Research progress on bioinformatics databases and tools related to rice genetics and breeding
-
摘要:
水稻Oryza sativa L.是主要的粮食作物，也是单子叶植物研究的模式植物。面对日益严峻的环境和人口压力，培育高产、优质、环境适性强的水稻品种是解决当前粮食安全问题的有效途径。随着多组学技术的快速发展，积累了海量的水稻遗传育种相关的数据。生物信息数据库和在线分析工具是存储这些数据的载体，用以整合、可视化和共享数据，并为数据的深入挖掘和利用提供工具，从而为育种决策提供数据支撑。本综述系统梳理了近20年来开发的水稻生物信息数据库和在线分析工具，并基于内置数据集和功能对它们进行了分类和总结。最后，讨论了现有的水稻生物信息数据库和在线分析工具的问题与不足，并对它们在大数据和人工智能时代的发展方向进行了展望。
Abstract:Rice (Oryza sativa L.) is both a major staple food and a model crop plant for monocot studies. Facing the increasingly severe environmental and population problems, breeding varieties with high yield, high quality, and wide adaptability is the efficient way to solve the food security problems. With the rapid development of multi-omics technology, large volumes of data related to rice genetics and breeding have been accumulated. Bioinformatics databases and online analysis tools are developed to store, integrate, visualize, and share these datasets. In addition, some databases possess built-in tools for further mining and using datasets to provide data support for decision-making in breeding. In this review, we systematically sort out rice bioinformatics databases and online analysis tools developed in the past two decades. Subsequently, we classified and summarized these resources based on their built-in datasets and features. Finally, the problems and deficiencies of the existing rice bioinformatics resources were discussed, and the development direction of bioinformatics resources in the era of big data and artificial intelligence was prospected.
-
Keywords:
- Rice /
- Genetics and breeding /
- Bioinformatics database /
- Online analysis tool
-
-
表 1 已发表的水稻基因组数据库
Table 1 The published rice genomic databases
数据库
Database描述
Description参考文献
ReferenceNCBI 综合数据库、稻属16个物种参考基因组、基因组重测序数据，https://www.ncbi.nlm.nih.gov/ [16] Ensembl 综合数据库、稻属10个物种参考基因组、基因组注释，http://plants.ensembl.org/ [17] Phytozome 综合数据库、‘日本晴’和‘Kitaake’参考基因组、基因组注释，https://phytozome-next.jgi.doe.gov/ [18] RAP-DB ‘日本晴’、IRGSP-1.0参考基因组、基因组注释，http://rapdb.dna.affrc.go.jp/ [19] MSU-RGAP ‘日本晴’、MSU7.0参考基因组、基因组注释，http://rice.uga.edu/ [20] RIGW ‘珍汕97’和‘明恢63’参考基因组、多组学数据、互作数据，http://rice.hzau.edu.cn/rice_rs3/ [21] IC4R 参考基因组、基因组注释、基因表达谱，http://ic4r.org/ [22] Rice Genome Hub 稻属10个物种的参考基因组(32个基因组信息)，https://rice-genome-hub.southgreen.fr/ [23] RPAN 3KRG线性泛基因组、泛基因组浏览器，http://cgm.sjtu.edu.cn/3kricedb/ [10] RicePanGenome 线性泛基因组、基因组变异、67个参考基因组，http://db.ncgr.ac.cn/RicePanGenome/ [11] RiceRc 图形泛基因组、33个参考基因组，http://ricerc.sicau.edu.cn/ [12] RiceSuperPIRdb 图形泛基因组、251个参考基因组，http://www.ricesuperpir.com/ [13] RGI 基于同源基因簇的水稻泛基因组、16个水稻参考基因组，https://riceome.hzau.edu.cn [14] OryzaGenome 稻属参考基因组，208个种质基因组信息，涉及19个野生稻和2个栽培稻物种，http://viewer.shigen.info/oryzagenome2detail/ [24] RiceRelativesGD 水稻17个近缘物种基因组和单倍型信息，http://ibi.zju.edu.cn/ricerelativesgd/ [25] funRiceGenes 基因功能数据库、IRGSP-1.0和MSU7.0基因注释，http://funricegenes.ncpgr.cn/ [26] 
下载: 导出CSV
表 2 水稻转录和转录后调控相关数据库
Table 2 The transcriptional and posttranscriptional regulation related databases in rice
数据库
Database描述
Description参考文献
ReferenceRiceXPro 微阵列数据集，自然条件下各个生长发育阶段、幼苗激素和胁迫处理的基因表达信息，https://ricexpro.dna.affrc.go.jp/ [27] CREP ‘珍汕97’和‘明恢63’的39个组织的基因表达信息，
