- Research article
- Open Access
Genome sequencing of rice subspecies and genetic analysis of recombinant lines reveals regional yield- and quality-associated loci
- Xiukun Li†1,
- Lian Wu†1,
- Jiahong Wang2,
- Jian Sun1,
- Xiuhong Xia1,
- Xin Geng1,
- Xuhong Wang1,
- Zhengjin Xu1 and
- Quan Xu1Email authorView ORCID ID profile
© Xu et al. 2018
- Received: 4 July 2018
- Accepted: 6 September 2018
- Published: 18 September 2018
Two of the most widely cultivated rice strains are Oryza sativa indica and O. sativa japonica, and understanding the genetic basis of their agronomic traits is of importance for crop production. These two species are highly distinct in terms of geographical distribution and morphological traits. However, the relationship among genetic background, ecological conditions, and agronomic traits is unclear.
In this study, we performed the de novo assembly of a high-quality genome of SN265, a cultivar that is extensively cultivated as a backbone japonica parent in northern China, using single-molecule sequencing. Recombinant inbred lines (RILs) derived from a cross between SN265 and R99 (indica) were re-sequenced and cultivated in three distinct ecological conditions. We identify 79 QTLs related to 15 agronomic traits. We found that several genes underwent functional alterations when the ecological conditions were changed, and some alleles exhibited contracted responses to different genetic backgrounds. We validated the involvement of one candidate gene, DEP1, in determining panicle length, using CRISPR/Cas9 gene editing.
This study provides information on the suitable environmental conditions, and genetic background, for functional genes in rice breeding. Moreover, the public availability of the reference genome of northern japonica SN265 provides a valuable resource for plant biologists and the genetic improvement of crops.
- Oryza sativa
- De novo assembly
- QTL dissection
- Yield and quality
Rice is one of the most important staple crops in the world and provides more than 20% of the calorie intake for more than half of the world’s population. Given continuing population growth and increasing competition for arable land between food and energy crops, food security is becoming an ever more serious global problem. Two major types of Oryza sativa japonica and O. sativa indica subspecies have historically been recognized. Varied degrees of geographical distribution and morphology characters exist between the two subspecies. Elucidation of the relationship among the functional genomic of indica and japonica, ecological conditions, and agronomic traits may significantly contribute to the improvement of rice production. China established a nationwide mega project entitled “Breeding and cultivation system of super rice in China” in 1996. After nearly a decade of cultivation, super rice accounts for more than 60% of the total area under rice cultivation and has contributed an estimated two billion dollars to the Chinese national economy [1, 2]. Shennong 265 (SN265), the first released commercial super rice variety, showed not only erect panicles but also strong root activity and high yield in a range of growing environments. SN265 leads the breeding direction as the backbone parent in northern China. Rice genetics and functional genomics have been rapidly advancing, particularly over the last decade, since the first determination of the Nipponbare genome sequence . To improve understanding of the genetic mechanism of hybrid super rice, the genomes of two elite indica rice varieties, namely, Zhenshan 97 and Minghui 63, have been assembled . Recently, a near-complete indica rice genome of R498 was published, which enriched the implications for plant biology and crop genetic improvement in indica . As the japonica varieties in northern China have varying degrees of indica pedigree introgression , the establishment of a reference for japonica in northern China is imperative. Thus, the de novo assembly of the SN265 genome will serve as a reference for the discovery of genes and structural variations that contribute to the increase in rice production in super rice varieties in northern China.
Here, we constructed a high-density linkage map by re-sequencing the recombinant inbred lines (RILs) derived from a cross between the japonica variety SN265 and indica variety R99. We de novo assembled the two parental genomes of SN265 and R99 based on single-molecule real-time sequencing (SMRT) and high-throughput next-generation sequencing (NGS). The RILs were planted in three areas with distinct ecological conditions, and 15 important agronomic traits were investigated. The re-sequencing and assembly of the parental genomes facilitated QTL analysis and candidate gene identification. The influence of genetic background and ecological condition to gene function was investigated in this study.
