Plant Omics: Advances in Big Data Biology

Description

This book provides a comprehensive overview of plant omics and big data in the fields of plant and crop biology. It discusses each omics layer individually, including genomics, transcriptomics, proteomics, and covers model and non-model species. In a section on advanced topics, it considers developments in each specialized domain, including genome editing and enhanced breeding strategies (such as genomic selection and high-throughput phenotyping), with the aim of providing tools to help tackle global food security issues. The importance of online resources in big data biology are highlighted in a section summarizing both wet- and dry-biological portals. This section introduces biological resources, datasets, online bioinformatics tools and approaches that are in the public domain. This title: reviews each omics layer individually;focuses on new advanced research domains and technology; andsummarizes publicly available experimental and informatics resources.This book is for students, engineers, researchers, and academics in plant biology, genetics, biotechnology, and bioinformatics. Plant Omics CABI BIOTECHNOLOGY SERIES Copyright Contents Contributors Preface 1 Plant Genomics 1.1 Introduction 1.2 Advanced Technologies in Plant Genomics 1.3 Status of Fabaceae Genomics 1.4 Status of Poaceae Genomics 1.5 Conclusion References 2 Plant Transcriptomics: Data--driven Global Approach to Understand Cellular Processes and Their Reg 2.1 Introduction 2.2 Overview of RNA-Seq-Based Transcriptome Profiling 2.2.1 Phase-IA: Sampling time-point, replication, and depth of coverage 2.2.2 Phase-IB: Single or paired-end sequence reads - platform and error rate 2.2.3 Phase-II: Factors in processing of sequence reads and their limitations 2.2.4 Phase III: Choosing the reference and mapping in model plant species 2.2.5 Phase III: De novo or hybrid assembly for non-model species 2.2.6 Phase III: Choice of aligner 2.2.7 Phase IV: Detection of differentially expressed transcripts and their gene loci 2.3 Conclusions and Perspectives Acknowledgment References 3 Plant Proteomics 3.1 Introduction 3.2 Proteomic Technology in Plant Science 3.3 Plant-subcellular Proteomics 3.3.1 Importance of plant-subcellular proteomics 3.3.2 Subcellular proteomics: understanding mechanism in soybean under flooding stress 3.4 Plant Proteomics of Post-translational Modifications 3.4.1 Importance of post-translational modifications in plants 3.4.2 Post-translational modifications: understanding mechanism in soybean under flooding stress 3.5 Plant Proteomics: Understanding Environmental Stress Responses 3.5.1 Plant proteomics: understanding interaction between plants and biotic stress 3.5.2 Plant proteomics: understanding signaling mechanism under abiotic stresses 3.6 Future Perspective References 4 Plant Metabolomics: The Great Potential of Plant Metabolomics in Big Data Biology 4.1 Introduction 4.2 Analytical Targets and Techniques 4.2.1 Analytical targets in plant metabolomics 4.2.1.1 Central metabolites 4.2.1.2 Secondary metabolites 4.2.2 Analytical methods for plant metabolomics 4.2.3 Metabolite identification/annotation in metabolomics data 4.3 The Importance of Sharing Metabolomics Data 4.3.1 Metabolome data repositories 4.3.2 Toward reproducible metabolome data analysis 4.3.3 Future metabolomics data analysis enhancing new biological discoveries 4.4 Conclusions and Outlook References 5 Plant Phenomics 5.1 Introduction to Plant Phenomics 5.2 Basic Technologies for Plant Phenotyping 5.3 Indoor Phenotyping 5.3.1 Indoor phenotyping platforms 5.3.1.1 Laboratory or growth chamber 5.3.1.2 Greenhouse 5.3.2 Limitations of the current indoor phenotyping platforms 5.4 Field Phenotyping 5.4.1 Field phenotyping platforms 5.4.1.1 Satellites 5.4.1.2 UAVs 5.4.1.3 Ground-based platforms 5.4.2 Limitations of the current field phenotyping platforms 5.5 Conclusion and Future Perspectives References 6 Plant Non--coding Transcriptomics: Overview of lncRNAs in Abiotic Stress Responses 6.1 Introduction 6.2 History of ncRNA Research 6.3 Classification of ncRNAs 6.4 Molecular