王向峰

王向峰，男，1978 年生，博士生导师，中国农业大学教授。入选国家级人才计划。2002 年获中国农业大学生物科学学士学位；2007 年获北京大学生物信息学博士学位；2007 到2010 年先后在美国耶鲁大学、哈佛大学完成博士后阶段工作；2010 到2014 年期间，在美国亚利桑那大学，农业与生命科学学院，任终身制助理教授。主要研究方向为利用组学大数据从事玉米杂交育种理论、玉米杂种优势遗传互作机制、玉米基因组驯化、玉米适应性演化机制等方面的工作。应用人工智能与机器学习技术开发玉米智能设计育种决策模型、全基因组选择模型、基因型与环境互作模型；开发玉米育种信息管理系统、育种大数据分析软件；开发玉米多组学数据关联分析算法、种质资源挖掘工具、各类生物信息软件。在Genome Biology、Science Bulletin 、Plant Cell 、PNAS, Trends in Plant Science 、Molecular Plant、Plant Journal 等国际知名期刊发表论文60 余篇。

王向峰课题组招收我校“生物科学”与“生物育种”强基计划学生。辅修过我校信电学院的“数据科学与大数据技术（辅修）”专业的学生，可以被优先考虑进入实验室开展轮转课题，以及作为推免生提前开展硕博阶段的工作。强基计划学生可以在大一下学期考虑辅修“数据科学与大数据技术”相关课程，包括：Linux应用基础与实验、程序设计I与实验、数据库原理与实验、Web技术应用与实验、计算机系统基础与实验、程序设计I与实验、数据结构与算法实验、大数据统计方法与实验、大数据挖掘技术与实验、大数据存储处理技术与实验、大数据分析与可视化技术与实验、人工智能技术与实验。

王向峰教授以解决我国玉米商业化育种体系中的关键技术与重大需求为导向，致力于育种模型、理论与新方法的研究。近5年来，从基于多组学的种质资源挖掘工具、全基因组选择模型、智能设计育种决策模型、基因型与环境互作模型等方法学层面；到玉米杂种优势的利用、表型可塑性的环境响应等理论层面；到开发玉米育种信息管理系统、交互式数字育种平台等应用层面，开展系统性研究工作。代表性成果包括：1）开发多组学数据关联分析软件MODAS，并应用于玉米耐盐基因的挖掘与利用；2）开发基于机器学习算法的作物基因组设计育种工具包CropGBM与基因组优化虚拟设计育种模型GOVS；3）探索玉米杂种优势的利用规律与玉米表型可塑性对环境响应的生物学基础；4）集成自主研究成果，打造为我国种业服务的智能育种技术体系。申请人在国际期刊发表精准育种相关论文10余篇，相关成果为我国商业化育种体系的建设与现代种业的智能化升级提供技术支撑与理论指导。

育种模型、软件与数据库，以及生物信息工具

1.  Wang Lab’s Smart Breeding team webpage (http://ibreeding.org/)

2.     CropGBM: Genomic Breeding Machines for Crops (https://ibreeding.github.io/)

3.     IP4GS: Integrated Platform 4 Genomic Selection (https://ngdc.cncb.ac.cn/ip4gs/)

4.     G2P: Singularity platform-based Genotype-to-Phenotype prediction environment (https://g2p-env.github.io/)

5.     MODAS: Multi-Omics Data Association Studies (https://modas-bio.github.io/)

6.     GOVS: Genome Optimization by Virtual Simulation (https://govs-pack.github.io/)

7.     SR4R database: SNP Ready for Rice (http://sr4r.ic4r.org/)

8.     iFLAS: Integrative Full Length Alternative Splicing analysis (http://github.com/CrazyHsu/iFLAS_toolkit)

发表文章（^# 通讯作者，^* 第一作者）

1.     Li T, Jiang S, Fu R, Wang X, Cheng Q and Jiang S (2023) IP4GS: Bringing genomic selection analysis to breeders. Front. Plant Sci. 14:1131493.

