Plant omics: advances in big data biology
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Wallingford, Oxfordshire ; Boston
CABI
2023
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| Schriftenreihe: | CABI biotechnology series
11 |
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| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xviii, 290 Seiten Illustrationen, Diagramme |
| ISBN: | 9781789247510 |
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| 245 | 1 | 0 | |a Plant omics |b advances in big data biology |c edited by: Hajime Ohyanagi, Eiji Yamamoto, Ai Kitazumi, Kentaro Yano |
| 264 | 1 | |a Wallingford, Oxfordshire ; Boston |b CABI |c 2023 | |
| 300 | |a xviii, 290 Seiten |b Illustrationen, Diagramme | ||
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Contents Contributors xiii Preface xvii PART I: Baseline Knowledge 1. Plant Genomics Masaru Bamba. Kenta Shirasawa, Sachiko Isobe, Nadia Kamal. Klaus Mayer and Shusei Sato 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 2. Plant Transcriptomics: Data-driven Global Approach to Understand Cellular Processes and Their Regulation in Model and Non-Model Plants Ai Kitazumi. Isaiah C.M. Pabuayon, Kevin R. Cushman, Kentaro Yano and Benildo G. de los Reyes 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-Ш: 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 1 1 1 3 4 6 10 10 11 13 15 17 19 20 23 24 25
Contents vi 3. Plant Proteomics Setsuko Komatsu and Ghazala Mustafa 3.1. Introduction 3.2. Proteomic Technology in Plant Science 3.3. Plant-subcellular Proteomics 3.3.1. Importanceof 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 plant and biotic stress 3.5.2. Plant proteomics: understanding signaling mechanism under abiotic stresses 3.6. Future Perspective 30 30 31 32 32 38 39 39 40 41 41 42 43 4. Plant Metabolomics: The Great Potential of Plant Metabolomics in Big Data Biology 50 Miyako Kusano and Atsushi Fukushima 4.1. Introduction 50 4.2. Analytical Targets and Techniques 4.2.1 Analytical targets in plant metabolomics 4.2.2. Analytical methods for plant metabolomics 52 52 55 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. Towards reproducible metabolome data analysis 56 56 57 57 4.3.3. Future metabolomics data analysis enhancing new biological discoveries 4.4. Conclusions and Outlook 60 60 5. Plant Phenomics 67 Wei Guo andjiangsan Zhao 5.1. Introduction to Plant Phenomics 67 5.2. Basic Technologies for Plant Phenotyping 5.3. Indoor Phenotyping 5.3.1.
Indoor phenotyping platforms 68 69 69 5.3.2. Limitations of the current indoor phenotyping platforms 5.4. Field Phenotyping 70 71 5.4.1. Field phenotyping platforms 5.4.2. Limitations of the current field phenotyping platforms 5.5. Conclusion and Future Perspectives 71 72 73
Contents vii 6. Plant Non-coding Transcriptomics: Overview of IncRNAs in Abiotic Stress Responses Akihiro Matsui and Motoaki Seki 6.1. Introduction 6.2. History of ncRNA Research 6.3. Classification of ncRNAs 6.4. Molecular Functions of ncRNA 6.4.1. MicroRNAs 6.4.2. Trans-acting siRNAs (ta-siRNAs) and phased siRNAs (pha-siRNAs) 6.4.3. Pol IV- and Pol V-derived IncRNAs 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. IncRNAs COLDAIR, COOLAIR, and COLDWRAP that regulate chromatin modification at the FLC locus 6.4.8. EN0D40 and ASCO, mRNA-like long intergenic ncRNAs that regulate alternative splicing events by interacting with RNA-binding protein 6.4.9. APOLO and HID1. long intergenic ncRNAs forming RNA-DNA hybrids that repress gene expression 79 79 82 82 83 83 83 84 85 85 86 86 87 87 6.4.10. ceRNA/RNA Soggy/RNA decoy mimic miRNA targets 88 6.4.11. CircularRNA 6.4.12. RNA polymerase Ш-derived IncRNAs 6.4.13. Viroids: sub-viral plant-pathogenic IncRNAs 6.5. Concluding Remarks 88 89 89 89 7. Plant Epigenomics Taiko Kim To and Jong-Myong Kim 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 8. Plant Organellar Omics Masatake Kanai, Kentaro Tamura, Katarzyna Tarnawska-Glatt, Shino Goto-Yamada, Kenji Yamada and Shoji Mano 8.1. Introduction 8.2. Nucleus 8.3. Endoplasmic Reticulum 97 97 98 98 99 100 100 100 101 102 103 108 108 108 110
