Bioinformatics in Agriculture : Next Generation Sequencing Era

Bioinformatics in Agriculture: Next Generation Sequencing Era is a comprehensive volume presenting an integrated research and development approach to the practical application of genomics to improve agricultural crops. Exploring both the theoretical and applied aspects of computational biology, and focusing on the innovation processes, the book highlights the increased productivity of a translational approach. Presented in four sections and including insights from experts from around the world, the book includes: Section I: Bioinformatics and Next Generation Sequencing Technologies; Section II: Omics Application; Section III: Data mining and Markers Discovery; Section IV: Artificial Intelligence and Agribots. Bioinformatics in Agriculture: Next Generation Sequencing Era explores deep sequencing, NGS, genomic, transcriptome analysis and multiplexing, highlighting practices forreducing time, cost, and effort for the analysis of gene as they are pooled, and sequenced. Readers will gain real-world information on computational biology, genomics, applied data mining, machine learning, and artificial intelligence. This book serves as a complete package for advanced undergraduate students, researchers, and scientists with an interest in bioinformatics. Discusses integral aspects of molecular biology and pivotal tool sfor molecular breeding Enables breeders to design cost-effective and efficient breeding strategies Provides examples ofinnovative genome-wide marker (SSR, SNP) discovery Explores both the theoretical and practical aspects of computational biology with focus on innovation processes Covers recent trends of bioinformatics and different tools and techniques Front Cover Bioinformatics in Agriculture Copyright Page Contents List of contributors About the editors Foreword Preface Section I: Bioinformatics and next-generation sequencing technologies (Chapters 1–14) Section II: Omics application (Chapters 15–26) Section III: Data mining and markers discovery (Chapters 27–33) Section IV: Artificial intelligence and agribots (Chapters 34–37) I. Bioinformatics and next generation sequencing technologies 1 Advances in agricultural bioinformatics: an outlook of multi “omics” approaches 1.1 Introduction 1.2 Different types of “omics” approaches 1.2.1 Phenomics 1.2.1.1 Applications 1.2.1.2 Challenges 1.2.2 Genomics 1.2.2.1 Applications of genomic technologies 1.2.2.2 Challenges of genomics in agricultural field 1.2.3 Transcriptomics 1.2.3.1 Applications 1.2.3.2 Different transcriptomic techniques with their application 1.2.3.3 Challenges 1.2.4 Proteomics 1.2.4.1 Applications 1.2.4.2 Technologies involved in proteomic analysis 1.2.4.3 Challenges of proteomic approaches 1.2.5 Metabolomics 1.2.5.1 Metabolomic application in crop production 1.2.5.2 Challenges of metabolomic technologies 1.2.6 Ionomics 1.2.6.1 Applications of plant ionomics 1.2.7 Computomics 1.2.7.1 Applications 1.2.7.2 Challenges 1.3 Conclusions and future prospective References 2 Promises and benefits of omics approaches to data-driven science industries 2.1 Sequencing technologies 2.2 Advances in genome assembly technology 2.2.1 Algorithms in reference-based and de novo assembly 2.2.2 Postassembly algorithms for encoding the biology 2.2.3 Genome-wide association, a valuable tool mapping associations with a phenotype 2.3 Transcriptomics—where genome connects to gene function 2.3.1 Methodologies and algorithms 2.3.1.1 RNA-seq data analysis 2.3.1.1.1 Quality control 2.3.1.1.2 Alignment 2.3.1.1.3 Quantification 2.3.1.1.4 Differential expression 2.3.1.2 Validating RNA-seq experiments 2.3.2 Noncoding RNA 2.3.3 Epigenomics 2.4 Beyond genomics and transcriptomics toward proteomics and metabolomics 2.4.1 Proteomics 2.4.2 Metabolomics 2.5 Integrating omics datasets 2.6 Challenges 2.7 Machine learning in omics 2.7.1 Machine learning for genomic studies 2.8 Big data storage and management 2.9 Future directions