertions in them corresponding to mutant lines in the plant functional genomics lab (PFG) and 45 of those had at least two insertionally mutated alleles (and corresponding mutant lines) present in the database (RiceGE, http://signal.salk.edu/cgi-bin/ RiceGE). However, 8 of the 45 had the same or nearly the same insertion sites in the two different mutant lines and so those lines were excluded. Finally, we ordered seventy-four T-DNA inser- tional lines containing mutants in 37 genes. Sixty-eight of these knockout lines (japonica cv. Dongjin or Hwayoung), after excluding six lines which had insufficient seeds, were grown in the greenhouse. We observed the phenotypes of these knockout lines for 4 weeks after they germinated. The progenies showing phenotypic changes and their wild-type siblings from individual lines were harvested to extract genomic DNAs (described below) for co-segregation analyses as indicated in Figure S9. Usually, 15– 20 rice plants are used for testing co-segregations and repeat two or three times this experiment. We selected lines having at least two co-segregating mutants and also repeated it at least twice. Genotyping of T-DNA Insertional Mutants Showing Defective Phenotypes Related to the Light Response We visually picked out progenies displaying expected pheno- types such as color defects (albino or pale green), growth retardation or oxidative stress-related symptoms. Next, genomic DNA was extracted from mutants and from their phenotypically normal siblings (Figure S9). The genotypes of the siblings in each mutant family were determined by carrying out PCRs using two sets of primers: one designed to identify the rice gene that had been knocked out by using primers containing target gene sequences in front of and behind the T-DNA insertion site, the other designed to verify the insertion of T-DNA in the gene by using primers that amplify the hygromycin phosphotransferase (hph) gene contained within the T-DNA insert (Figure S9). The primers located upstream and downstream of the T-DNA insertional sites in each line were designed based on sequence information available from the Rice Functional Genomic Express Database (Rice GE, http://signal.salk.edu/cgi-bin/RiceGE). The PCR amplifications were carried out in 20 ml volumes of a mixture that contained 20 ng of plant DNA, 106 Taq buffer, 0.2 mM dNTP, 0.5 unit Taq polymerase (Invitrogen), and 0.2 mM of the primers for 35 cycles at 94uC for 60 s, 60uC for 60 s, and 72uC for 150 s. All primers (Sigma) for genotyping are described in Table S5. RNA Isolation and Reverse Transcriptase-PCR Reaction Leaves from rice plants grown for 2 weeks in the greenhouse or in a dark incubator were collected and total RNA was isolated using TRIZOL reagent according to the manufacturer’s instruc- tions (Invitrogen, Carlsbad, CA). The total RNA was DNaseI- treated for 15 minutes then purified using the RNeasy Midi Kit (Qiagen, Germantown, MD). The total RNA was then enriched for poly-A RNA by using the Oligotex mRNA Kit (Qiagen). All steps were performed according to the manufacturer’s instructions. The quantity of total RNA and mRNA were determined by measuring absorbance at 260 nm and 280 nm by using a Nanodrop ND-1000 (Nanodrop, Wilmington, DE). In addition, the level of protein contamination in the RNA was determined Functional Analysis of Singleton and Gene Families based on the A260/A280 ratio. Only RNA samples with ratios of 2.0–2.2 were used for these experiments. Reverse transcriptase- (RT-) PCR were carried out as used in previous study [85]. Generation and Analysis of Microarray Data All hybridizations were done at the Arraycore Microarray Facility at the University of California, Davis (Arraycore@ucda- vis.edu). Probe labeling, hybridizations with the NSF45K micro- array, slide scanning and identification of spot intensity were as described (Jung et al. submitted). To minimize variations caused by experimental procedures, replicated data was normalized using the Lowess normalization method in the LMGene Package [86,87]. To identify differentially expressed genes, we used the publicly available R program LMGene developed by Rocke [86]. FDR (false discovery rate, adjusted p-value) and log2 fold changes of light over dark were generated for all genes. The expression data from these experiments are available through Gene Expression Ominibus (GEO) (Accession # GSE8261). To identify genes consistently expressed in response to light among different array platforms, we selected genes that were induced in our NSF45K array experiments and also showed at least 0.5 log2 values (1.4-fold induction) in more than two light intensity conditions of the BGI/Yale light vs. dark array data. Rice Multi-Platform Microarray Search and Analysis The Rice Multi-platform Search page (http://www.ricearray. org/matrix.search.shtml) is a tool that allows users the ability to search across four different rice oligo microarray platform types (Affymetrix, Agilent, BGI/Yale, and NSF45K) to determine which oligos from each platform represent to a common gene target. More detailed information on Rice Multi-Platform Search Tool is available at http://www.ricearray.org/matrix.search.shtml. The data from the BGI/Yale light vs. dark array dataset and the Affymetrix array data for seedling leaves and shoots for the seventeen genes in the chlorophyll biosynthesis pathway, twelve genes in the MEP pathway, and the thirty-seven genes on which functional analyses were conducted were extracted by using the rice multi-platform microarray search tool and publicly available Affymetrix and BGI/Yale array data. The detailed information on the multiplatform array data used in this study is presented in Table S6 and is also available at the NCBI GEO (http://www. ncbi.nlm.nih.gov/geo/). To make images using the multi-platform microarray data, we used TIGR MultiExperiment Viewer software (MeV, http://www.tm4.org/mev.html). We generated two tab-delimited multiple-samples files (tdms files) consisting of log2 ratios of light over dark treatment from the NSF45K and the BGI/Yale light vs. dark array datasets and of log2-transformed spot intensities of 23 Affymetrix array datasets related to development (Table S6). The tdms files were loaded onto the MeV and the resulting image data were used for creating Figure 2, Figure 4, Figure S6, and Figure S10. Analyses of Gene Families in Rice and Arabidopsis Gene families within the predicted rice proteome were identified using Pfam and BLASTP and more detail on these identifications can be found at http://www.tigr.org/tdb/e2k1/osa1/para. family/para.method.shtml. A total of 3842 paralogous protein families containing a total of 20729 proteins were identified (http://rice.tigr.org/tdb/e2k1/osa1/para.family/index.shtml). Table S7 shows the gene families we identified in rice. To identify Arabidopsis gene family members for Figure S6, we used GenomeNet browser (http://www.genome.jp/) and the protein sequences which had more than 200 score bits or 40% similarity were considered members of the same gene family. As a result, 9 PLoS Genetics | www.plosgenetics.org 14 August 2008 | Volume 4 | Issue 8 | e1000164