Contents 8.4. Golgi Apparatus 114 8.5. Vacuole 111 8.6. Peroxisome 8.7. Oil body 8.8. Plastid 8.9. Mitochondrion 112 8.10. Databases for Images/Movies of Organelle Dynamics 115 113 114 H4 8.11. Conclusions PART II: Advanced Topics 9. Plant Cis-elements and Transcription Factors Chi-Nga Chow, Kuan-Chieh Tseng and Wen-Chi Chang 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 forTFs 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 10. Plant Gene Expression Network 124 124 125 125 127 129 129 129 130 132 137 Miyu Asari, Ai Kitazumi, Eiji Nambara, Benildo G. de los Reyes and Kentaro Yano 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 11. Plant Hormones: Gene Family Organization and Homolog Interactions of Genes for Gibberellin
Metabolism and Signaling in Allotetraploid Brassica napus 137 138 138 139 140 141 143 143 144 144 145 146 146 147 151 Eiji Nambara, Dawei Yan, Jing Wen, Arjun Sharma. Frederik Nguyen. Ange Yan. Karin Uruma and Kentaro Yano 11.1. Plant Hormones and Height Control 152
Contents ix 11.2. Brassica napus 11.3. GAs 11.3.1. G A metabolism 11.3.2. GA signaling 152 153 153 155 11.3.3. GA-auxotroph and response mutants 11.4. GA Metabolism and Signaling Genes in B. napus: Gene Family Diversity and Gene Expression 155 156 11.4.1. Early GA biosynthesis (synthesis of GA12) 11.4.2. BnaGA20ox 11.4.3. BnaGAlox 11.4.4. BnaGA2ox 11.4.5. GA signaling genes 11.5. Expression of Homeologous Genes 11.6. General Discussion 12. Plant-Pathogen Interaction: New Era of Plant-Pathogen Interaction Studies: “Omics” Perspectives Shu an Zheng and Ryohei Terauchi 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-Pathogen Interplay 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 13. Plant GWAS Matthew Shenton 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. GWASsoftware 13.3. Graphical Representation of GWAS Results 13.3.1. Manhattan plot 13.3.2. Quantile-quantile (QQ) plot 156 160 162 162 163 164 ] 66 172 172 172 173 174 175 175 176 176 176 181 181 182 182 182 182 183 183 184 184 184 184
13.4. Case Studies 13.4.1. Arabidopsis 185 185 13.4.2. Rice 13.5. Problems with GWAS 186 186
Contents 13.5.1. Functional validation of G WAS results 13.5.2. Spurious association, rare alleles 13.6. Conclusion and Prospects 14. Plant Genomic Selection: a Concept That Uses Genomics Data in Plant Breeding 18 6 187 187 190 Eiji Yamamoto 14.1. Introduction 14.2. Core Processes in GS 14.2.1. Preparation of training data 14.2.2. Construction of GS model 190 192 193 193 14.3. Implementation of GS in Practical Plant Breeding 14.4. Advanced Topics in GS 14.4.1 GS model incorporating G x E effects 14.4.2 DNA marker selection for GS model construction 19 7 198 198 199 14.4.3 Combination with other omics 14.5. Concluding Remarks 199 199 15. Plant Genome Editing 205 Naoki Wada. Yuriko Osakabe and Keishi Osakabe 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 16. Introduction of Deep Learning Approaches in Plant Omics Research 205 207 207 209 212 212 217 Eli Kaminuma 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 217 217 218 218 219 219 16.3.2. Dimensionality reduction task: AE. Word2vec 16.4. Deep Reinforcement Learning: DON 219 219 16.5. Other Deep Learning Techniques: GNN. Transformer, AutoML 16.5.1. Deep learning for graphs: GNN 220 220 16.5.2. Natural language processing: Transformer 16.5.3. Automatic machine learning:
AutoML 16.6. Summary 220 220 221 17. Deep Learning on Images and Genetic Sequences in Plants: Classifications and Regressions Kanae Masuda and Takashi Akagi 17.1.I ntroduction 224 224