http://crep.ncpgr.cn/crep-cgi/home.pl[28] RED 水稻9个组织，在不同生长阶段和处理的基因表达谱和基因共表达网络，http://expression.ic4r.org [29] TENOR 包括‘日本晴’在不同环境胁迫和激素处理条件下的140个mRNA-seq数据集，https://tenor.dna.affrc.go.jp/ [30] PPRD 11726个水稻mRNA-seq数据集，使用统一流程和最新的参考基因组进行分析和整合，http://ipf.sustech.edu.cn/pub/ricerna/ [31] eRice ‘日本晴’和‘9311’的mRNA-seq、DNA甲基化和组蛋白修饰数据库，http://www.elabcaas.cn/rice/index.html [32] RiceENCODE 综合调控RNA转录的DNA修饰、组蛋白修饰、染色质构象等表观调控元件，http://glab.hzau.edu.cn/RiceENCODE/ [33] RiceNCexp 提供基于mRNA-seq和sRNA-seq的基因和sRNA转录水平和共表达网络信息，https://cbi.njau.edu.cn/RiceNCexp/ [34] ARMOUR 7个水稻品种在不同发育时期、组织和胁迫下的miRNA和相应的靶标信息，https://www.icgeb.org/armour.html [35] RiceLncPedia 包含了水稻ncRNAs的表达谱、变异位点、ncRNA之间和ncRNA与编码基因的共表达网络信息，http://3dgenome.hzau.edu.cn/RiceLncPedia [36] RiceATM 挖掘miRNA与水稻农艺性状的关系，包括表型选择、样本分组、微阵列数据预处理、统计分析和靶基因预测等功能，http://syslab3.nchu.edu.tw/rice/ [37] CSRDB 整合了水稻和玉米的sRNA和它们的靶基因信息，http://sundarlab.ucdavis.edu/smrnas/ [38] miRbase 包含水稻已知和新的miRNA的序列和前体序列信息，是使用最广泛的miRNA综合数据库，http://mirbase.org/ [39] PceRBase 包含水稻等26个物种的ceRNA、miRNA和它们的靶基因信息，http://bis.zju.edu.cn/pcernadb/index.jsp [40] GreeNC 2.0 水稻lncRNA数据库，http://greenc.sequentiabiotech.com/wiki2/Main_Page [41] PLncDB 提供lncRNA的长度、类型、表达谱和表观遗传等信息，http://plncdb.tobaccodb.org/ [42] CANTATAdb 提供lncRNA长度、类型和表达谱信息，http://cantata.amu.edu.pl，http://yeti.amu.edu.pl/CANTATA/ [43] PmiREN 包含水稻miRNA及其前体序列、二级结构、表达模式、潜在靶点等信息，http://www.pmiren.com/ [44] PlantcircBase 提供水稻circRNA的分类、表达谱信息，http://ibi.zju.edu.cn/plantcircbase/ [45] PlaASDB 水稻和拟南芥在非生物和生物胁迫下的AS事件及AS与基因表达之间的联系，http://zzdlab.com/PlaASDB/ASDB/index.html [46] PlantAPAdb 水稻和拟南芥等6个物种基因组范围内的APA位点及注释信息，http://www.bmibig.cn/plantAPAdb [47]
下载: 导出CSV
表 3 已发表的水稻基因网络数据库
Table 3 The publised gene network databases in rice
数据库
Database描述
Description参考文献
ReferenceRiceFREND 基于不同组织不同生长发育阶段的微阵列数据构建的共表达网络，http://ricefrend.dna.affrc.go.jp/ [53] OryzaExpress 基于微阵列数据构建的共表达网络，http://plantomics.mind.meiji.ac.jp/OryzaExpress/ [54] RiceAntherNet 基于微阵列数据集构建的花粉和花药发育过程的共表达网络，https://www.cpib.ac.uk/anther/riceindex.html [55] NetREx 基于同源映射和转录组数据集构建的基因在逆境和激素处理下的共表达网络，https://bioinf.iiit.ac.in/netrex/index.html [56] PRIN 基于模式物种蛋白互作基因同源映射构建的水稻蛋白质互作网络，http://bis.zju.edu.cn/prin/ [57] RiceNetv2 基于蛋白互作、mRNA-seq等多种数据集，利用机器学习算法构建的基因网络，http://www.inetbio.org/ricenet [58] RicePPINet 基于机器学习算法构建的蛋白质互作网络，http://netbio.sjtu.edu.cn/riceppinet [59]
下载: 导出CSV
表 4 已发表的水稻种质资源信息数据库
Table 4 The published germplasm information resources in rice
数据库
Database描述
Description参考文献
ReferenceMBKBASE 以‘R498’和‘日本晴’为参考基因组，包含130578份种质的表型、群体结构和单倍型信息，https://www.mbkbase.org/rice/ [60] RiceVarMap 4726份水稻种质资源的基因组变异、基因型和表型数据，http://ricevarmap.ncpgr.cn/v2/ [61] SNP-Seek 4036份水稻的SNP图谱、表型和全基因组关联分析数据集，https://snp-seek.irri.org/ [62] SR4R 包含5152份种质资源的遗传变异、群体遗传学和进化基因组学数据集，http://sr4r.ic4r.org/ [63] HapRice 253份国际和日本来源的水稻种质的SNP和单倍型图谱，http://qtaro.abr.affrc.go.jp/index.html [64] RFGB 3KRG的表型、遗传变异和基因单倍型信息，http://www.rmbreeding.cn/ [65] RiceData 包含省级以上审定品种、大面积推广品种、外引品种以及地方性农家品种信息，http://www.ricedata.cn/variety/ [66] RiTE DB 包含266份水稻品种中鉴定到的54911个转座子信息数据集，https://www.genome.arizona.edu/cgi-bin/rite/index.cgi [67] RTRIP 3KRG的基因组转座子信息，提供转座子序列位点图谱、遗传多样性、基因组进化和分子标记等信息，http://ibi.zju.edu.cn/Rtrip/index.html [68]
下载: 导出CSV
表 5 常用的基因编辑系统
Table 5 Commonly used gene editing systems
编辑系统
Editing system编辑作用
Editing function用途
ApplicationCRISPR/Cas 靶点切割/突变 基因敲除、遗传改良 CRISPR/Cas/Donor DNA片段插入/替换 DNA片段突变、遗传改良 CRISPR/Cas-CBE 单碱基转换：C > T(G > A) 单碱基转换、遗传改良 CRISPR/Cas-ABE 单碱基转换：A > G(T > C) 单碱基转换、遗传改良 CRISPR/Cas-CGBE 单碱基转换：C > G(G > C) 单碱基转换、遗传改良 CRISPR/Prime editors 小片段插入、替换 单碱基的任意转换和小DNA片段的