Population sequencing and linkage map construction
In order to construct the linage map, the RILs derived from the cross between SN265 and R99, along with the parents, were sequenced on an Illumina HiSeq2500 platform. Through the high-throughput sequencing, we obtained a total of 434.37 Gb of clean data, with approximately 6.25-fold depth for each RILs. For parent lines, 30.0-fold depth and 32.0-fold depth data were generated for R99 and SN265, respectively. We aligned these data to the Os-Nipponbare-Reference-IRGSP-1.0 (http://rapdb.dna.affrc.go.jp/download/irgsp1.html) using SOAP2 [6, 7]. Totally 1,708,775 single nucleotide polymorphisms (SNPs) between SN265 and R99 were identified using SOAPsnp . To avoid ambiguity in the analysis, we removed the SNPs that has low genotyping scores or located in highly repetitive regions. As the low-coverage sequencing caused the missing genotype for RILs, the k-nearest neighbor algorithm was used to impute the missing genotypes of each RILs . Subsequently, a recombinant bin map was constructed by 1,456,445 high-quality SNPs. The map contained 3569 recombinant blocks, with the average length of 58.17 kb (Additional file 1: Figure S1 and Additional file 2: Figure S2).
Assembly of the parental genome
Comparison of basic sequence statistic of SN265 and Os-Nipponbare-Reference-IRGSP-1.0
Indica pedigree percentage affects yield and quality traits
QTL detection and analysis using the RIL population
Fine-mapping of QTLs for QTL clustering
The influence of ecological conditions and genetic background to gene function
As the RILs were derived from the cross between indica and japonica and we cultivated the RILs into three areas with distinct ecological conditions, we were able to elucidate the influence of ecological conditions and genetic background to gene function. First, we found that Gn1a largely contributed to grain number per panicle in SY and JS, but not in SZ. However, QTL analysis only detected DEP1 in SZ as a grain number per panicle QTL, but not in SY and JS. We further compared the plant carrying DEP1/dep1 and Gn1a/gn1a in the three areas, which showed that with increasing latitude, the effect of dep1 on increasing grain number became weaker, whereas the effect of gn1a on increasing grain number became stronger (Fig. 5). Similarly, QTL analysis can detect DTH8, SDG708, and PHYB in JS, but only PHYB in SZ, and only SDG708 and DTH8 in SY. We selected different gene combinations of DTH8, SDG708, and PHYB in the RILs and compared the heading date of these lines in the three areas. The results confirmed that DTH8 and SDG708 barely affect heading date in SZ, and the PHYB imparted weaker effects on SY (Additional file 11: Figure S6). In summary, the function of Gn1a, SDG708, SD1, and GW5 was enlarged with increasing latitude. On the other hand, a disruption in the function of PHYB and DEP1 was observed with increasing latitude. Interestingly, DTH8 showed the strongest function in middle latitudes and became weaker with increasing or decreasing latitude.
Then, we analyzed the influence of indica pedigree percentage to gene function. The RILs were divided into three groups based on the indica pedigree percentage: japonica group (indica pedigree percentage 0~0.4), inter-group (indica pedigree percentage 0.4~0.6), and indica group (indica pedigree percentage 0.6~1). We found that gene function also differs with genetic background. Interestingly, the japonica-type allele of SD1 induces a decrease in plant height with increasing indica pedigree percentage, whereas the indica-type sd1 allele increases plant height with higher indica pedigree percentage (Additional file 12: Figure S7). Moreover, the japonica-type allele of PHYB delays the heading date with increasing indica pedigree in SZ, but accelerates heading date in JS and SY. Additionally, several gene functions in the inter-type group showed worst agronomic traits among three groups. For example, the inter-group exhibited the lowest grain number per panicle under a DEP1 and Gn1a genetic backgrounds (Additional file 13: Table S6). This may explain why the rare commercial varieties have the inter-type genetic background.