2.     Fu R, Wang X^#. Modeling the influence of phenotypic plasticity on maize hybrid performance. Plant Communications. 2023 Jan 11 online, (In press)

3.     Yan J, Wang X^#. Machine learning bridges omics sciences and plant breeding. Trends in Plant Sciences. 2022. S1360-1385(22)00224-2.

4.     Yan J, Wang X^#. Unsupervised and semi-supervised learning: the next frontier in machine learning for plant systems biology. ThePlant Journal. 2022. 111(6): 1527-1538.

5.     Liu S, Xu F, Xu Y, Wang Q, Yan J, Wang J, Wang X, Wang X^#. MODAS: exploring maize germplasm with multi-omics data association studies. Science Bulletin. 2022. 67(9), 903-906.

6.     Cheng Q, Jiang S, Xu F, Wang Q, Xiao Y, Zhang R, Zhao J, Yan J, Ma C^#, Wang X^#. Genome Optimization via Virtual Simulation to Accelerate Maize Hybrid Breeding. Briefings in Bioinformatics. 2022 Jan 17;23(1): bbab447.

7.     Yan J, Xu Y, Cheng Q, Jiang S, Wang Q, Xiao Y, Ma C, Yan J^#, Wang X^#. LightGBM: accelerated genomically designed crop breeding through ensemble learning. Genome Biology. 2021 Sep 20;22(1):271.

8.     Liang X, Liu S, Wang T, Li F, Cheng J, Lai J, Qin F, Li Z^#, Wang X^#, Jiang C^#. Metabolomics-driven gene mining and genetic improvement of tolerance to salt-induced osmotic stress in maize. New Phytologist. 2021;230 (6):2355-2370.

9.     Xu Y, Laurie J, Wang X^#. CropGBM: An ultra-efficient machine learning toolbox for genomic selection-assisted breeding in crops. 2021 Oct, In: Bilichak A., Laurie J.D. (eds) Accelerated Breeding of Cereal Crops. Springer Protocols Handbooks. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1526-3_5

10.  McGowan M, Wang J, Dong H, Liu X, Jia Y, Wang X, Iwata H, Li Y, Lipka A.E, Zhang Z. Ideas in Genomic Selection with the Potential to Transform Plant Molecular Breeding: A Review. 2021. A chapter for the book Plant Breeding Reviews. Volume 45. John Wiley & Sons, Inc.

11.  Yang C, Yan J, Jiang S, Li X, Min H, Wang X^#, Hao D^#. Resequencing 250 soybean accessions: new insights into genes associated with agronomic traits and genetic networks. Genomics Proteomics Bioinformatics. 2021. July 24; S1672- 0229 (21) 00160-1.

12.  Cui F, Taier G, Wang X, Wang K. Genome-Wide Analysis of the HSP20 Gene Family and Expression Patterns of HSP20 Genes in Response to Abiotic Stresses in Cynodon transvaalensis. Frontiers in Genetics. 2021. 12:732812.

13.  CNCB-NGDC Members and Partners. Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2021. Nucleic Acids Res. 2021 Jan 8;49(D1): D18-D28

14.  Cui F, Taier G, Li M, Dai X, Hang N, Zhang X, Wang X^#, Wang K^#. The genome of the warm-season turfgrass African bermudagrass (Cynodon transvaalensis). Horticulture Research. 2021 May 1;8(1):93

15.  Xiao Y, Jiang S, Cheng Q, Wang X, Yan J, Zhang R, Qiao F, Ma C, Luo J, Li W, Liu H, Yang W, Song W, Meng Y, Warburton ML, Zhao J^#, Wang X^#, Yan J^#. The genetic mechanism of heterosis utilization in maize improvement. Genome Biology. 2021. 10;22(1):148.

16.  Zhang SJ, Liu L, Yang R^#, Wang X^#. Genome size evolution mediated by Gypsy retrotransposons in Brassicaceae. Genomics Proteomics Bioinformatics. 2020. 18(3):321-332.