References 3 Bioinformatics intervention in functional genomics: current status and future perspective—an overview 3.1 Introduction 3.2 Functional genomic approaches 3.3 Serial analysis of gene expression 3.3.1 Advantages of serial analysis of gene expression 3.3.2 Drawbacks of serial analysis of gene expression technique 3.4 DNA microarray 3.4.1 Applications of microarray 3.4.2 Drawbacks of microarray 3.4.3 Bioinformatics tools for microarray data analysis 3.4.3.1 GeneChip Operating Software 3.4.3.2 Affymetrix Expression Console Software 3.5 Next-generation sequencing technologies 3.5.1 Illumina sequencing 3.5.1.1 Cost of sequencing full genome 3.5.2 Applications of next-generation sequencing 3.5.3 Bioinformatics tools for next-generation sequencing 3.6 Databases and genome annotation 3.6.1 Biological databases 3.6.1.1 Primary database 3.6.1.1.1 DNA databases 3.6.1.1.2 RNA databases 3.6.2 Functional genomic databases 3.6.2.1 Rice functional genomics 3.6.2.2 Functional genomics in Malvaceae family plants 3.6.2.3 Functional genomics in fungi 3.7 Conclusion References 4 Genome informatics: present status and future prospects in agriculture 4.1 Introduction 4.2 The evolution of DNA-seq 4.2.1 The first generation of sequencing technologies 4.2.2 The second generation of sequencing technologies 4.2.3 The third generation of sequencing technologies 4.3 Genomics in agriculture 4.3.1 Genome assembly 4.3.1.1 Pipeline of genome assembly 4.3.1.2 Simple sequence repeats 4.3.2 RNA-seq in agriculture 4.3.2.1 Types and pipeline of RNA-seq 4.3.3 Databases and prediction servers 4.3.3.1 List of plant-specific databases 4.4 Conclusion, applications, and future prospects of next-generation sequencing in agriculture References 5 Genomics and its role in crop improvement 5.1 Introduction 5.1.1 Genome 5.1.2 DNA sequencing 5.1.3 Research areas 5.1.3.1 Structural genomics 5.1.3.2 Functional genomics 5.1.3.3 Epigenomics 5.1.4 Model systems for the study of genome 5.1.4.1 Viruses and bacteriophages 5.1.4.2 Cyanobacteria 5.2 Development of genomic resources 5.2.1 Molecular markers 5.2.2 Transcriptome assemblies 5.2.3 Biparental mapping populations 5.2.4 Genetic linkage maps 5.2.5 Comparative genome mapping 5.2.6 Functional genomics 5.3 Application of genomic resources for crop improvement 5.3.1 Genetic fingerprinting 5.3.2 Hybrid testing 5.3.3 Marker-assisted selection 5.3.4 Gene trait association analysis using natural diverse populations 5.3.5 Genetic transformations 5.4 Genome analysis 5.4.1 Sequencing 5.4.1.1 Shotgun sequencing 5.4.1.2 High-throughput sequencing 5.4.2 Assembly 5.4.2.1 Assembly approaches 5.4.2.2 Finishing 5.4.3 Annotation 5.5 Applications of genomics 5.5.1 Genomics in medicine 5.5.2 Genomics in synthetic biology and bioengineering 5.5.3 Conservation genomics 5.6 Next-generation genomics for crop improvement 5.7 Genomic features for future breeding References 6 Genome-wide predictions, structural and functional annotations of plant transcription factor gene families: a bioinformat... 6.1 Transcription factor: an introduction 6.2 Plant transcription factors and its multifarious applications 6.2.1 AP2/ERF family 6.2.2 bHLH family 6.2.3 bZIP 6.2.4 DNA binding with one finger family 6.2.5 MADS family 6.2.6 Myeloblastosis family 6.2.7 NAM/ATAF/CUC family 6.2.8 WRKY family 6.2.9 Zinc fingers 6.3 Transcription factors for biotic and abiotic tolerance 6.4 Transcription factor databases 6.5 Bioinformatics tools used for structural and functional analysis of transcription factor gene families 6.5.1 Data mining by National Center for Biotechnology Information 6.5.2 BLAST tool 6.5.3 Multiple sequence alignment 6.5.3.1 MAFFT 6.5.3.2 T-Coffee 6.5.3.3 Clustal 6.5.3.4 MUSCLE 6.5.3.5 Kalign 6.5.4 Physicochemical properties analysis 6.5.5 Motif and domain prediction 6.5.5.1 InterPro 6.5.5.2 SMART 6.5.5.3 MEME Suite 6.5.6 In silico structure prediction of proteins 6.5.6.1 I-TASSER 6.5.6.2 Modeller 6.5.6.3 PDBsum 6.5.7 Gene predictions 6.5.8 