Rice is a short-day plant model that flowers more rapidly in short-day conditions exhibit delayed flowering under long-day conditions, thereby indicating the existence of critical day length responses . In the wild-type plants, although Hd3a mRNA is highly expressed at day lengths ≤ 13 h, its expression markedly decreased to about one-tenth of the expression, at a day length of 13.5 h and became undetectable at day lengths of ≥ 14 h . Our previous study has monitored day length and the temperature during the whole growth season in all three areas in 2016 . We observed RIL growth under long-day conditions for the whole growing season in SY and JS, and the day length even exceeded 15 h in SY at July. However, the entire growth period of the RILs involved short-day conditions in SZ (Additional file 14: Figure S8). Thus, we assumed that the photoperiod-sensitive genes play supporting roles in SZ as it maintains optimal day length possibly for the entire growth season, and other factors, such as temperature, play a lead role in establishing heading date. Then, we analyzed the relationship between temperature and heading date in SZ using the method described in our previous study . We found that temperature has a strong correlation with heading date, and this correlation exhibited a positive/negative change by a 10-day rhythm (Additional file 14: Figure S8). Ambient temperature regulates various aspects of plant growth and development, but the actual indicators in rice remain elusive. In Arabidopsis, in addition to its photoreceptor function, PHYB acts as a temperature sensor . As PHYB was the only detected QTL corresponding to heading date in SZ, we assumed that PHYB may also be involved in the temperature responses in rice.
Grain weight is a major determinant of crop grain yield and is controlled by naturally occurring quantitative traits loci. Grain shape largely differed between indica and japonica. GW5 was detected in most japonica cultivars during rice domestication, and a 1212-bp deletion was associated with the increased grain width in japonica cultivars . The present study confirmed that the 1212-bp deletion 5.7-kb upstream of GW5 was the major factor affecting grain width, which explains 38.26% of the observed variation. The SN265-type truncated dep1 allele is widely distributed among japonica varieties in Northeast China and the Yangtze River area . Moreover, the function of the indica-type gn1a is enhanced with increasing latitude. These results suggest that the introduction of the japonica elite allele into the indica genetic background or cultivated zone may improve the agronomic traits of indica and vice versa. The erect panicle architecture caused by the dep1 allele significantly increases grain yield; however, the quality traits of these varieties are only considered to be mediocre. As grain width is always significantly positively correlated to chalkiness level, the combination of the indica-type gw5 and dep1 alleles can simultaneously improve grain yield and morphological traits. Moreover, genotypic analysis of QTLs demonstrated that the haplotype status in RIL lines is responsible for the corresponding traits, whereas the combination of favorable QTLs contributes to relatively high yield per plant. The combination of the SN265-type allele of DEP1, GW5, and indica-type of Gn1a is associated with the highest grain yield per plant in all three of the areas (Fig. 5).
With the application of high-throughput sequencing technology, numerous rice accessions have been re-sequenced and phenotyped in the past few years, allowing the exploration of genomic diversity, particularly in terms of identifying loci that are responsive to domestication, as well as in elucidating the molecular mechanism underlying important agronomic traits [9, 26–29]. The de novo assembly of the rice genome provides us with more information to comprehensively capture the genomic diversity in this species . In this study, we performed de novo assembly of a 364.45-Mb SN265 genome as a reference for super rice in northern China using an RIL population, real-time sequencing (SMRT), and high-throughput NGS.
Our study identified 79 QTLs that are related to the 15 agronomic traits in three areas with distinct ecological condition and found that several genes underwent functional alterations when the ecological conditions and genetic background were altered. We de novo assembly a super rice variety SN265, and the availability of high-quality reference genomes for the japonica subspecies not only facilitates the identification of genes corresponding to agronomic traits but also provides a range of implications for plant biology and crop genetic improvement.
Plant materials and quality measurements
We conducted a cross between “Shennong265” (Oryza sativa japonica) and “R99” (O. sativa indica) and used the single-seed descendant method to generate RILs with at least 10 generation inbred. A total of 151 RILs were constructed and were used in this study. Field experiments were conducted in three typical rice cultivated areas: the Agricultural Genomics Institute at Shenzhen (SZ; N22°, E114°), the sub-base of China National Hybrid Rice R&D Center in Jiangsu Province (JS; N32°, E120°), and the Rice Research Institute of Shenyang Agricultural University (SY; N41°, E123°) for two growing seasons during 2015–2016. The cultivation method and field management were described in our previous report . We harvested the field examination plants at 45 days after heading for each line in each of the three areas. A total of 20 plants from the middle rows were harvested for each line. The quality measurement was conducted as described in our previous study . We only used the 2016 data in the present study, as the 2 years of data showed similar trends and are highly correlated. All samples were analyzed with two biological replicates.