17.  Jiang S, Cheng Q, Yan J, Fu R, Wang X^#. Genome optimization for improvement of maize breeding.Theor Appl Genet. 2020. 133(5):1491-1502.

18.  Li H, Jiang S, Li C, Liu L, Lin Z, He H, Deng XW, Zhang Z^#, Wang X^#. The hybrid protein interactome contributes to rice heterosis as epistatic effects. Plant Journal. 2020. 102(1):116-128

19.  Yan J, Zou D, Li C, Zhang Z, Song S^#, Wang X^#. SR4R: An Integrative SNP Resource for Genomic Breeding and Population Research in Rice. Genomics Proteomics Bioinformatics. 2020. 18(2):173-185

20.  Lin Z, Qin P, Zhang X, Fu C, Deng H, Fu X, Huang Z, Jiang S, Li C, Tang X, Wang X, He G, Yang Y, He H, Deng XW. Divergent selection and genetic introgression shape the genome landscape of heterosis in hybrid rice. Proc Natl Acad Sci USA. 2020. 3;117(9):4623-4631

21.  Zhang H, Zhang Q, Zhai H, Gao S, Yang L, Wang Z, Xu Y, Huo J, Ren Z, Zhao N, Wang X, Li J, Liu Q, He S. IbBBX24 Promotes the Jasmonic Acid Pathway and Enhances Fusarium Wilt Resistance in Sweet Potato. Plant Cell. 2020 Apr; 32(4):1102-1123.

22.  Guo W, Zhu P, Pellegrini M, Zhang MQ, Wang X, Ni Z. CGmapTools improves the precision of heterozygous SNV calls and supports allele-specific methylation detection and visualization in bisulfite-sequencing data. Bioinformatics. 2018. 1;34(3):381-387

23.  Liang P, Liu S, Xu F, Jiang S, Yan J, He Q, Liu W, Lin C, Zheng F, Wang X^#, Miao W^#. Powdery mildews are characterized by contracted carbohydrate metabolism and diverse effectors to adapt to obligate biotrophic lifestyle. Frontiers in Microbiology. 2018. 18;9:3160.

24.  Zhang SJ, Meng P, Zhang J, Jia P, Lin J, Wang X, Chen F^#, Wei X^#. Machine learning models for genetic risk assessment of infants with non-syndromic orofacial cleft. Genomics Proteomics Bioinformatics. 2018. 16(5):354-364.

25.  Zhan J, Li G, Ryu CH, Ma C, Zhang S, Lloyd A, Hunter BG, Larkins BA, Drews GN, Wang X, Yadegari R. Opaque-2 regulates a complex gene network associated with cell differentiation and storage functions of maize endosperm. Plant Cell. 2018. 30(10):2425-2446. 

26.  Zhang SJ, Wang C, Yan S, Fu A, Luan X, Li Y, Sunny Shen Q, Zhong X, Chen JY, Wang X, Chin-Ming Tan B, He A, Li CY. Isoform evolution in primates through independent combination of alternative RNA processing events. Molecular Biology Evolution. 2017. 1;34(10):2453-2468

27.  Wang Y, Yu H, Tian C, Sajjad M, Gao C, Tong Y, Wang X^#, Jiao Y^#. Transcriptome association identifies regulators of wheat spike architecture. Plant Physiology. 2017. 175(2):746-757

28.  Zhang H, Zhang Q, Zhai H, Li Y, Wang X, Liu Q, He S, Transcript profile analysis reveals important roles of jasmonic acid signalling pathway in the response of sweet potato to salt stress. Scientific Reports. 2017. 13;7:40819

29.  Yan J, Lv S, Hu M, Gao Z, He H, Ma Q, Deng XW, Zhu Z^#, Wang X^#. Single-Molecule Sequencing Assists Genome Assembly Improvement and Structural Variation Inference. Molecular Plants. 2016. 6;9(7):1085-7

30.  IC4R Project Consortium., Hao L, Zhang H, Zhang Z, Hu S, Xue Y, Wang X etc., Information Commons for Rice (IC4R), Nucleic Acids Res. 2016 4;44: D1172-80.

