Plant Omics: Advances in Big Data Biology

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