Contents 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 18. Deep Learning in Plant Omics: Object Detection and Image Segmentation Wei Guo and Akshay L. Chandra 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 xi 225 225 226 226 227 227 228 229 229 229 230 230 231 234 234 236 236 236 238 238 238 239 Pz\RT III: Resources 19. Plant Experimental Resources 246 Masatomo Kobayashi 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 246 247 247 248 248 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 248 249 250 250 250 20. Plant Omics Databases: an Online Resource Guide Feng Li, Yingtian Deng, Eiji Yamamoto and Zhenya Liu 20.1. Introduction 20.2. Arabidopsis Omics Databases 20.2.1. Arabidopsis genome databases 20.2.2. Arabidopsis epigenome databases 253 253 254 2 54 255
xii Contents 20.2.3. Arabidopsis transcriptome databases 255 20.2.4. Arabidopsis proteome databases 20.3. Omics Databases for Crop Piants 20.3.1. Rice (Oryzasativa 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 256 256 256 258 259 259 259 260 2 60 261 20.6. Portals for Plant Omics Databases 261 20.6.1. Bio-Analytic Resource for Plant Biology (BAR) 20.6.2. Gramene 20.6.3. Phytozome 20.7. Future Perspectives 261 261 261 263 Index 271 |
| adam_txt |
Contents Contributors xiii Preface xvii PART I: Baseline Knowledge 1. Plant Genomics Masaru Bamba. Kenta Shirasawa, Sachiko Isobe, Nadia Kamal. Klaus Mayer and Shusei Sato 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 2. Plant Transcriptomics: Data-driven Global Approach to Understand Cellular Processes and Their Regulation in Model and Non-Model Plants Ai Kitazumi. Isaiah C.M. Pabuayon, Kevin R. Cushman, Kentaro Yano and Benildo G. de los Reyes 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-Ш: 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 1 1 1 3 4 6 10 10 11 13 15 17 19 20 23 24 25
Contents vi 3. Plant Proteomics Setsuko Komatsu and Ghazala Mustafa 3.1. Introduction 3.2. Proteomic Technology in Plant Science 3.3. Plant-subcellular Proteomics 3.3.1. Importanceof 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 plant and biotic stress 3.5.2. Plant proteomics: understanding signaling mechanism under abiotic stresses 3.6. Future Perspective 30 30 31 32 32 38 39 39 40 41 41 42 43 4. Plant Metabolomics: The Great Potential of Plant Metabolomics in Big Data Biology 50 Miyako Kusano and Atsushi Fukushima 4.1. Introduction 50 4.2. Analytical Targets and Techniques 4.2.1 Analytical targets in plant metabolomics 4.2.2. Analytical methods for plant metabolomics 52 52 55 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. Towards reproducible metabolome data analysis 56 56 57 57 4.3.3. Future metabolomics data analysis enhancing new biological discoveries 4.4. Conclusions and Outlook 60 60 5. Plant Phenomics 67 Wei Guo andjiangsan Zhao 5.1. Introduction to Plant Phenomics 67 5.2. Basic Technologies for Plant Phenotyping 5.3. Indoor Phenotyping 5.3.1.
Indoor phenotyping platforms 68 69 69 5.3.2. Limitations of the current indoor phenotyping platforms 5.4. Field Phenotyping 70 71 5.4.1. Field phenotyping platforms 5.4.2. Limitations of the current field phenotyping platforms 5.5. Conclusion and Future Perspectives 71 72 73
Contents vii 6. Plant Non-coding Transcriptomics: Overview of IncRNAs in Abiotic Stress Responses Akihiro Matsui and Motoaki Seki 6.1. Introduction 6.2. History of ncRNA Research 6.3. Classification of ncRNAs 6.4. Molecular Functions of ncRNA 6.4.1. MicroRNAs 6.4.2. Trans-acting siRNAs (ta-siRNAs) and phased siRNAs (pha-siRNAs) 6.4.3. Pol IV- and Pol V-derived IncRNAs 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. IncRNAs COLDAIR, COOLAIR, and COLDWRAP that regulate chromatin modification at the FLC locus 6.4.8. EN0D40 and ASCO, mRNA-like long intergenic ncRNAs that regulate alternative splicing events by interacting with RNA-binding protein 6.4.9. APOLO and HID1. long intergenic ncRNAs forming RNA-DNA hybrids that repress gene expression 79 79 82 82 83 83 83 84 85 85 86 86 87 87 6.4.10. ceRNA/RNA Soggy/RNA decoy mimic miRNA targets 88 6.4.11. CircularRNA 6.4.12. RNA polymerase Ш-derived IncRNAs 6.4.13. Viroids: sub-viral plant-pathogenic IncRNAs 6.5. Concluding Remarks 88 89 89 89 7. Plant Epigenomics Taiko Kim To and Jong-Myong Kim 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 8. Plant Organellar Omics Masatake Kanai, Kentaro Tamura, Katarzyna Tarnawska-Glatt, Shino Goto-Yamada, Kenji Yamada and Shoji Mano 8.1. Introduction 8.2. Nucleus 8.3. Endoplasmic Reticulum 97 97 98 98 99 100 100 100 101 102 103 108 108 108 110