31.  Xin M, Yang G, Yao Y, Peng H, Hu Z, Sun Q, Wang X, Ni Z. Temporal small RNA transcriptome profiling unraveled partitioned miRNA expression in developing maize endosperms between reciprocal crosses. Front Plant Sci. 2015. 15;6:744

32.  Yao X, Arst HN Jr, Wang X, Xiang X., Discovery of a vezatin-like protein for dynein-mediated early endosome transport. Mol Biol Cell. 2015.1;26(21):3816-27

33.  Zhan J, Thakare D, Ma C, Lloyd A, Nixon NM, Arakaki AM, Burnett WJ, Logan KO, Wang D, Wang X, Drews GN, Yadegari R., RNA sequencing of laser-capture microdissected compartments of the maize kernel identifies regulatory modules associated with endosperm cell differentiation. Plant Cell. 2015. 27(3):513-31

34.  Ma C, Zhang HH, Wang X. Machine learning for Big Data analytics in plants. Trends in Plant Sciences. 2014. 19(12):798-808.

35.  Thakare D, Yang R, Steffen JG, Zhan J, Wang D, Clark RM, Wang X, Yadegari R, RNA-Seq analysis of laser-capture microdissected cells of the developing central starchy endosperm of maize, Genomic Data. 2014 Aug 7;2:242-5

36.  Xin M, Yang R, Yao Y, Ma C, Peng H, Sun Q, Wang X, Ni Z., Dynamic parent-of-origin effects on small interfering RNA expression in the developing maize endosperm. BMC Plant Biol. 2014. 24;14:192

37.  Yao X, Wang X, Xiang X. FHIP and FTS proteins are critical for dynein mediated transport of early endosomes in Aspergillus. Mol Biol Cell. 2014 Jul 15;25(14):2181-9

38.  Li G, Wang D, Yang R, Logan K, Chen H, Zhang S, Skaggs MI, Lloyd A, Burnett WJ, Laurie JD, Hunter BG, Dannenhoffer JM, Larkins BA^#, Drews GN, Wang X^#, Yadegari R^#. Temporal patterns of gene expression in developing maize endosperm identified through transcriptome sequencing. Proc Natl Acad Sci. USA 2014. 27;111(21):7582-7

39.  Ma C, Xin M, Feldmann KA, Wang X^#. Machine Learning-Based Differential Network Analysis: A Study of Stress-Responsive Transcriptomes in Arabidopsis. Plant Cell. 2014 Feb;26(2):520-37

40.  Xin M, Yang R, Li G, Chen H, Laurie J, Ma C, Wang D, Yao Y, Larkins B, Sun Q, Yadegari, R, Wang X^# and Ni Z. Dynamic expression of imprinted genes associates with maternally controlled nutrient allocation during maize endosperm development. Plant Cell. 2013. 25(9):3212-27;

41.  Zhang Y, Yu N, Huang Q, Yin G, Guo A, Wang X, Xiong Z, Liu Z. Complete genome of Hainan papaya ringspot virus using small RNA deep sequencing. Virus Genes. 2014 Jun;48(3):502-8

42.  Chen, H and Wang, X^#. CrusView: a Java-based visualization platform for comparative genomics analyses in Brassicaceae species. 2013. Plant Physiology; 163(1):354-62

43.  Wei, X and Wang, X^#. A computational workflow to identify allele-specific expression and epigenetic modification in maize. 2013. Genomics Proteomics Bioinformatics; 11(4): 247-52

44.  Yang R, Chen H, Jarvis D, Beilstein M, Grimwood J, Jenkins J, Shu S, Prochnik S, Xin M, Ma C, Schmutz J, Wing R, Mitchell-Olds T, Schumaker K^#, Wang X^#. The reference genome of the halophytic plant Eutrema salsugineum. 2013. Frontier in Plant Sciences; 4:46

45.  Ma C, Chen H, Yang R, Xin M, Wang X^#. KGBassembler: A karyotype-based genome assembler for Brassicaceae species. 2012. Bioinformatics; 28(23):3141-3