Gene duplication and functional divergence studies 6.6 Conclusion References 7 Proteomics as a tool to understand the biology of agricultural crops 7.1 Introduction 7.2 Gel-based proteomics 7.2.1 Sodium dodecyl sulfate-polyacrylamide gel electrophoresis 7.2.2 Two-dimensional gel electrophoresis 7.2.3 Two-dimensional-difference-in-gel electrophoresis 7.3 Gel-free proteomics 7.3.1 Multidimensional Protein Identification Technology 7.3.2 Sequential window acquisition of all theoretical mass spectra 7.3.3 Label-free quantification 7.3.4 Isobaric tags for relative and absolute quantitation 7.3.5 Tandem mass tag 7.3.6 Stable Isotope Labeling by Amino acids in Cell Culture 7.4 High-throughput posttranslational modification proteomics 7.4.1 Phosphorylation 7.4.2 Glycosylation 7.4.3 Acetylation 7.5 Conclusion References Further reading 8 Metabolomics and sustainable agriculture: concepts, applications, and perspectives 8.1 Introduction 8.2 Sustainable agriculture and agro-production systems 8.3 Concepts of metabolomics and their applications to agriculture 8.4 Bridging metabolomics to sustainable agriculture 8.4.1 Metabolomics for biotic and abiotic stresses assessment 8.4.2 Metabolomics for soils science and soil conservation 8.4.3 Metabolomics for crops production 8.4.4 Metabolomics for crops quality 8.4.5 Metabolomics and postharvest crops science 8.5 Conclusions and future perspectives References 9 Plant metabolomics: a new era in the advancement of agricultural research 9.1 An introduction to metabolomics 9.2 Significance of metabolomics in plant biotechnology 9.3 Technologies involved in metabolomics improvement 9.4 Metabolomics databases 9.5 Metabolite profiling, identification, and quantification 9.6 Metabolic engineering in plants 9.7 Environmental and ecological metabolomics 9.8 Extraction methods in metabolomics 9.9 Metabolomics-assisted breeding techniques 9.9.1 Metabolic quantitative trait loci 9.9.2 Metabolic genome-wide association studies 9.10 Metabolites present in plant metabolome 9.11 Workflow of metabolomics analysis 9.11.1 Sample preparation 9.11.2 Data mining, annotation, and processing in metabolomics 9.11.3 Statistical tools and biomarker identification 9.12 Current and emerging methodologies of metabolomics in agriculture 9.13 Integration of metabolomics tools with other omics tools 9.14 Metabolomics under normal and stress conditions in plants 9.14.1 Drought stress 9.14.2 Salinity stress 9.14.3 Waterlogging stress 9.14.4 Temperature stress 9.14.5 Metal-induced stress 9.15 Applications and future perspective of metabolomics in plant biotechnology and agriculture References 10 Explore the RNA-sequencing and the next-generation sequencing in crops responding to abiotic stress 10.1 Introduction 10.2 From the beginning to the crop sciences: transcriptome analysis, its evolution, and state of the art 10.3 The overview on plant sequencing of RNA studies 10.4 The RNA-sequencing analysis workflow 10.4.1 Data generation 10.4.2 Raw data processing 10.4.3 Data analysis 10.4.3.1 Step 1—transcriptome assembly 10.4.4 Accessing the overall quality of the assembly 10.4.5 Transcript quantification 10.4.6 Differential expression analysis 10.4.7 Annotation and functional analysis 10.5 Functional genomics 10.6 Final considerations Acknowledgments References 11 Identification of novel RNAs in plants with the help of next-generation sequencing technologies 11.1 Introduction 11.1.1 Noncoding RNA classes in plants 11.2 Small RNA 11.2.1 MicroRNA 11.2.2 Small-interfering RNA 11.2.3 Heterochromatic small-interfering RNA 11.2.4 Phased small-interfering RNA and trans-acting small-interfering RNA 11.2.5 Natural antisense-small-interfering RNA 11.2.6 Transfer RNA–derived small RNA 11.3 Long noncoding RNA 11.4 Circular RNA 11.5 Chimeric RNA References 12 Molecular evolution, three-dimensional structural characteristics, mechanism of action, and functions of plant beta-gala... 12.1 Introduction 12.2 Protein sequence features of plant