Functional Analysis of Singleton and Gene Families Table 1. Rice Insertional Mutant Pools Available and Estimation of Coverage for 365 Light-responsive Candidate Genes Showing at least 8-Fold Induction. Rice Insertional Mutant Pool PFG T-DNA g RTIM Tos17 g RMD T-DNA g TRIM T-DNA g Geno-plate T-DNA g ZJ T-DNA g CISRO Ac/Ds g UCD dSpm/Ds g OSTID Ds g SHIP Ds g GSNU Ds g TOTAL g N. of FSTs Mapped a 82,520 17,937 15,610 6,965 7,187 714 589 10,373 1,301 6,244 1,046 150,486 N. of Genic Regions b 20,319 3,772 4,724 2,595 2,019 179 311 2,419 751 1,379 523 26,562 7,382 695 1,418 743 582 42 52 585 147 441 93 10,585 N. of Promoter Sequences c N. of Mutants d N. of KO Genes e (%f) 277 80 46 10 16 10 4 24 6 20 3 495 161 (45.2) 35 (9.8) 34 (9.6) 10 (2.8) 13 (3.7) 1 (0.3) 4 (1.1) 21 (5.9) 6 (1.7) 12 (3.4) 3 (0.8) 205 (57.6) aThe number of flanking sequence tags (FSTs) mapped based on a total of 57142 gene models released January 24, 2007 at http://www.tigr.org/tdb/e2k1/osa1/ pseudomolecules/info.shtml. bThe number of non-redundant genic regions, including exons, introns, and 59UTRs, in the mutant pool with insertions. cThe number of non-redundant promoter regions in the mutant pool with insertions. dThe number of insertional mutants in each rice insertional mutant pool with insertions in any of the 365 light-responsive candidate genes. eThe number of knocked out (KO) genes corresponding to genic or 59UTR (within 300 bp upstream from ATG) regions of any of the 365 light-responsive candidate genes in each rice insertional mutant pool. fThe percentage of available knocked out (KO) genes in each rice insertional mutant pool relative to the 365 candidate genes. gDetailed information on the rice insertional mutant pools was obtained from the Rice Functional Genomic Express Database (http://signal.salk.edu/cgi-bin/ RiceGE?DATA = At5g53850). doi:10.1371/journal.pgen.1000164.t001 Rice Functional Genomic Express Database (http://signal.salk. edu/cgi-bin/RiceGE). Because we wanted to include 2 indepen- dently derived mutant alleles in our analysis of each candidate gene so as to help discriminate between phenotypic changes generated by somaclonal variation versus those resulting from the insertional mutations themselves, we limited our phenotypic analysis to 74 mutant lines with T-DNA insertions in a total of 37 candidate genes. The overall scheme we used for functional analysis based on our microarray experiment is presented in Figure 2A and Figure S2). We classified the 37 candidate genes for which we had corresponding mutants into two groups according to whether the candidate gene belonged to a gene family or not. There were 12 unique genes (those without gene family members) (Figure 2B and Figure S3A) and 25 belonging to gene families (see Materials and Methods). The latter class was further divided into two subgroups by considering the predominance of each gene’s expression in the light based on the NSF45K light vs. dark array dataset. As a result there were 13 predominantly expressed-light- induced gene family members (referred to as ‘‘P’’ in Figure 2B and as ‘‘Predominant’’, marked with asterisks in Figure S3B) and 12 gene family members that were not the predominantly expressed in the light (referred to as ‘‘NP’’ marked in Figure 2B and as ‘‘Non predominant’’, marked with sharps in Figure S3C). Because other more predominantly or equally expressed gene family members might compensate for a defective gene family member in the light, the non-predominantly expressed gene family members were not considered good candidates for functional analysis and were initially excluded from the functional analysis (Figure S3C). Next, we identified 5 genes, Os03g48030, Os11g05050, Os02g58790, Os09g37620, and Os09g16950, for which light responses between the NSF45K and BGI/Yale light vs. dark array datasets were significantly inconsistent and deleted them from our primary list of candidate genes for the initial round of functional analysis (Figure 2). Of these, Os03g48030 was a unique gene and Os11g05050, Os02g58790, Os09g37620, and Os09g16950 were the members of their respective gene families the most predominantly expressed in the light in the NSF45K array data set. We also included one unique gene (Os07g46460) and one predominantly light-induced gene family member (Os03g37830) in our candidate gene list based only on our own data because information on their light responses was not available among the BGI/Yale data (Figure 2). We then screened the remaining mutants, those associated with 11 unique genes and 9 predom- inantly light-induced gene family members, to determine their phenotypes (Figure 3). We also assayed the phenotypes of the knockout lines associated with the 17 genes we had eliminated from our primary list of candidate genes to check the efficiency of identifying mutant phenotypes for the not predominantly light- induced genes in a gene family and/or genes with expression patterns that weren’t consistent between the NSF45K and BGI/ Yale light vs. dark array datasets. (Detailed data regarding all 37 of the genes that were functionally analyzed are presented in Table S2.) Our criteria for selecting candidate genes that respond to light might have inadvertently eliminated from consideration genes among the BGI/Yale data that exhibit condition-dependent light responsiveness. Functional Analysis of 20 Candidate Genes We initially carried out functional analysis for 20 selected candidate genes, 11 unique genes and 9 predominantly light- induced gene family members (Figure 3A and 3B). First, we identified defective phenotypes associated with six of the 11 unique PLoS Genetics | www.plosgenetics.org 4 August 2008 | Volume 4 | Issue 8 | e1000164