替换或插入、遗传改良
下载: 导出CSV
表 6 水稻基因编辑生物信息工具与数据库
Table 6 The bioinformatics tools and databases for gene editing in rice
数据库
Database描述
Description参考文献
ReferenceCRISPR-GE “一站式”基因编辑设计工具包，涵盖靶点筛选、引物设计、编辑结果鉴定、脱靶分析等流程，http://skl.scau.edu.cn/ [73-74] MMEJ预测工具 评估MMEJ介导片段删除效率，http://www.rgenome.net/ [75] BEtarget 提供候选靶点在基因上的位置、GC含量、潜在脱靶值和脱靶位点、编辑窗口内的碱基变化及对应的氨基酸变化等信息，http://skl.scau.edu.cn/betarget/ [76] MMEJ-KO 基于微同源删除基因组片段的靶点设计工具，http://skl.scau.edu.cn/mmejko/ [77] GeneCat 快速提取基因组序列工具，http://skl.scau.edu.cn/genecat/ [78] Cas-Designers sgRNA设计线上工具，http://www.rgenome.net/cas-designer/ [79] Cas-Offinder sgRNA脱靶预测线上工具，http://www.rgenome.net/cas-offinder/ [80] CRISPR-P 多功能的sgRNA设计工具，随后升级为CRISPR-P 2.0，支持49个物种基因组的sgRNA设计，http://crispr.hzau.edu.cn/CRISPR2/ [81-82] CRISPRbase 碱基编辑综合知识平台，统计了多个物种的编辑效率、靶点偏好性和精准度，并进行功能注释，http://crisprbase.maolab.org/ [83] PGED 植物基因组编辑数据库，可供用户查阅或上存基因编辑靶位点、突变情况、表型信息等，http://plantcrispr.org [84] CAFRI-Rice 水稻冗余基因数据库，为基因编辑候选靶标的选择提供参考，http://pcafri-rice.khu.ac.kr [85] Ribo-uORF uORF综合数据库，收集了6个物种的高可信度uORF和TIS位点信息，为uORF编辑提供靶标，http://rnainformatics.org.cn/RiboUORF [86]
下载: 导出CSV
表 7 可用于水稻智能育种的机器学习软件和算法
Table 7 The machine learning software and algorithms for intelligent breeding in rice
软件
Software模型
Model描述
Description参考文献
ReferenceBGLR BL、BR、BayesA/B 基于基因−环境互作和多性状的基因组选择模型的构建，https://github.com/gdlc/BGLR-R [89] BRNN brnn 基于双向循环神经网络进行基因组选择和表型预测，https://cran.r-project.org/web/packages/brnn/ [90] BWGS BayesA/B、BL、BRR 基于R语言开发进行基因组选择和表型预测，https://cran.r-project.org/web/packages/BWGS/ [91] CropGBM LightGBM 基因型和表型数据预处理、群体结构分析、SNP 特征选择、表型预测和数据可视化，https://github.com/YuetongXU/CropGBM [92] DeepGS CNN 基于深度学习整合多组学数据进行基因组选择，https://github.com/cma2015/DeepGS/ [93] DNNGP DNN 基于深度神经网络整合多组学数据进行基因组选择，http://github.com/AIBreeding/DNNGP/ [94] HIBLUP BLUP 利用谱系、基因组和表型信息，评估个体的遗传价值，https://hiblup.github.io/ [95] KAML KAML、GBLUP 控制质量性状和数量性状的关键基因挖掘，https://github.com/YinLiLin/KAML [96] PopVar RRBLUP、 BayesA/B/C、
BL、BRR利用基因型和表型数据预测双亲后代的遗传方差和表型值，https://cran.r-project.org/web/packages/PopVar/ [97] rrBLUP RRBLUP 基因组分子标记遗传效应估计与表型预测，https://cran.r-project.org/web/packages/rrBLUP/ [98] sommer GBLUP、RRBLUP 加性效应、显性效应、上位性效应评估和遗传力的计算，https://cran.r-project.org/web/packages/sommer/ [99] STGS ANN、BLUP、LASSO、
RF、RR、SVM基于分子标记对单一性状进行基因组选择，https://cran.r-project.org/web/packages/STGS/ [100] GCTA BLUP SNP 遗传力评估和全基因组关联分析，http://cnsgenomics.com/software/gcta/ [101] JWAS Bayes 基因组选择和全基因组关联分析，http://reworkhow.github.io/JWAS.jl/latest/ [102] PIBULP BLUP 遗传参数评估与育种值估计，https://github.com/huiminkang/PIBLUP [103] solGS RRBLUP 基于组学数据进行复杂性状表型预测，http://cassavabase.org/solgs [104] IPAT GBLUP、RRBLUP、BayesB 全基因组关联分析与育种值估计的在线图形化界面工具，http://poissonfish.github.io/iPat/index.html [105]
下载: 导出CSV
-
[1] VAN ITTERSUM M K. Crop yields and global food security. Will yield increase continue to feed the world?[J]. European Review of Agricultural Economics, 2016, 43(1): 191-192. doi: 10.1093/erae/jbv034
[2] 景海春, 田志喜, 种康, 等. 分子设计育种的科技问题及其展望概论[J]. 中国科学(生命科学), 2021, 51(10): 1356-1365. [3] WALLACE J G, RODGERS-MELNICK E, BUCKLER E S. On the road to breeding 4.0: Unraveling the good, the bad, and the boring of crop quantitative genomics[J]. Annual Review of Genetics, 2018, 52: 421-444. doi: 10.1146/annurev-genet-120116-024846
