Genome Wide Identification of Stresses-resistant SNPs and Ge

发布时间：2024-02-03 18:27

　　水稻(Oryza sativa L.)对全球数十亿人的生活至关重要。因为环境因素多种多样,包括可能限制水稻产量的生物和非生物逆境胁迫,使得大米的生产一直面临挑战。深入探究与水稻抗逆性状密切相关的基因对水稻育种实践具有重要的指导意义。应用不同测序技术产生的大量作物基因组DNA序列信息让研究人员能够从中识别出大量的分子遗传标记,并将这些标记用于分析作物的遗传性状和品种改良。新一代测序技术正在加快基因组测序的速度,同时大量的基因组测序产生了海量数据,这为数据管理分析带来巨大的挑战。此外,基因组重测序序列分析流程中的各种工具具有参数设置与使用复杂的特点,使研究人员,尤其是非生物信息学专业人员,在生物大数据前面临诸多困难。为了实现作物基因组重测序数据的流程化管理与分析,本文首先设计了全新的作物基因组重测序数据分析与基因组变异挖掘技术平台;然后应用该平台技术,从对不同逆境胁迫具有不同抗性能力的水稻品种中检测出SNP(Single Nucleotide Polymorphism)及其基因,并构建了一个水稻抗逆胁迫SNPs数据库;最后,在广泛收集抗稻瘟病水稻品种及其基因组重测序数据的基础上,应用上述平...

【文章页数】：166 页

【学位级别】：博士

【文章目录】：
摘要
ABSTRACT
1 Introduction
    1.1 Rice importance and challenges
    1.2 Breeding technologies
    1.3 Rice NGS
    1.4 Study aims and objectives
2 LITERATURE REVIEW
    2.1 Next Generation Sequencing technology
        2.1.1 Next-generation sequencing
        2.1.2 DNA and RNA data analysis
        2.1.3 Analysis pipelines for the NGS data
    2.2 Next Generation Sequencing technology applications in plants
        2.2.1 Techniques for SNP identification in plants
        2.2.2 The applications of SNPs in plants
        2.2.3 Plant comparative transcriptomics
3 Rice Genomic Variant Analysis platform
    3.1 Introduction
    3.2 Materials and Methods
        3.2.1 Data collection
        3.2.2 Implementation
        3.2.3 NGS Snakemake workflow
        3.2.4 Equipment
    3.3 Results
        3.3.1 Running genomic variant analysis pipeline
        3.3.2 Output Files
    3.4 Discussion
        3.4.1 The necessity of developing a new genomic variant analysis pipeline
        3.4.2 Rice genomic variant analysis pipeline features
    3.5 Conclusion
4 Rice Stress-Resistant SNP database
    4.1 Introduction
    4.2 Materials and Methods
        4.2.1 Data collection and organization
        4.2.2 SNP Calling and Annotation
        4.2.3 Identification of Stress-Resistant SNPs in Rice
        4.2.4 Database Design and Web-Application Architecture
    4.3 Results and Discussion
        4.3.1 Database Content
        4.3.2 Phenotype Data
        4.3.3 SNP Data
        4.3.4 Biotic Stress-Resistant SNPs
        4.3.5 Abiotic Stress-Resistant SNPs
        4.3.6 Web Interface
    4.4 Conclusion
5 Genome-wide identification of blast resistant SNPs and genes in rice(Oryza sativa L.)
    5.1 Introduction
    5.2 Materials and Methods
        5.2.1 Data Collection
        5.2.2 Detection of Variant in different rice varieties
        5.2.3 Functional classification of SNPs
        5.2.4 Identification of blast-resistant candidate SNPs from blast resistant rice varieties
        5.2.5 Identification of blast-responsive genes in rice using RNA-seq data
        5.2.6 Assessment of deleterious SNPs
        5.2.7 Detailed annotation of identified blast-resistant candidate genes
    5.3 Results
        5.3.1 Genome assembly of different rice varieties
        5.3.2 Variant detection from the different rice variety genomes
        5.3.3 Classification of the blast-resistant candidate SNPs
        5.3.4 Identification of blast-resistant candidate genes in rice
        5.3.5 GO enrichment analysis of blast-resistant candidate genes
        5.3.6 Analysis on the expression of blast-resistant candidate genes in rice under the infection of blast fungus
        5.3.7 In-silico prediction of deleterious ns SNPs in the blast-resistant candidate genes
        5.3.8 Prediction of the impact of deleterious ns SNPs on the function and structure of the blast-resistant candidate genes
    5.4 Discussion
        5.4.1 Mining of blast-resistant candidate ns SNPs and genes in rice
        5.4.2 Analysis on the function and expression of blast-resistant candidate genes
        5.4.3 Analysis of the impact of blast-resistant candidate ns SNPs on their target genes
    5.5 Conclusion
Appendix
    A-1 List of Abiotic and Biotic stress responsive rice varieties
    A-2 Snakemake workflow
    A-3 List of Abiotic and Biotic Resistant Genes in rice
    A-4 List of ns SNPs in up-regulated genes predicted as deleterious using SIFT and Poly Phen-2 SNP prediction tools
    A-5 Differential expression fold change of genes associated with the deleterious nsSNPs
    A-6 Functional annotation of up-regulated genes having deleterious ns SNPs
References
Published Papers and other Academic Achievements during the Program
Acknowledgements



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