DNA extraction and re-sequencing
We sampled the young leaves for each lines 2 weeks after transplanting. To obtain the high-quality DNA, the cetyltrimethylammonium bromide (CTAB) method was used to extract genomic DNA. The sequencing libraries were constructed on the Illumina HiSeq2500 following the manufacturer’s instructions. We aligned the sequencing data to the japonica reference genome (Nipponbare, http://rapdb.dna.affrc.go.jp/download/irgsp1.html/) using SOAP2 . To construct the genetic map, we combined the co-segregating markers (SNP and/or InDel) into bins using HighMap software . The constructed map contained 3569 bins, and there were average 247 bins on each chromosome. The map contained 1965.33 cM genetic distance. There were 12 linkage groups in the linkage map, which correspond to the 12 rice chromosomes. The full collinearity between the genetic map and the rice genome was observed, as the minimum value of spearman coefficient for chromosome was 0.9725 (Chr. 6).
Single-molecule real-time sequencing (SMRT) and high-throughput NGS
The genomic DNA of each line was extracted from fresh leaves using DNeasy Plant Mini kits (Qiagen, Germany) according to manufacturer’s instructions. DNA libraries for SMRT sequencing were performed as described elsewhere . The single-molecule sequencing (SMS) data are assembled following a hierarchical approach: (1) select a subset of longer reads as seed data and correct through canu/falcon , (2) use the error-corrected reads for a draft assembly by different assemblers, and (3) polish the draft assembly using Quiver/Arrow and Pilon. In the correction approach, Canu first selects longer seed reads with the settings “genomeSize = 1000000000” and “corOutCoverage = 80,” then detects raw reads overlapping through high-sensitive overlapper MHAP (mhap-2.1.2, option “corMhapSensitivity = normal”), then finally performs an error correction through falcon_sense method (option “correctedErrorRate = 0.025”). In the next approach, with the default parameters, error-corrected reads are trimmed unsupported bases and hairpin adaptors to get the longest supported range. In the last approach, Canu generates the draft assembly by the longest 80 coverage trimmed reads. The draft assembly is polished to obtain the final assembly. Two rounds of polishing are conducted. The first round polishing adopts arrow algorithm by SMS data with the 40 threads, and the second polishing adopts pilon algorithm (v1.22, available at https://github.com/broadinstitute/pilon) using illumina data with the parameters “--mindepth 10 --changes --threads 4 --fix bases.”
The RNAs of SN265 and R99 were isolated from the fresh leaves using a TaKaRa MiniBEST Universal RNA Extraction Kit according to manufacturer’s protocol. The sequencing was performed using the Illumina HiSeq 2500 platform according to manufacturer’s instructions. We obtained 8 Gb of RNA-seq data. The MITE-Hunter, LTR_FINDER v1.05, RepeatScout v1.0.5, and PILER-DF v2.4 were used to construct a primary repeat sequence database using structural prediction and ab initio prediction theory [33–36]. We classified the primary database based on PASTE Classifier and then combined with the Repbase database to form the final repeat sequence database for the final prediction through Repeat Masker v4.0.6 [37–39]. In protein-coding gene prediction, the repeat elements were masked and excluded from the genome assembly. Gene annotation was performed by three prediction steps: (1) ab initio prediction using Augustus v2.4, Genscan, GlimmerHMM v3.0.4, GeneID v1.4, and SNAP (version 2006-07-28); (2) homologous species prediction based on Oryza sativa, Arabidopsis thaliana, Setaria italica, Sorghum bicolor, and Zea mays using GeMoMa v1.3.1; and (3) unigene prediction based on full-length transcriptome data assembly with no reference genome was conducted through PASA v2.0.2 [40–45]. The three predictions were integrated through EVM v1.1.1, and final modifications were performed by PASA v2.0.2 . Non-coding RNAs (microRNAs, rRNAs, and tRNAs) were identified with different strategies according to their unique structural features. The miRBase, Rfam, and tRNAscan-SE v1.3.1 databases were used to predict microRNA, rRNA, and tRNA, respectively [47, 48]. We predicted pseudogenes through scanning for homologous genes and excluding genuine genes by GenBlastA v1.0.4 . We selected the candidate genes with premature stop codons and frameshift mutations as the final pseudogene predictions by GeneWise v2.4.1 . In order to annotate genes’ function, we blasted the predicted genes to the NR, KOG, GO, TrEMBL, and KEGG databases by BLAST v2.2.31 (-evalue 1e-5) [50–55]. In addition, the motifs were annotated according to the sequence alignments with the HAMAP, Pfam, PRINTS, ProDom, SMART, TIGRFAMs, SUPERFAMILY, PIRSF, CATH-Gene3D, and PANTHER databases by InterProScan software .