[4] JIA L, XIE L J, LAO S T, et al. Rice bioinformatics in the genomic era: Status and perspectives[J]. The Crop Journal, 2021, 9(3): 609-621. doi: 10.1016/j.cj.2021.03.003
[5] 彭歆, 罗立新, 张力, 等. 重离子诱发的2个水稻突变体表型鉴定及遗传分析[J]. 华南农业大学学报, 2018, 39(1): 12-17. doi: 10.7671/j.issn.1001-411X.2018.01.003 [6] 程式华. 中国水稻育种百年发展与展望[J]. 中国稻米, 2021, 27(4): 1-6. [7] WREN J D, GEORGESCU C, GILES C B, et al. Use it or lose it: Citations predict the continued online availability of published bioinformatics resources[J]. Nucleic Acids Research, 2017, 45(7): 3627-3633. doi: 10.1093/nar/gkx182
[8] WING R A, AMMIRAJU J S S, LUO M, et al. The Oryza map alignment project: The golden path to unlocking the genetic potential of wild rice species[J]. Plant Molecular Biology, 2005, 59(1): 53-62. doi: 10.1007/s11103-004-6237-x
[9] YAO W, LI G, ZHAO H, et al. Exploring the rice dispensable genome using a metagenome-like assembly strategy[J]. Genome Biology, 2015, 16: 187. doi: 10.1186/s13059-015-0757-3
[10] SUN C, HU Z Q, ZHENG T Q, et al. RPAN: Rice pan-genome browser for ~3000 rice genomes[J]. Nucleic Acids Research, 2017, 45(2): 597-605. doi: 10.1093/nar/gkw958
[11] ZHAO Q, FENG Q, LU H, et al. Pan-genome analysis highlights the extent of genomic variation in cultivated and wild rice[J]. Nature Genetics, 2018, 50(2): 278-284. doi: 10.1038/s41588-018-0041-z
[12] QIN P, LU H W, DU H L, et al. Pan-genome analysis of 33 genetically diverse rice accessions reveals hidden genomic variations[J]. Cell, 2021, 184(13): 3542-3558. doi: 10.1016/j.cell.2021.04.046
[13] SHANG L, LI X, HE H, et al. A super pan-genomic landscape of rice[J]. Cell Research, 2022, 32(10): 878-896. doi: 10.1038/s41422-022-00685-z
[14] YU Z, CHEN Y, ZHOU Y, et al. Rice Gene Index: A comprehensive pan-genome database for comparative and functional genomics of Asian rice[J]. Molecular Plant, 2023, 16(5): 798-801. doi: 10.1016/j.molp.2023.03.012
[15] WANG J, YANG W, ZHANG S, et al. A pangenome analysis pipeline provides insights into functional gene identification in rice[J]. Genome Biology, 2023, 24(1): 19. doi: 10.1186/s13059-023-02861-9
[16] PRUITT K D, TATUSOVA T, MAGLOTT D R. NCBI reference sequences (RefSeq): A curated non-redundant sequence database of genomes, transcripts and proteins[J]. Nucleic Acids Research, 2007, 35(Suppl_1): D61-D65. doi: 10.1093/nar/gkl842
[17] HUBBARD T, BARKER D, BIRNEY E, et al. The Ensembl genome database project[J]. Nucleic Acids Research, 2002, 30(1): 38-41. doi: 10.1093/nar/30.1.38
[18] GOODSTEIN D M, SHU S, HOWSON R, et al. Phytozome: A comparative platform for green plant genomics[J]. Nucleic Acids Research, 2012, 40(DI): D1178-D1186.
[19] SAKAI H, LEE S S, TANAKA T, et al. Rice annotation project database (RAP-DB): An integrative and interactive database for rice genomics[J]. Plant and Cell Physiology, 2013, 54(2): e6. doi: 10.1093/pcp/pcs183
[20] KAWAHARA Y, DE LA BASTIDE M, HAMILTON J P, et al. Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data[J]. Rice, 2013, 6(1): 4. doi: 10.1186/1939-8433-6-4
[21] SONG J M, LEI Y, SHU C C, et al. Rice Information GateWay: A comprehensive bioinformatics platform for indica rice genomes[J]. Molecular Plant, 2018, 11(3): 505-507. doi: 10.1016/j.molp.2017.10.003
[22] SANG J, ZOU D, WANG Z, et al. IC4R-2.0: Rice genome reannotation using massive RNA-seq data[J]. Genomics, Proteomics & Bioinformatics, 2020, 18(2): 161-172.