46.  Yang R and Wang X^#. Organ evolution in angiosperms driven by correlated divergences of gene sequences and expression patterns. 2012. The Plant Cell; 25(1):71-82

47.  Ma C and Wang X^#. Application of the Gini correlation coefficient to infer regulatory relationships in transcriptome analysis. 2012. Plant Physiology; 160(1):192-203

48.  Xin M, Wang X, Peng H, Yao Y, Xie C, Han Y, Ni Z, Sun Q. Transcriptome comparison of susceptible and resistant wheat in response to powdery mildew infection. 2012. Genomics, Proteomics Bioinformatics; 10(2):94-106

49.  Wang X^# and Liu XS^#. Systematic curation of miRBase microRNA annotation using integrated deep small RNA sequencing data for C. elegans and Drosophila. 2011. Frontiers in Genetics; 2:25.

50.  Wang X^#, Laurie J, Liu T, Wentz J, Liu XS^#. Computational dissection of Arabidopsis smRNAome leads to discovery of RNA interference machinery associated with transcription start sites. 2011. Genomics; 97(4):235-43.

51.  Zhang H, He H, Wang X, Li L and Deng X. Genome-wide identification of Hy5 binding sites in Arabidopsis. 2011. ThePlant Journal; 65(3):346-58

52.  He H, Zhang H, Wang X, Wu N, Yang X, Chen R, Li Y, Deng XW and Li L. Development of a versatile, target-oriented tiling microarray assay for measuring allele-specific gene expression. 2010. Genomics; 96(5):308-15

53.  He G, Zhu X, Elling AA, Chen L, Wang X, Guo L, Liang M, He H, Zhang H, Chen F, Qi Y, Chen R and Deng XW. Global Epigenetic and Transcriptional Trends among Two Rice Subspecies and Their Reciprocal Hybrids. 2010. The Plant Cell; 22:17-33.

54.  Zhou J, Wang X, He K, Stocl V, Tongprasit W, Elling A, Charron J, Deng XW. Genome-wide profiling of histone H3 lysine 9 acetylation and dimethylation in Arabidopsis reveals correlation between multiple histone marks and gene expression. 2010. Plant Molecular Biology; 72:6, 585-595

55.  Wang X^*, Elling A^*, Li X^*, Li L^*, Charron J, Martinessen R, Wang J, Peng Z, Qi Y, Liu XS and Deng X. Genome-wide and organ-specific landscapes of epigenetic modifications and their relationships to mRNA and smRNA transcriptomes in maize. 2009. The Plant Cell; 21(4): 1053–1069

56.  Wang X^*, Yu Z^*, Deng XW, Li L. Transcriptionally active gene fragments derived from potentially fast-evolving donor genes in the rice genome. (2009). Bioinformatics; 15;25(10): 1215-1218.

57.  Li X^*, Wang X^*, He K, Ma Y, Su N, He H, Stolc V, Tongprasit W, Jin W, Jiang J, Terzaghi W, Li S & Deng XW. High-resolution mapping of epigenetic modifications of the rice genome uncovers interplay between DNA methylation, histone methylation, and gene expression. (2008). The Plant Cell; 20: 259-276

58.  Wang X. Statistical Analysis of Tiling-path microarrays. (2008) A Chapter for the book: Oligonucleotide Array Sequence Analysis; NOVA Science Publisher, New York, USA ISBN: 978-1-60456-542-3

59.  Peng Z, Zhang H, Liu T, Dzikiewicz K, Li S, Wang X, Hu G, Zhu Z, Wei X, Zhu Q, Sun Z,Ge S, Ma L, Li L and Deng XW. Characterization of the genome expression trends in the heading-stage panicle of six rice lineages. (2008). Genomics; 93: 169-178;

60.  Yin BL, Guo L, Zhang DF, Terzaghi W, Wang X, Liu TT, He H, Cheng ZK and Deng XW. Integration of Cytological Features with Molecular and Epigenetic Properties of Rice Chromosome 4. (2008). Molecular Plant; 1: 816-829;