Vector construction and plant transformation
To conduct the CRISPR/Cas9 gene editing, we performed the vector construction as described by Li et al. . The targeting sequence including PAM sequence (23 bp) was selected in the 5th exon of DEP1 gene. We confirmed the specificity of targeting sequence by BLAST searching against the rice genome (http://blast.ncbi.nlm.nih.gov/Blast.cgi) . We performed rice transformation as described elsewhere . We extracted the genomic DNA from transformants, and the genomic DNA were sequenced for mutant identification. The PCR products (200–500 bp) were sequenced and identified using the Degenerate Sequence Decoding method .
The National Natural Science Foundation of China (31430062 and 31501284) supported this study.
Availability of data and materials
The datasets supporting the conclusions of this article are included within the article and its additional files. The sequences reported in this paper have been deposited in the National Center for Biotechnology Information (NCBI). This Whole Genome Shotgun data has been deposited at DDBJ/ENA/GenBank under the accessions QWGC00000000 (SN265) and QWGD00000000 (R99). PRJNA486237 and PRJNA486425 (the Oryza sativa raw sequence reads), and SRP158741 contain the raw sequence reads of RILs. The seeds of recombinant inbred line populations and the parents are available from the corresponding author on reasonable request.
ZX and QX designed this study and contributed to the original concept of the project. XL and LW performed most of the experiments. JW and JS participated the genome assemble. XG and XX participated in the assessment of yield components in three areas. XW participated in quality measurement. QX wrote the paper. All authors read and approved the final manuscript.
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The authors declare that they have no competing interests.
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- Qian Q, Guo L, Smith SM, Li J. Breeding high-yield superior quality hybrid super rice by rational design. Natl Sci Rev. 2016;3(3):283–94.View ArticleGoogle Scholar
- Sun J, Liu D, Wang J-Y, Ma D-R, Tang L, Gao H, Xu Z-J, Chen W-F. The contribution of intersubspecific hybridization to the breeding of super-high-yielding japonica rice in northeast China. Theor Appl Genet. 2012;125(6):1149–57.View ArticleGoogle Scholar
- Fujisawa M, Baba T, Nagamura Y, Nagasaki H, Waki K, Vuong H, Matsumoto T, Wu JZ, Kanamori H, Katayose Y. The map-based sequence of the rice genome. Nature. 2005;436(7052):793–800.View ArticleGoogle Scholar
- Zhang J, Chen LL, Xing F, Kudrna DA, Yao W, Copetti D, Mu T, Li W, Song JM, Xie W. Extensive sequence divergence between the reference genomes of two elite indica rice varieties Zhenshan 97 and Minghui 63. Proc Natl Acad Sci U S A. 2016;113(35):E5163.View ArticleGoogle Scholar
- Du H, Ying Y, Ma Y, Qiang G, Cao Y, Zhuo C, Ma B, Ming Q, Yan L, Zhao X. Sequencing and de novo assembly of a near complete indica rice genome. Nat Commun. 2017;8:15324.View ArticleGoogle Scholar
- Li R, Yu C, Li Y, Lam TW, Yiu SM, Kristiansen K, Wang J. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics. 2009;25(15):1966–7.View ArticleGoogle Scholar
- Kawahara Y, Bastide MDL, Hamilton JP, Kanamori H, Mccombie WR, Shu O, Schwartz DC, Tanaka T, Wu J, Zhou S. Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Rice. 2013;6(1):4.View ArticleGoogle Scholar