[23] AGRET C, GOTTIN C, DEREEPER A, et al. South green resources to manage rice big genomics data[C]//The Plant & Animal Genome Conference (PAG). San Diego: Scherago International, 2020.
[24] OHYANAGI H, EBATA T, HUANG X, et al. OryzaGenome: Genome diversity database of wild Oryza species[J]. Plant and Cell Physiology, 2016, 57(1): e1.
[25] MAO L, CHEN M, CHU Q, et al. RiceRelativesGD: A genomic database of rice relatives for rice research[J]. Database, 2019, 2019: baz110. doi: 10.1093/database/baz110
[26] YAO W, LI G, YU Y, et al. funRiceGenes dataset for comprehensive understanding and application of rice functional genes[J]. GigaScience, 2018, 7(1): gix119.
[27] SATO Y, TAKEHISA H, KAMATSUKI K, et al. RiceXPro version 3.0: Expanding the informatics resource for rice transcriptome[J]. Nucleic Acids Research, 2013, 41(D1): D1206-D1213.
[28] WANG L, XIE W, CHEN Y, et al. A dynamic gene expression atlas covering the entire life cycle of rice[J]. The Plant Journal, 2010, 61(5): 752-766. doi: 10.1111/j.1365-313X.2009.04100.x
[29] XIA L, ZOU D, SANG J, et al. Rice Expression Database (RED): An integrated RNA-Seq-derived gene expression database for rice[J]. Journal of Genetics and Genomics, 2017, 44(5): 235-241. doi: 10.1016/j.jgg.2017.05.003
[30] KAWAHARA Y, OONO Y, WAKIMOTO H, et al. TENOR: Database for comprehensive mRNA-seq experiments in rice[J]. Plant and Cell Physiology, 2016, 57(1): e7.
[31] YU Y, ZHANG H, LONG Y, et al. Plant Public RNA-seq Database: A comprehensive online database for expression analysis of ~45 000 plant public RNA-Seq libraries[J]. Plant Biotechnology Journal, 2022, 20(5): 806-808. doi: 10.1111/pbi.13798
[32] ZHANG P, WANG Y, CHACHAR S, et al. eRice: A refined epigenomic platform for japonica and indica rice[J]. Plant Biotechnology Journal, 2020, 18(8): 1642-1644. doi: 10.1111/pbi.13329
[33] XIE L, LIU M, ZHAO L, et al. RiceENCODE: A comprehensive epigenomic database as a rice Encyclopedia of DNA elements[J]. Molecular Plant, 2021, 14(10): 1604-1606. doi: 10.1016/j.molp.2021.08.018
[34] ZHANG B, FEI Y, FENG J, et al. RiceNCexp: A rice non-coding RNA co-expression atlas based on massive RNA-seq and small-RNA seq data[J]. Journal of Experimental Botany, 2022, 73(18): 6068-6077. doi: 10.1093/jxb/erac285
[35] SANAN-MISHRA N, TRIPATHI A, GOSWAMI K, et al. ARMOUR - A rice miRNA: mRNA interaction resource[J]. Frontiers in Plant Science, 2018, 9: 602. doi: 10.3389/fpls.2018.00602
[36] ZHANG Z, XU Y, YANG F, et al. RiceLncPedia: A comprehensive database of rice long non-coding RNAs[J]. Plant Biotechnology Journal, 2021, 19(8): 1492-1494. doi: 10.1111/pbi.13639
[37] LIU W T, YANG C C, CHEN R K, et al. RiceATM: A platform for identifying the association between rice agronomic traits and miRNA expression[J]. Database, 2016, 2016: baw151.
[38] JOHNSON C, BOWMAN L, ADAI A T, et al. CSRDB: A small RNA integrated database and browser resource for cereals[J]. Nucleic Acids Research, 2007, 35(Suppl_1): D829-D833.
[39] KOZOMARA A, BIRGAOANU M, GRIFFITHS-JONES S. miRBase: From microRNA sequences to function[J]. Nucleic Acids Research, 2019, 47(D1): D155-D162. doi: 10.1093/nar/gky1141
[40] YUAN C, MENG X, LI X, et al. PceRBase: A database of plant competing endogenous RNA[J]. Nucleic Acids Research, 2017, 45(D1): D1009-D1014. doi: 10.1093/nar/gkw916
[41] MARSICO M D, PAYTUVI GALLART A, SANSEVERINO W, et al. GreeNC 2.0: A comprehensive database of plant long non-coding RNAs[J]. Nucleic Acids Research, 2022, 50(D1): D1442-D1447. doi: 10.1093/nar/gkab1014
[42] JIN J, LU P, XU Y, et al. PLncDB V2.0: A comprehensive encyclopedia of plant long noncoding RNAs[J]. Nucleic Acids Research, 2021, 49(D1): D1489-D1495. doi: 10.1093/nar/gkaa910
[43] SZCZEŚNIAK M W, BRYZGHALOV O, CIOMBOROWSKA-BASHEER J, et al. CANTATAdb 2.0: Expanding the collection of plant long noncoding RNAs[J]. Methods in Molecular Biology, 2019, 1933: 415-429.