Functions of ncRNA 6.4.1 miRNAs 6.4.2 Trans-acting siRNAs (ta-siRNAs) and phased siRNAs (pha-siRNAs) 6.4.3 Pol IV- and Pol V-derived lncRNAs and siRNAs 6.4.4 RNA interfering events induced by cis-natural antisense RNAs (cis-NATs) 6.4.5 Cis-NATs enhance mRNA translation 6.4.6 Cis-NATs derived from RNA degradation 6.4.7 lncRNAs COLDAIR, COOLAIR, and COLDWRAP that regulate chromatin modification at the FLC locus 6.4.8 ENOD40 and ASCO, mRNA-like long intergenic ncRNAs that regulate alternative splicing events by 6.4.9 APOLO and HID1, long intergenic ncRNAs forming RNA-DNA hybrids that repress gene expression 6.4.10 ceRNA/RNA Soggy/RNA decoy mimic miRNA targets 6.4.11 Circular RNA 6.4.12 RNA polymerase III-derived lncRNAs 6.4.13 Viroids: sub-viral plant-pathogenic lncRNAs 6.5 Concluding Remarks Acknowledgments References 7 Plant Epigenomics 7.1 Significance of Histone Modifications 7.1.1 Histone proteins in plants 7.1.2 Functions of conservative modification sites in canonical histone proteins 7.1.3 The genome-wide distribution and responsiveness of major histone modifications 7.1.4 Histones and histone modifications in the construction of genomes and chromosome structures 7.2 DNA Methylation 7.2.1 DNA methylation in plants 7.2.2 DNA methylation mechanism in A. thaliana 7.2.3 Genome-wide DNA methylation patterns in plant genomes 7.2.4 Methods to investigate global DNA methylation patterns References 8 Plant Organellar Omics 8.1 Introduction 8.2 Nucleus 8.3 Endoplasmic Reticulum 8.4 Golgi Apparatus 8.5 Vacuole 8.6 Peroxisome 8.7 Oil Body 8.8 Plastid 8.9 Mitochondrion 8.10 Databases for Images/Movies of Organelle Dynamics 8.11 Conclusions Acknowledgment References 9 Plant Cis--elements and Transcription Factors 9.1 Introduction 9.2 Methods to Infer TF-DNA Interactions 9.2.1 Wet-lab approaches 9.2.2 Dry-lab approaches 9.3 Related Databases for TFs and Cis-elements 9.3.1 TF-related databases 9.3.2 Cis-element-related databases 9.4 Advanced Analysis in GRNs 9.5 Prospective View on Studies of Gene Regulation References 10 Plant Gene Expression Network 10.1 Introduction 10.2 Visualization of Relationships of Genes by GENs: Nodes and Edges 10.3 Types of Relationships in GENs 10.4 Similarity and/or Reciprocity in Gene Expression Profiles 10.4.1 PCC 10.4.2 DCA 10.5 Common Regulatory Mechanisms in Gene Expressions 10.6 Sequence Similarities in mRNAs 10.7 Similarities in the Biological Functions of Expressed Genes 10.7.1 GENs with computational annotations of genes 10.7.2 GENs containing knowledge-based information and ontology for biological functions 10.7.3 GENs with metabolic pathway information 10.8 Network Construction Tools with Multiple Types of Information about Genes 10.9 Knowledge-bases for RNA-Seq Data, Expression Data, and GENs Acknowledgments References 11 Plant Hormones: Gene Family Organization and Homolog Interactions of Genes for Gibberellin Metabo 11.1 Plant Hormones and Height Control 11.2 Brassica napus 11.3 GAs 11.3.1 GA metabolism 11.3.2 GA signaling 11.3.3 GA-auxotroph and response mutants 11.4 GA Metabolism and Signaling Genes in B. napus: Gene Family Diversity and Gene Expression 11.4.1 Early GA biosynthesis (synthesis of GA12) 11.4.2 BnaGA20ox 11.4.3 BnaGA3ox 11.4.4 BnaGA2ox 11.4.5 GA signaling genes 11.5 Expression of Homeologous Genes 11.6 General Discussion Acknowledgments References 12 Plant-Pathogen Interaction: New Era of Plant-Pathogen Interaction Studies: “Omics” Perspectives 12.1 Introduction 12.2 Overview of Plant Defense against Pathogens 12.3 Transcriptome of Plant and Pathogen Interactions: Providing a Global Understanding of the Host- 12.4 Proteomics and Plant-Pathogen Interactome: Network Analysis 12.5 NLRome Provides a Comprehensive Way to Study NLRs 12.6 NLR and Avr Interaction Could Be Divided into Three Patterns 12.7 NLRs Function in Singleton, Pair, or Network 12.8 Pan-NLRome Reveals Diversity of NLRs 12.9 Concluding Remarks Acknowledgments References 13 Plant GWAS 13.1 Introduction 13.2 Core Processes in GWAS 13.2.1 Associating genotypic variations with phenotypic variations 13.2.2 Preparing GWAS populations 13.2.3 Checking phenotype