- Li R, Li Y, Fang X, Yang H, Wang J, Kristiansen K, Wang J. SNP detection for massively parallel whole-genome resequencing. Genome Res. 2009;19(6):1124.View ArticleGoogle Scholar
- Huang X, Wei X, Sang T, Zhao Q, Feng Q, Zhao Y, Li C, Zhu C, Lu T, Zhang Z. Genome-wide association studies of 14 agronomic traits in rice landraces. Nat Genet. 2010;42(11):961–7.View ArticleGoogle Scholar
- Pendleton M, Sebra R, Pang AWC, Ummat A, Franzen O, Rausch T, Stütz AM, Stedman W, Anantharaman T, Hastie A. Assembly and diploid architecture of an individual human genome via single-molecule technologies. Nat Methods. 2015;12(8):780–6.View ArticleGoogle Scholar
- Huang X, Qian Q, Liu Z, Sun H, He S, Luo D, Xia G, Chu C, Li J, Fu X. Natural variation at the DEP1 locus enhances grain yield in rice. Nat Genet. 2009;41(4):494–7.View ArticleGoogle Scholar
- Ashikari M, Sakakibara H, Lin S, Yamamoto T, Takashi T, Nishimura A, Angeles ER, Qian Q, Kitano H, Matsuoka M. Cytokinin oxidase regulates rice grain production. Science. 2005;309(5735):741–5.View ArticleGoogle Scholar
- Sasaki A, Ashikari M, Ueguchi-Tanaka M, Itoh H, Nishimura A, Swapan D, Ishiyama K, Saito T, Kobayashi M, Khush GS. A mutant gibberellin–synthesis gene in rice. Nature. 2002;416(6882):701–2.View ArticleGoogle Scholar
- Weng J, Gu S, Wan X, Gao H, Guo T, Su N, Lei C, Zhang X, Cheng Z, Guo X. Isolation and initial characterization of GW5, a major QTL associated with rice grain width and weight. Cell Res. 2008;18(12):1199–209.View ArticleGoogle Scholar
- Shomura A, Izawa T, Ebana K, Ebitani T, Kanegae H, Konishi S, Yano M. Deletion in a gene associated with grain size increased yields during rice domestication. Nat Genet. 2008;40(8):1023–8.View ArticleGoogle Scholar
- Fang Y, Xie K, Xiong L. Conserved miR164-targeted NAC genes negatively regulate drought resistance in rice. J Exp Bot. 2014;65(8):2119–35.View ArticleGoogle Scholar
- Nuruzzaman M, Manimekalai R, Sharoni AM, Satoh K, Kondoh H, Ooka H, Kikuchi S. Genome-wide analysis of NAC transcription factor family in rice. Gene. 2010;465(1):30–44.View ArticleGoogle Scholar
- Wei X, Xu J, Guo H, Jiang L, Chen S, Yu C, Zhou Z, Hu P, Zhai H, Wan J. DTH8 suppresses flowering in rice, influencing plant height and yield potential simultaneously. Plant Physiol. 2010;153(4):1747–58.View ArticleGoogle Scholar
- Liu B, Wei G, Shi J, Jin J, Shen T, Ni T, Shen WH, Yu Y, Dong A. SET DOMAIN GROUP 708, a histone H3 lysine 36-specific methyltransferase, controls flowering time in rice (Oryza sativa). New Phytol. 2016;210(2):577–88.View ArticleGoogle Scholar
- Ishikawa R, Aoki M, Kurotani K, Yokoi S, Shinomura T, Takano M, Shimamoto K. Phytochrome B regulates heading date 1 (Hd1)-mediated expression of rice florigen Hd3a and critical day length in rice. Mol Gen Genomics. 2011;285(6):461–70.View ArticleGoogle Scholar
- Itoh H, Nonoue Y, Yano M, Izawa T. A pair of floral regulators sets critical day length for Hd3a florigen expression in rice. Nat Genet. 2010;42(7):635–8.View ArticleGoogle Scholar
- Li X, Wu L, Geng X, Xia X, Wang X, Xu Z, Xu Q. Deciphering the environmental impacts on rice quality for different rice cultivated areas. Rice. 2018;11(1):7.View ArticleGoogle Scholar
- Jung JH, Domijan M, Klose C, Biswas S, Ezer D, Gao M, Khattak AK, Box MS, Charoensawan V, Cortijo S. Phytochromes function as thermosensors in Arabidopsis. Science. 2016;354(6314):886–9.View ArticleGoogle Scholar