[44] GUO Z, KUANG Z, WANG Y, et al. PmiREN: A comprehensive encyclopedia of plant miRNAs[J]. Nucleic Acids Research, 2020, 48(D1): D1114-D1121. doi: 10.1093/nar/gkz894
[45] XU X, DU T, MAO W, et al. PlantcircBase 7.0: Full-length transcripts and conservation of plant circRNAs[J]. Plant Communications, 2022, 3(4): 100343. doi: 10.1016/j.xplc.2022.100343
[46] GUO X, WANG T, JIANG L, et al. PlaASDB: A comprehensive database of plant alternative splicing events in response to stress[J]. BMC Plant Biology, 2023, 23(1): 225. doi: 10.1186/s12870-023-04234-7
[47] ZHU S, YE W, YE L, et al. PlantAPAdb: A comprehensive database for alternative polyadenylation sites in plants[J]. Plant Physiology, 2020, 182(1): 228-242. doi: 10.1104/pp.19.00943
[48] 徐海冬, 宁博林, 牟芳, 等. 选择性多聚腺苷酸化的生物学效应及其调控机制研究进展[J]. 遗传, 2021, 43(1): 4-15. doi: 10.16288/j.yczz.20-200 [49] HAN L, ZHONG W, QIAN J, et al. A multi-omics integrative network map of maize[J]. Nature Genetics, 2023, 55(1): 144-153. doi: 10.1038/s41588-022-01262-1
[50] LIU C, MA Y, ZHAO J, et al. Computational network biology: Data, models, and applications[J]. Physics Reports, 2020, 846: 1-66. doi: 10.1016/j.physrep.2019.12.004
[51] HAQUE S, AHMAD J S, CLARK N M, et al. Computational prediction of gene regulatory networks in plant growth and development[J]. Current Opinion in Plant Biology, 2019, 47: 96-105. doi: 10.1016/j.pbi.2018.10.005
[52] YAN J, WANG X. Machine learning bridges omics sciences and plant breeding[J]. Trends in Plant Science, 2023, 28(2): 199-210. doi: 10.1016/j.tplants.2022.08.018
[53] SATO Y, NAMIKI N, TAKEHISA H, et al. RiceFREND: A platform for retrieving coexpressed gene networks in rice[J]. Nucleic Acids Research, 2013, 41(Database issue): D1214-D1221.
[54] HAMADA K, HONGO K, SUWABE K, et al. OryzaExpress: An integrated database of gene expression networks and omics annotations in rice[J]. Plant and Cell Physiology, 2011, 52(2): 220-229.
[55] LIN H, YU J, PEARCE S P, et al. RiceAntherNet: A gene co-expression network for identifying anther and pollen development genes[J]. The Plant Journal, 2017, 92(6): 1076-1091. doi: 10.1111/tpj.13744
[56] SIRCAR S, MUSADDI M, PAREKH N. NetREx: Network-based rice expression analysis server for abiotic stress conditions[J]. Database, 2022, 2022: baac060.
[57] GU H, ZHU P, JIAO Y, et al. PRIN: A predicted rice interactome network[J]. BMC Bioinformatics, 2011, 12: 161. doi: 10.1186/1471-2105-12-161
[58] LEE T, OH T, YANG S, et al. RiceNet v2: An improved network prioritization server for rice genes[J]. Nucleic Acids Research, 2015, 43(W1): W122-W127. doi: 10.1093/nar/gkv253
[59] LIU S, LIU Y, ZHAO J, et al. A computational interactome for prioritizing genes associated with complex agronomic traits in rice (Oryza sativa)[J]. The Plant Journal, 2017, 90(1): 177-188. doi: 10.1111/tpj.13475
[60] PENG H, WANG K, CHEN Z, et al. MBKbase for rice: An integrated omics knowledgebase for molecular breeding in rice[J]. Nucleic Acids Research, 2020, 48(D1): D1085-D1092.
[61] ZHAO H, YAO W, OUYANG Y, et al. RiceVarMap: A comprehensive database of rice genomic variations[J]. Nucleic Acids Research, 2015, 43(Database issue): D1018-D1022.
[62] MANSUETO L, FUENTES R R, BORJA F N, et al. Rice SNP-seek database update: New SNPs, indels, and queries[J]. Nucleic Acids Research, 2017, 45(D1): D1075-D1081. doi: 10.1093/nar/gkw1135
[63] YAN J, ZOU D, LI C, et al. SR4R: An integrative SNP resource for genomic breeding and population research in rice[J]. Genomics, Proteomics & Bioinformatics, 2020, 18(2): 173-185.
[64] YONEMARU J, EBANA K, YANO M. HapRice, an SNP haplotype database and a web tool for rice[J]. Plant and Cell Physiology, 2014, 55(1): e9.