data 13.2.4 Mixed linear model 13.2.5 Analyzing statistical significance 13.2.6 GWAS software 13.3 Graphical Representation of GWAS Results 13.3.1 Manhattan plot 13.3.2 Quantile-quantile (QQ) plot 13.4 Case Studies 13.4.1 Arabidopsis 13.4.2 Rice 13.5 Problems with GWAS 13.5.1 Functional validation of GWAS results 13.5.2 Spurious association, rare alleles 13.6 Conclusion and Prospects References 14 Plant Genomic Selection: a Concept That Uses Genomics Data in Plant Breeding 14.1 Introduction 14.2 Core Processes in GS 14.2.1 Preparation of training data 14.2.2 Construction of GS model 14.3 Implementation of GS in Practical Plant Breeding 14.4 Advanced Topics in GS 14.4.1 GS model incorporating G × E effects 14.4.2 DNA marker selection for GS model construction 14.4.3 Combination with other omics 14.5 Concluding Remarks References 15 Plant Genome Editing 15.1 Introduction 15.2 Genome Editing Using CRISPR-Cas9 in Plants: an Overview 15.3 Genome Manipulation Using a CRISPR-dCas9-based System Without DSB Induction 15.4 Engineered Cas9 and Newly Discovered Cas Proteins for Plant Genome Editing 15.5 Prime Editing 15.6 Conclusions References 16 Introduction of Deep Learning Approaches in Plant Omics Research 16.1 Introduction 16.2 Supervised Learning 16.2.1 Classification task: CNN 16.2.2 Regression task: RNN, LSTM 16.3 Unsupervised Learning 16.3.1 Generation task: GAN 16.3.2 Dimensionality reduction task: AE, word2vec 16.4 Deep Reinforcement Learning: DQN 16.5 Other Deep Learning Techniques: GNN, Transformer, AutoML 16.5.1 Deep learning for graphs: GNN 16.5.2 Natural language processing: transformer 16.5.3 Automatic machine learning: AutoML 16.6 Summary References 17 Deep Learning on Images and Genetic Sequences in Plants: Classifications and Regressions 17.1 Introduction 17.2 Deep Learning for Plant Images 17.2.1 Deep learning for taxonomic classification of plant images 17.2.2 Deep learning for stress/disease diagnosis based on plant images 17.2.3 Deep learning for non-invasive prediction of plant images 17.2.4 Deep learning for regression and quantification of plant images 17.2.5 Deep learning for automated sorting of plant images 17.3 Deep Learning for DNA Sequences 17.4 Deep Learning for Amino Acid Sequences: Prediction of Protein Folding 17.5 CNN Guides for Beginners: Tips and Precautions in Practice 17.5.1 Installing libraries and preparing data for application of a CNN 17.5.2 Evaluation of CNN model performance 17.5.3 Interpretability and explainability of CNN models 17.6 Future Perspectives Acknowledgments References 18 Deep Learning in Plant Omics: Object Detection and Image Segmentation 18.1 Introduction 18.2 Object Detection and Image Segmentation in Plant Phenomics 18.2.1 Object detection and its applications 18.2.2 Image segmentation and its applications 18.3 Current Challenges of Object Detection and Image Segmentation for Plant Phenomics 18.3.1 Data annotation cost 18.3.2 Generalization capability of current deep learning models 18.4 Conclusion and Future Perspective References 19 Plant Experimental Resources 19.1 Introduction 19.2 Overview of Arabidopsis Resources 19.2.1 Arabidopsis seed resources for omics analysis 19.2.2 Arabidopsis DNA resources 19.3 Overview of Experimental Plant Resources for Crop Research 19.3.1 Rice resources 19.3.2 Wheat resources 19.3.3 Tomato resources 19.3.4 Legume resources 19.4 Conclusion and Perspective References 20 Plant Omics Databases: an Online Resource Guide 20.1 Introduction 20.2 Arabidopsis Omics Databases 20.2.1 Arabidopsis genome databases 20.2.2 Arabidopsis epigenome databases 20.2.3 Arabidopsis transcriptome databases 20.2.4 Arabidopsis proteome databases 20.3 Omics Databases for Crop Plants 20.3.1 Rice (Oryza sativa L.) 20.3.2 Wheat (Triticum aestivum L.) 20.3.3 Maize (Zea mays) 20.3.4 Soybean (Glycine max) 20.3.5 Tomato (Solanum lycopersicum) 20.3.6 Pepper (Capsicum annuum) 20.4 Databases for Bryophytes 20.5 Databases for Other Plant Species 20.6 Portals for Plant Omics Databases 20.6.1 Bio-Analytic Resource for Plant Biology (BAR) 20.6.2 Gramene 20.6.3 Phytozome 20.7 Future Perspectives References Index