Contents 8.4. Golgi Apparatus 114 8.5. Vacuole 111 8.6. Peroxisome 8.7. Oil body 8.8. Plastid 8.9. Mitochondrion 112 8.10. Databases for Images/Movies of Organelle Dynamics 115 113 114 H4 8.11. Conclusions PART II: Advanced Topics 9. Plant Cis-elements and Transcription Factors Chi-Nga Chow, Kuan-Chieh Tseng and Wen-Chi Chang 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 forTFs 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 10. Plant Gene Expression Network 124 124 125 125 127 129 129 129 130 132 137 Miyu Asari, Ai Kitazumi, Eiji Nambara, Benildo G. de los Reyes and Kentaro Yano 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 11. Plant Hormones: Gene Family Organization and Homolog Interactions of Genes for Gibberellin
Metabolism and Signaling in Allotetraploid Brassica napus 137 138 138 139 140 141 143 143 144 144 145 146 146 147 151 Eiji Nambara, Dawei Yan, Jing Wen, Arjun Sharma. Frederik Nguyen. Ange Yan. Karin Uruma and Kentaro Yano 11.1. Plant Hormones and Height Control 152
Contents ix 11.2. Brassica napus 11.3. GAs 11.3.1. G A metabolism 11.3.2. GA signaling 152 153 153 155 11.3.3. GA-auxotroph and response mutants 11.4. GA Metabolism and Signaling Genes in B. napus: Gene Family Diversity and Gene Expression 155 156 11.4.1. Early GA biosynthesis (synthesis of GA12) 11.4.2. BnaGA20ox 11.4.3. BnaGAlox 11.4.4. BnaGA2ox 11.4.5. GA signaling genes 11.5. Expression of Homeologous Genes 11.6. General Discussion 12. Plant-Pathogen Interaction: New Era of Plant-Pathogen Interaction Studies: “Omics” Perspectives Shu an Zheng and Ryohei Terauchi 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-Pathogen Interplay 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 13. Plant GWAS Matthew Shenton 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. GWASsoftware 13.3. Graphical Representation of GWAS Results 13.3.1. Manhattan plot 13.3.2. Quantile-quantile (QQ) plot 156 160 162 162 163 164 ] 66 172 172 172 173 174 175 175 176 176 176 181 181 182 182 182 182 183 183 184 184 184 184
13.4. Case Studies 13.4.1. Arabidopsis 185 185 13.4.2. Rice 13.5. Problems with GWAS 186 186
Contents 13.5.1. Functional validation of G WAS results 13.5.2. Spurious association, rare alleles 13.6. Conclusion and Prospects 14. Plant Genomic Selection: a Concept That Uses Genomics Data in Plant Breeding 18 6 187 187 190 Eiji Yamamoto 14.1. Introduction 14.2. Core Processes in GS 14.2.1. Preparation of training data 14.2.2. Construction of GS model 190 192 193 193 14.3. Implementation of GS in Practical Plant Breeding 14.4. Advanced Topics in GS 14.4.1 GS model incorporating G x E effects 14.4.2 DNA marker selection for GS model construction 19 7 198 198 199 14.4.3 Combination with other omics 14.5. Concluding Remarks 199 199 15. Plant Genome Editing 205 Naoki Wada. Yuriko Osakabe and Keishi Osakabe 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 16. Introduction of Deep Learning Approaches in Plant Omics Research 205 207 207 209 212 212 217 Eli Kaminuma 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 217 217 218 218 219 219 16.3.2. Dimensionality reduction task: AE. Word2vec 16.4. Deep Reinforcement Learning: DON 219 219 16.5. Other Deep Learning Techniques: GNN. Transformer, AutoML 16.5.1. Deep learning for graphs: GNN 220 220 16.5.2. Natural language processing: Transformer 16.5.3. Automatic machine learning:
AutoML 16.6. Summary 220 220 221 17. Deep Learning on Images and Genetic Sequences in Plants: Classifications and Regressions Kanae Masuda and Takashi Akagi 17.1.I ntroduction 224 224