[65] WANG C, YU H, HUANG J, et al. Towards a deeper haplotype mining of complex traits in rice with RFGB v2.0[J]. Plant Biotechnology Journal, 2020, 18(1): 14-16. doi: 10.1111/pbi.13215
[66] 鄂志国, 王磊. 中国水稻品种及其系谱数据库[J]. 中国水稻科学, 2011, 25(5): 565-566. doi: 10.3969/j.issn.1001-7216.2011.05.017 [67] COPETTI D, ZHANG J, EL BAIDOURI M, et al. RiTE database: A resource database for genus-wide rice genomics and evolutionary biology[J]. BMC Genomics, 2015, 16(1): 538. doi: 10.1186/s12864-015-1762-3
[68] LIU Z, WANG T, WANG L, et al. RTRIP: A comprehensive profile of transposon insertion polymorphisms in rice[J]. Plant Biotechnology Journal, 2020, 18(12): 2379-2381. doi: 10.1111/pbi.13425
[69] 刘耀光, 李构思, 张雅玲, 等. CRISPR/Cas植物基因组编辑技术研究进展[J]. 华南农业大学学报, 2019, 40(5): 38-49. doi: 10.7671/j.issn.1001-411X.201905058 [70] 李文龙, 栾鑫, 张强, 等. 基于CRISPR/Cas9基因编辑技术的水稻定向改良研究进展[J]. 广东农业科学, 2022, 49(9): 114-124. doi: 10.16768/j.issn.1004-874X.2022.09.012 [71] 何晓玲, 刘鹏程, 马伯军, 等. 基于CRISPR/Cas9的基因编辑技术研究进展及其在植物中的应用[J]. 植物学报, 2022, 57(4): 508-531. [72] ANZALONE A V, RANDOLPH P B, DAVIS J R, et al. Search-and-replace genome editing without double-strand breaks or donor DNA[J]. Nature, 2019, 576(7785): 149-157. doi: 10.1038/s41586-019-1711-4
[73] XIE X, MA X, ZHU Q, et al. CRISPR-GE: A convenient software toolkit for CRISPR-based genome editing[J]. Molecular Plant, 2017, 10(9): 1246-1249. doi: 10.1016/j.molp.2017.06.004
[74] LIU W, XIE X, MA X, et al. DSDecode: A web-based tool for decoding of sequencing chromatograms for genotyping of targeted mutations[J]. Molecular Plant, 2015, 8(9): 1431-1433. doi: 10.1016/j.molp.2015.05.009
[75] BAE S, KWEON J, KIM H S, et al. Microhomology-based choice of Cas9 nuclease target sites[J]. Nature Methods, 2014, 11(7): 705-706. doi: 10.1038/nmeth.3015
[76] XIE X, LI F, TAN X, et al. BEtarget: A versatile web-based tool to design guide RNAs for base editing in plants[J]. Computational and Structural Biotechnology Journal, 2022, 20: 4009-4014. doi: 10.1016/j.csbj.2022.07.046
[77] XIE X, LIU W, DONG G, et al. MMEJ-KO: A web tool for designing paired CRISPR guide RNAs for microhomology-mediated end joining fragment deletion[J]. Science China Life Sciences, 2021, 64(6): 1021-1024. doi: 10.1007/s11427-020-1797-3
[78] MUTWIL M, OBRO J, WILLATS W G T, et al. GeneCAT: Novel webtools that combine BLAST and co-expression analyses[J]. Nucleic Acids Research, 2008, 36(Suppl_2): W320-W326.
[79] PARK J, BAE S, KIM J S. Cas-Designer: A web-based tool for choice of CRISPR-Cas9 target sites[J]. Bioinformatics, 2015, 31(24): 4014-4016. doi: 10.1093/bioinformatics/btv537
[80] BAE S, PARK J, KIM J S. Cas-OFFinder: A fast and versatile algorithm that searches for potential off-target sites of Cas9 RNA-guided endonucleases[J]. Bioinformatics, 2014, 30(10): 1473-1475. doi: 10.1093/bioinformatics/btu048
[81] LEI Y, LU L, LIU H Y, et al. CRISPR-P: A web tool for synthetic single-guide RNA design of CRISPR-system in plants[J]. Molecular Plant, 2014, 7(9): 1494-1496. doi: 10.1093/mp/ssu044
[82] LIU H, DING Y, ZHOU Y, et al. CRISPR-P 2.0: An improved CRISPR-Cas9 tool for genome editing in plants[J]. Molecular Plant, 2017, 10(3): 530-532. doi: 10.1016/j.molp.2017.01.003
[83] FAN J, SHI L, LIU Q, et al. Annotation and evaluation of base editing outcomes in multiple cell types using CRISPRbase[J]. Nucleic Acids Research, 2023, 51(D1): D1249-D1256. doi: 10.1093/nar/gkac967
[84] ZHENG Y, ZHANG N, MARTIN G B, et al. Plant genome editing database (PGED): A call for submission of information about genome-edited plant mutants[J]. Molecular Plant, 2019, 12(2): 127-129. doi: 10.1016/j.molp.2019.01.001
[85] HONG W J, KIM Y J, KIM E J, et al. CAFRI-Rice: CRISPR applicable functional redundancy inspector to accelerate functional genomics in rice[J]. The Plant Journal, 2020, 104(2): 532-545. doi: 10.1111/tpj.14926
[86] LIU Q, PENG X, SHEN M, et al. Ribo-uORF: A comprehensive data resource of upstream open reading frames (uORFs) based on ribosome profiling[J]. Nucleic Acids Research, 2023, 51(D1): D248-D261. doi: 10.1093/nar/gkac1094
[87] XUE C, QIU F, WANG Y, et al. Tuning plant phenotypes by precise, graded downregulation of gene expression[J]. Nature Biotechnology, 2023. doi: 10.1038/s41587-023-01707-w.