61.  Zhang HY, He H, Chen LB, Li L, Liang MZ, Wang X, Liu XG, He GM, Chen RS, Ma LG, and Deng XW. A Genome-Wide Transcription Analysis Reveals a Close Correlation of Promoter INDEL Polymorphism and Heterotic Gene Expression in Rice Hybrids. (2008) Molecular Plant; 1: 720-731;

62.  Li L, He H, Zhang J, Wang X, Bai S, Stolc V, Tongprasit W, Young ND, Yu O, Deng XW. Transcriptional analysis of highly syntenic regions between Medicago truncatula and Glycine max using tiling microarrays. (2008). Genome Biology; 19;9(3):R57

63.  Li L^*, Wang X^*, Sasidharan R., Stolc V, Deng W, He H, Korbel J, Chen X, Tongprasit W, Ronald P, Chen R, Gerstein M, Deng XW. Global identification and characterization of transcriptionally active regions in the rice genome. (2007). PLoS ONE; 2(3): e294

64.  Zhou J^*, Wang X^*, Jiao Y, Qin Y, Liu X, He K, Chen C, Ma L, Wang J, Xiong L, Zhang Q, Fan L, Deng XW. Global genome expression analysis of rice in response to drought and high-salinity stresses in shoot, flag leaf, and panicle. (2007). Plant Molecular Biology; 63(5):591-608.

65.  Wang X^*, He H^*, Li L, Chen R, Deng XW, Li S. NMPP: a user-customized NimbleGen microarray data processing pipeline. (2006) Bioinformatics; 22(23): 2955-2957;

66.  Wang X^*, Li L^*, Chen C, Wang J, Li S, and Deng XW. Analysis of oligo hybridization properties by high-resolution tiling arrays in rice. (2006) Proceeding report, 5th International Rice Genetics Symposium; 65-76. 19-23;

67.  Li L^*, Wang X^*, Stolc V, Li X, Zhang D, Su N, Tongprasit W, Li S, Cheng Z, Wang J, Deng XW. Genome-wide transcription analyses in rice using tiling microarrays. (2006) Nature Genetics; 38: 124-129.

68.  Li L^*, Wang X^*, Xia M, Stolc V, Su N, Peng Z, Li S, Wang J, Wang X, Deng XW. Tiling microarray analysis of rice chromosome 10 to identify the transcriptome and relate its expression to chromosomal architecture. (2005) Genome Biology; 6(6):R52.

69.  Stolc V^*, Li L^*, Wang X^*, Li X, Su N, Tongprasit W, Han B, Xue Y, Li J, Snyder1 M, Gerstein M, Wang J, Deng XW. A pilot study of transcription unit analysis in rice using tiling-path microarray. (2005) Plant Mol Biol; 59(1):137–14

70.  Ma L, Chen C, Liu X, Jiao Y, Su N, Li L, Wang X, Cao M, Sun N, Zhang X, Bao J, Li J, Pedersen S, Bolund L, Zhao H, Yuan L, Wong GKS, Wang J, Deng XW, Wang J. A microarray analysis of the rice transcriptome and its comparison to Arabidopsis. (2005). Genome Research; 15(9):1274-1283

71.  Jiao Y^*, Jia P^*, Wang X^*, Su N, Yu S, Zhang D, Ma L, Feng Q, Jin Z, Li L, Xue Y, Cheng Z, Zhao H, Han B, Deng XW. A Tiling Microarray Expression Analysis of Rice Chromosome 4 Suggests a Chromosome-Level Regulation of Transcription. (2005) The Plant Cell; 17(6):1641-57. Epub 2005 Apr 29

72.  Li L^*, Wang X^*, Li X, Su N, Stolc V, Han B, Li J, Xue Y, Wang J, Deng XW. Toward genome-wide transcriptional analysis in rice using MAS oligonucelotide tiling-path microarrays. (2005). Rice Is Life: Scientific Perspectives for World Rice Research.

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