Rice

The genome sequence, ab initio gene predictions, protein alignments, GeneWise predictions, and other plant EST alignments were examined using the Neomorphic/Affymetrix Annotation Station software (described by Haas and coworkers [28 (link)]). No rice transcript alignments either alone or in the context of PASA assemblies were made available to users so that we could reasonably estimate optimal gene structure annotation accuracy in the context of ab initio gene predictions and homologies to sequences derived from other organisms. A group of annotators were provided with the same data sets evaluated by EVM, only in graphical form. Annotators were instructed to model a gene structure in the targeted region that best reflected the available evidence using the Annotation Station software. Annotators were not allowed to examine the data deeper than the visual display provided. The sequence alignments themselves were not available except in the context of the glyphs highlighting their end points, and no additional sequence analyses such as running blast was allowed. The focus of this effort was not to measure the maximal accuracy of manual gene annotation accuracy in general, but only to measure the maximal possible accuracy of an automated annotation such as EVM given the restricted inputs.

Software was developed to deal with SLAF-seq data. Procedures are shown in Figure S1. All SLAF pair-end reads with clear index information were clustered based on sequence similarity. To reduce computing requirements, identical reads were merged together, and sequence similarity was detected using one-to-one alignment by BLAT [23] (link) (-tileSize = 10 -stepSize = 5). Sequences with over 90% identity were grouped in one SLAF locus.
Alleles were defined in each SLAF using the MAF evaluation. To prevent false positive results, the sequence error rate was estimated using the rice data as a control. These were obtained using the same sequencing scheme as that used with common carp (Figure S1B). True genotypes had markedly higher MAF values than genotypes containing sequence errors. Tags with sequence errors were corrected to the most similar genotype to improve data efficiency. In mapping populations of diploid species, one locus can contain at most 4 genotypes, so the groups containing more than 4 tags were filtered out as repetitive SLAFs. SLAFs with sequence depth less than 213 were defined as low-depth SLAFs and were filtered out of the following analysis. Only groups with suitable depth and fewer than 4 seed tags were identified as high-quality SLAFs, and SLAFs with 2–4 tags were identified as polymorphic SLAFs.
To evaluate the accuracy of our genotyping objectively, a Bayesian approach was proposed. Using the coverage of each allele and the number of single-nucleotide polymorphism, we calculated a posteriori conditional probability that a given individual would have a specific genotype at a corresponding locus. We proceeded as follows. Supposing there were alleles at any given locus, denoted as . For a diploid species, the number of all possible genotypes was equal to and is less than five regardless of the type of segregation of the loci. We assign a priori probability to each genotype according to the theoretical frequencies with which these genotypes would occur in such a finite probability space. For a homozygous genotype, this priori probability would equal , but it would be double that for a heterozygous genotype. Consider a pair of distinguished alleles and , the probability of sequencing one allele to another can be calculated using the following formula: Here is the average ratio of sequencing error. In our model it took on a value of 0.015 for the Illumina sequencing platform, and we used to represent the length of reads and for number of single-nucleotide polymorphisms. Based on this, we obtained the probability of allele conditioned on the genotype. , denoted as . The depth observation of allele was assumed to be , and the conditional probability of observation of each genotype can be illustrated as follows: In this way, we determined the probability of assigned genotype conditioned on the following coverage observation: The probability was translated to a genotyping quality score finally using: The final genotyping quality score value indicated the confidence with which the genotype had been called. In particular, when the difference in depth between both alleles exceeded 1∶5, the score value could be modified directly using formula (1) due to systematic bias. The upper bound of the score is 30.
This genotyping quality score was used to select qualified markers and individuals for subsequent analysis. This was a dynamic optimization process. Briefly, we counted low-quality markers for each SLAF marker and for each individual and deleted the worst markers or individuals. We repeated this process, deleting one individual or marker each time. We ceased when the average genotyping quality score of all SLAF markers reached the cutoff value, which was 13.

NB is a traditional lowland indica cultivar that originated in India and is resistant to BSR caused by Burkholderia glumae. KO is a modern lowland rice cultivar released in Japan and is susceptible to BSR^{27 (link)}. To analyse RBG1res, by crossing SL535 with KO and using marker-assisted selection to remove nontarget DNA regions, we successfully developed a NIL homozygous for RBG1res. The resulting RBG1res-NIL contains approximately 380 kb from NB on the short arm of chromosome 10 (between simple sequence repeat (SSR) markers RM474 and RM7361-1; Supplementary Table S3). By screening 3072 M₂ lines of KO mutagenized with N-methyl-N-nitrosourea according to a method described previously^{31 (link)}, we identified a null mutant (Mut-W56*) whose sequence encoding tryptophan at position 56 was changed such that a stop codon was introduced that produces a truncated protein (5.5 kD). Genomic DNA of the M₂ plants was screened with the NB51 primer set listed in Supplementary Table S3 by the targeting induced local lesions in genomes (TILLING) method as described earlier^{52 (link)}. All of the experimental research and field studies on plants (either cultivated or wild), including transgenic plant materials, complied with relevant institutional, national, and international guidelines and legislation.

To examine the expression response of the candidate genes to Mg²⁺ deficiency, seedlings of rice variety Nipponbare or the four parental lines were first grown in the 1/4 strength IRRI solution for two weeks, and then cultured in the full strength IRRI solution with 1.0 mM Mg²⁺ or without Mg²⁺ for three weeks. The roots were sampled for RNA extraction. A randomized complete block design with three replicates and a plot size of 24 seedling were used to layout the experiment. Samples were taken from all the 24 seedlings of a plot and mixed for RNA extraction.
To investigate the expression pattern of the candidate genes in different organs at different growth stages, 3-week-old seedling of Nipponbare precultured hydroponically were transplanted to the paddy field in the Experimenal Farm in Shenzhen of the Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences. Tissue samples taken includes roots, basal stem, leaf sheath and leaf blade at the vegetative stage and roots, basal stem, lower leaf sheath, lower leaf blade, flag leaf sheath, flag leaf blade, node I–II, inter node II, peduncle, rachis, spikelet, husk and seed at the reproductive stages. A single plant was regarded as a biological replicate and three biological replicates were used.
The total RNA was extracted by Trizol (Vazyme Biotech Co. Ltd, China). Then the total RNA was reverse- transcripted with the HiScript Q RT SuperMix for qPCR kit (Vazyme Biotech Co). The AceQ Universal SYBR qPCR Master Mix kit (Vazyme Biotech Co) was used for quantitative analysis (Chen et al., 2020 (link)). The primers for qRT-PCR were shown in Supplementary Table S2.

DELLA protein sequences of pumpkin (7), cucumber (4), melon (4), watermelon (4), Arabidopsis (5), soybean (7), Brassica napus (13), rice (1), tomato (2) and maize (3) were retrieved from cucurbit genomics database, TAIR and Ensembl database (http://plants.ensembl.org/index.html), respectively. Based on multiple sequence alignment, phylogenetic tree was created using the neighbor-joining (NJ) method with MEGA 7.0. The conserved domains were analyzed by the NCBI CDD, and the conserved motifs were predicted by MEME (https://meme-suite.org/meme/). Furthermore, the distribution maps of conserved domains and conserved motifs were visualized using TBtools v 1.0986961. Promoter sequences (2 kp before the start codon) of CmoDELLA genes were analyzed through online PlantCare (https://bioinformatics.psb.ugent.be/webtools/plantcare/html/) to obtain cis-regulatory elements. Interaction networks between CmoDELLA proteins and other proteins were conducted through online STRING (https://string-db.org/), using Arabidopsis as reference species.