[88] 王向峰, 才卓. 中国种业科技创新的智能时代: “玉米育种4.0”[J]. 玉米科学, 2019, 27(1): 1-9. doi: 10.13597/j.cnki.maize.science.20190101 [89] PéREZ P, DE LOS CAMPOS G. Genome-wide regression and prediction with the BGLR statistical package[J]. Genetics, 2014, 198(2): 483-495. doi: 10.1534/genetics.114.164442
[90] PÉREZ-RODRÍGUEZ P, GIANOLA D, GONZÁLEZ-CAMACHO J M, et al. Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat[J]. G3:Genes| Genomes| Genetics, 2012, 2(12): 1595-1605.
[91] CHARMET G, TRAN L G, AUZANNEAU J, et al. BWGS: A R package for genomic selection and its application to a wheat breeding programme[J]. PLoS One, 2020, 15(4): e0222733. doi: 10.1371/journal.pone.0222733
[92] YAN J, XU Y, CHENG Q, et al. LightGBM: Accelerated genomically designed crop breeding through ensemble learning[J]. Genome Biology, 2021, 22(1): 271. doi: 10.1186/s13059-021-02492-y
[93] MA W, QIU Z, SONG J, et al. A deep convolutional neural network approach for predicting phenotypes from genotypes[J]. Planta, 2018, 248(5): 1307-1318. doi: 10.1007/s00425-018-2976-9
[94] WANG K, ABID M A, RASHEED A, et al. DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants[J]. Molecular Plant, 2023, 16(1): 279-293. doi: 10.1016/j.molp.2022.11.004
[95] YIN L, ZHANG H, TANG Z, et al. HIBLUP: An integration of statistical models on the BLUP framework for efficient genetic evaluation using big genomic data[J]. Nucleic Acids Research, 2023, 51(8): 3501-3512. doi: 10.1093/nar/gkad074
[96] YIN L, ZHANG H, ZHOU X, et al. KAML: Improving genomic prediction accuracy of complex traits using machine learning determined parameters[J]. Genome Biology, 2020, 21(1): 146. doi: 10.1186/s13059-020-02052-w
[97] MOHAMMADI M, TIEDE T, SMITH K P. PopVar: A genome-wide procedure for predicting genetic variance and correlated response in biparental breeding populations[J]. Crop Science, 2015, 55(5): 2068-2077. doi: 10.2135/cropsci2015.01.0030
[98] ENDELMAN J B. Ridge regression and other kernels for genomic selection with R package rrBLUP[J]. The Plant Genome, 2011, 4(3): 250-255. doi: 10.3835/plantgenome2011.08.0024
[99] COVARRUBIAS-PAZARAN G. Genome-assisted prediction of quantitative traits using the R package sommer[J]. PLoS One, 2016, 11(6): e0156744. doi: 10.1371/journal.pone.0156744
[100] BUDHLAKOTI N, MISHRA D C, RAI A, et al. STGS: Genomic selection using single trait [EB/OL]. [2023-06-30]. https://cran.r-project.org/web/packages/STGS.
[101] YANG J, LEE S H, GODDARD M E, et al. GCTA: A tool for genome-wide complex trait analysis[J]. American Journal of Human Genetics, 2011, 88(1): 76-82. doi: 10.1016/j.ajhg.2010.11.011
[102] CHENG H, FERNANDO R, GARRICK D, et al. JWAS: Julia implementation of whole-genome analysis software[C]//Proceedings of the World Congress on Genetics Applied to Livestock Production. Auckland, New Zealand: World Congress on Genetics Applied to Livestock Production, 2018.
[103] KANG H, NING C, ZHOU L, et al. PIBLUP: High-performance software for large-scale genetic evaluation of animals and plants[J]. Frontiers in Genetics, 2018, 9: 226. doi: 10.3389/fgene.2018.00226
[104] TECLE I Y, EDWARDS J D, MENDA N, et al. solGS: A web-based tool for genomic selection[J]. BMC Bioinformatics, 2014, 15(1): 398. doi: 10.1186/s12859-014-0398-7
[105] CHEN C J, ZHANG Z. iPat: Intelligent prediction and association tool for genomic research[J]. Bioinformatics, 2018, 34(11): 1925-1927.
[106] XU Y, MA K, ZHAO Y, et al. Genomic selection: A breakthrough technology in rice breeding[J]. The Crop Journal, 2021, 9(3): 669-677. doi: 10.1016/j.cj.2021.03.008
[107] XU Y, ZHANG X, LI H, et al. Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction[J]. Molecular Plant, 2022, 15(11): 1664-1695. doi: 10.1016/j.molp.2022.09.001
[108] 蒋金金, 苏汉东, 洪登峰, 等. 植物生物技术研究进展[J]. 植物生理学报, 2023, 59(8): 1436-1462. doi: 10.13592/j.cnki.ppj.600006
计量
- 文章访问数: 1433
- HTML全文浏览量: 42
- PDF下载量: 109