- Ashikari M, Wu J, Yano M, Sasaki T, Yoshimura A. Rice gibberellin-insensitive dwarf mutant gene Dwarf 1 encodes the alpha-subunit of GTP-binding protein. Proc Natl Acad Sci U S A. 1999;96(18):10284–9.View ArticleGoogle Scholar
- Xu H, Zhao M, Zhang Q, Xu Z, Xu Q. The DENSE AND ERECT PANICLE 1 (DEP1) gene offering the potential in the breeding of high-yielding rice. Breed Sci. 2016;66(5):659–67.View ArticleGoogle Scholar
- Wang W, Mauleon R, Hu Z, Chebotarov D, Tai S, Wu Z, Li M, Zheng T, Fuentes RR, Zhang F. Genomic variation in 3,010 diverse accessions of Asian cultivated rice. Nature. 2018;557(7703):43.View ArticleGoogle Scholar
- Huang X, Yang S, Gong J, Zhao Q, Feng Q, Zhan Q, Zhao Y, Li W, Cheng B, Xia J. Genomic architecture of heterosis for yield traits in rice. Nature. 2016;537(7622):629–33.View ArticleGoogle Scholar
- Huang X, Kurata N, Wei X, Wang Z-X, Wang A, Zhao Q, Zhao Y, Liu K, Lu H, Li W. A map of rice genome variation reveals the origin of cultivated rice. Nature. 2012;490(7421):497–501.View ArticleGoogle Scholar
- Huang X, Zhao Y, Wei X, Li C, Wang A, Zhao Q, Li W, Guo Y, Deng L, Zhu C. Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat Genet. 2012;44(1):32.View ArticleGoogle Scholar
- Zhao Q, Feng Q, Lu H, Li Y, Wang A, Tian Q, Zhan Q, Lu Y, Zhang L, Huang T. Pan-genome analysis highlights the extent of genomic variation in cultivated and wild rice. Nat Genet. 2018;50(2):278.View ArticleGoogle Scholar
- Liu D, Ma C, Hong W, Huang L, Liu M, Liu H, Zeng H, Deng D, Xin H, Song J. Construction and analysis of high-density linkage map using high-throughput sequencing data. PLoS One. 2014;9(6):e98855.View ArticleGoogle Scholar
- Koren S, Walenz BP, Berlin K, Miller JR, Bergman NH, Phillippy AM. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 2017;27(5):722.View ArticleGoogle Scholar
- Price AL, Jones NC, Pevzner PA. De novo identification of repeat families in large genomes. Bioinformatics. 2005;21(suppl_1):i351.View ArticleGoogle Scholar
- Han Y, Wessler SR. MITE-Hunter: a program for discovering miniature inverted-repeat transposable elements from genomic sequences. Nucleic Acids Res. 2010;38(22):e199.View ArticleGoogle Scholar
- Xu Z, Wang H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 2007;35(Web Server issue):W265–8.View ArticleGoogle Scholar
- Edgar RC, Myers EW. PILER: identification and classification of genomic repeats. Bioinformatics. 2005;21(Suppl 1):i152.View ArticleGoogle Scholar
- Wicker T, Sabot F, Hua-Van A, Bennetzen JL, Capy P, Chalhoub B, Flavell A, Leroy P, Morgante M, Panaud O. A unified classification system for eukaryotic transposable elements. Nat Rev Genet. 2009;10(4):276.View ArticleGoogle Scholar
- Jurka J, Kapitonov VV, Pavlicek A, Klonowski P, Kohany O, Walichiewicz J. Repbase Update, a database of eukaryotic repetitive elements. Cytogenet Genome Res. 2005;110(1–4):462–7.View ArticleGoogle Scholar
- Tarailograovac M, Chen N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr Protoc Bioinformatics. 2004; Chapter 4(Unit 4):Unit 4.10.Google Scholar
- Stanke M, Waack S. Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics. 2003;19(suppl_2):215–25.Google Scholar
- Majoros WH, Pertea M, Salzberg SL. TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders. Bioinformatics. 2004;20(16):2878–9.View ArticleGoogle Scholar