Contents 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 18. Deep Learning in Plant Omics: Object Detection and Image Segmentation Wei Guo and Akshay L. Chandra 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 xi 225 225 226 226 227 227 228 229 229 229 230 230 231 234 234 236 236 236 238 238 238 239 Pz\RT III: Resources 19. Plant Experimental Resources 246 Masatomo Kobayashi 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 246 247 247 248 248 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 248 249 250 250 250 20. Plant Omics Databases: an Online Resource Guide Feng Li, Yingtian Deng, Eiji Yamamoto and Zhenya Liu 20.1. Introduction 20.2. Arabidopsis Omics Databases 20.2.1. Arabidopsis genome databases 20.2.2. Arabidopsis epigenome databases 253 253 254 2 54 255
xii Contents 20.2.3. Arabidopsis transcriptome databases 255 20.2.4. Arabidopsis proteome databases 20.3. Omics Databases for Crop Piants 20.3.1. Rice (Oryzasativa 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 256 256 256 258 259 259 259 260 2 60 261 20.6. Portals for Plant Omics Databases 261 20.6.1. Bio-Analytic Resource for Plant Biology (BAR) 20.6.2. Gramene 20.6.3. Phytozome 20.7. Future Perspectives 261 261 261 263 Index 271 |
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| id | DE-604.BV047874164 |
| illustrated | Illustrated |
| index_date | 2024-07-03T19:20:48Z |
| indexdate | 2024-07-20T06:44:05Z |
| institution | BVB |
| isbn | 9781789247510 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033256623 |
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| physical | xviii, 290 Seiten Illustrationen, Diagramme |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | CABI |
| record_format | marc |
| series | CABI biotechnology series |
| series2 | CABI biotechnology series |
| spelling | Plant omics advances in big data biology edited by: Hajime Ohyanagi, Eiji Yamamoto, Ai Kitazumi, Kentaro Yano Wallingford, Oxfordshire ; Boston CABI 2023 xviii, 290 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier CABI biotechnology series 11 Biotechnologie (DE-588)4069491-4 gnd rswk-swf Pflanzen (DE-588)4045539-7 gnd rswk-swf Cytologie (DE-588)4070177-3 gnd rswk-swf Omics-Technologie (DE-588)1204156549 gnd rswk-swf Pflanzen (DE-588)4045539-7 s Omics-Technologie (DE-588)1204156549 s Biotechnologie (DE-588)4069491-4 s Cytologie (DE-588)4070177-3 s DE-604 Ohyanagi, Hajime edt Yamamoto, Eiji edt Kitazumi, Ai edt Yano, Kentaro edt Erscheint auch als Online-Ausgabe, PDF 978-1-78924-752-7 Erscheint auch als Online-Ausgabe, EPUB 978-1-78924-753-4 CABI biotechnology series 11 (DE-604)BV041578879 11 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033256623&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Plant omics advances in big data biology CABI biotechnology series Biotechnologie (DE-588)4069491-4 gnd Pflanzen (DE-588)4045539-7 gnd Cytologie (DE-588)4070177-3 gnd Omics-Technologie (DE-588)1204156549 gnd |
| subject_GND | (DE-588)4069491-4 (DE-588)4045539-7 (DE-588)4070177-3 (DE-588)1204156549 |
| title | Plant omics advances in big data biology |
| title_auth | Plant omics advances in big data biology |
| title_exact_search | Plant omics advances in big data biology |
| title_exact_search_txtP | Plant omics advances in big data biology |
| title_full | Plant omics advances in big data biology edited by: Hajime Ohyanagi, Eiji Yamamoto, Ai Kitazumi, Kentaro Yano |
| title_fullStr | Plant omics advances in big data biology edited by: Hajime Ohyanagi, Eiji Yamamoto, Ai Kitazumi, Kentaro Yano |
| title_full_unstemmed | Plant omics advances in big data biology edited by: Hajime Ohyanagi, Eiji Yamamoto, Ai Kitazumi, Kentaro Yano |
| title_short | Plant omics |
| title_sort | plant omics advances in big data biology |
| title_sub | advances in big data biology |
| topic | Biotechnologie (DE-588)4069491-4 gnd Pflanzen (DE-588)4045539-7 gnd Cytologie (DE-588)4070177-3 gnd Omics-Technologie (DE-588)1204156549 gnd |
| topic_facet | Biotechnologie Pflanzen Cytologie Omics-Technologie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033256623&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV041578879 |
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