- Blanco E, Parra G, Guigó R. Using geneid to identify genes. Current Protocols in Bioinformatics. 2007;18(1):Unit 4.3.Google Scholar
- Korf I. Gene finding in novel genomes. BMC Bioinformatics. 2004;5(1):59.View ArticleGoogle Scholar
- Jens K, Michael W, Erickson JL, Schattat MH, Jan G, Frank H. Using intron position conservation for homology-based gene prediction. Nucleic Acids Res. 2016;44(9):e89.View ArticleGoogle Scholar
- Campbell MA, Haas BJ, Hamilton JP, Mount SM, Buell CR. Comprehensive analysis of alternative splicing in rice and comparative analyses with Arabidopsis. BMC Genomics. 2006;7(1):327.View ArticleGoogle Scholar
- Haas BJ, Salzberg SL, Wei Z, Pertea M, Allen JE, Orvis J, White O, Buell CR, Wortman JR. Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biol. 2008;9(1):R7.View ArticleGoogle Scholar
- Griffiths-Jones S, Moxon S, Marshall M, Khanna A, Eddy SR, Bateman A. Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res. 2005;33:121–4.View ArticleGoogle Scholar
- Nawrocki EP, Eddy SR. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics. 2013;29(22):2933–5.View ArticleGoogle Scholar
- She R, Chu JS, Wang K, Pei J, Chen N. GenBlastA: enabling BLAST to identify homologous gene sequences. Genome Res. 2009;19(1):143–9.View ArticleGoogle Scholar
- Birney E, Clamp M, Durbin R. GeneWise and genomewise. Genome Res. 2004;14(5):988–95.View ArticleGoogle Scholar
- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215(3):403–10.View ArticleGoogle Scholar
- Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;27(1):29–34.View ArticleGoogle Scholar
- Tatusov RL, Natale DA, Garkavtsev IV, Tatusova TA, Shankavaram UT, Rao BS, Kiryutin B, Galperin MY, Fedorova ND, Koonin EV. The COG database: new developments in phylogenetic classification of proteins from complete genomes. Nucleic Acids Res. 2001;29(1):22–8.View ArticleGoogle Scholar
- Boeckmann B, Bairoch A, Apweiler R, Blatter MC, Estreicher A, Gasteiger E, Martin MJ, Michoud K, O'Donovan C, Phan I. The Swiss-Prot knowledgebase and its supplement TREMBL in 2003. Nucleic Acids Res. 2003;31(1):365–70.View ArticleGoogle Scholar
- Marchlerbauer A, Lu S, Anderson JB, Chitsaz F, Derbyshire MK, Deweesescott C, Fong JH, Geer LY, Geer RC, Gonzales NR. CDD: a conserved domain database for the functional annotation of proteins. Nucleic Acids Res. 2011;39:225–9.View ArticleGoogle Scholar
- Zdobnov EM, Apweiler R. InterProScan—an integration platform for the signature-recognition methods in InterPro. Bioinformatics. 2001;17(9):847–8.View ArticleGoogle Scholar
- Li W, Zhu Z, Chern M, Yin J, Yang C, Ran L, Cheng M, He M, Wang K, Wang J. A natural allele of a transcription factor in rice confers broad-spectrum blast resistance. Cell. 2017;170(1):114–26.View ArticleGoogle Scholar
- Hsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, Agarwala V, Li Y, Fine EJ, Wu X, Shalem O. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat Biotechnol. 2013;31(9):827–32.View ArticleGoogle Scholar
- Nishimura A, Aichi I, Matsuoka M. A protocol for agrobacterium-mediated transformation in rice. Nat Protoc. 2006;1(6):2796.View ArticleGoogle Scholar
- Ma X, Zhang Q, Zhu Q, Liu W, Chen Y, Qiu R, Wang B, Yang Z, Li H, Lin Y. A robust CRISPR/Cas9 system for convenient, high-efficiency multiplex genome editing in monocot and dicot plants. Mol Plant. 2015;8(8):1274–84.View ArticleGoogle Scholar