beta-galactosidases 12.3 Molecular evolution of beta-galactosidases and their classification 12.4 Three-dimensional structural characteristics of plant beta-galactosidases 12.5 Structural comparison between MiBGAL and TBG4 12.6 Substrate specificity of plant beta-galactosidases 12.7 Mechanism of action of plant beta-galactosidases 12.8 Physiological function of plant beta-galactosidase 12.9 Conclusion Conflict of interest References 13 Next generation genomics: toward decoding domestication history of crops 13.1 Introduction 13.2 Whole genome sequencing 13.3 Alternative genome scale approaches 13.4 Emergence of pan-genomics 13.5 Methodologies in domestication genomics 13.6 Case studies on next-generation sequencing-assisted inference of domestication history 13.6.1 Rice 13.6.2 Citrus 13.6.3 Peanut 13.6.4 Olive 13.6.5 Tea References 14 In-silico identification of small RNAs: a tiny silent tool against agriculture pest 14.1 Introduction 14.2 Small RNAs 14.3 Types of small noncoding RNAs 14.4 Next-generation sequencing in agronomic advancements 14.5 Small RNA world and their identification 14.5.1 MicroRNA 14.5.2 PIWI-interacting RNAs 14.5.3 Small interfering RNAs 14.6 Limitations 14.7 Conclusion Acknowledgments References II. Omics application 15 Bioinformatics-assisted multiomics approaches to improve the agronomic traits in cotton 15.1 Introduction 15.1.1 A bird’s-eye view of the world cotton market 15.1.2 An overview of omics mainly focused on plant-omics 15.1.3 Introduction of bioinformatics in the area of next-generation sequencing 15.1.4 Brief description of “integration of omics” 15.1.5 Why is multiomics study preferred over single-omics? 15.2 Big data in biology and omics 15.3 Bioinformatics resources for cotton-omics 15.3.1 Genomics 15.3.1.1 Translational genomics 15.3.1.2 Epigenomics 15.3.1.3 Transcriptomics 15.3.1.4 Functional genomics 15.3.2 Proteomics 15.3.3 Metabolomics 15.4 Integration of multiomics data to cope with cotton plant diseases 15.5 Challenges in the integration and analysis of multiomics data of cotton 15.6 Conclusion Acknowledgments References 16 Omics-assisted understanding of BPH resistance in rice: current updates and future prospective 16.1 Introduction 16.2 Rice genomics in brown planthopper resistance 16.3 Rice transcriptomics in brown planthopper resistance 16.4 Rice proteomics in brown planthopper resistance 16.5 Rice metabolomics in brown planthopper resistance 16.6 Bioinformatics in brown planthopper resistance in rice 16.7 Conclusion and future prospective References 17 Contemporary genomic approaches in modern agriculture for improving tomato varieties 17.1 Importance and origin of tomatoes 17.2 Organization of tomato genome and genetic variation of tomato cultivars 17.3 Tomato breeding 17.4 Disease resistance 17.5 Insect resistance 17.6 Abiotic stress tolerance 17.7 Tomato genetic markers for selection 17.8 Genomic selection for abiotic stress in tomato 17.9 Tomato transcriptomics 17.10 Tomato proteomics 17.11 Tomato metabolomics References 18 Characterization of drought tolerance in maize: omics approaches 18.1 Introduction 18.2 Drought timing 18.3 Plant response to drought 18.4 Progress with conventional breeding strategies for drought tolerance in maize 18.4.1 Seedling and physiological traits for drought tolerance 18.4.2 Yield traits for drought tolerance 18.5 Omics for characterizing drought stress responses in maize 18.5.1 Genomics 18.5.2 Transcriptomics 18.5.3 Proteomics and metabolomics 18.5.4 Advances in phenomics 18.5.5 Bioinformatics tools and databases 18.6 Conclusion References 19 Deciphering the genomic hotspots in wheat for key breeding traits using comparative and structural genomics 19.1 Introduction 19.2 Genomic comparisons and gene discovery 19.2.1 Gene discovery and marker development 19.2.1.1 Colinearity-based gene cloning 19.2.2 Gene annotation and marker development 19.2.3 Functional comparative genomics in cereals 19.3 Genomic hotspots in wheat 19.3.1 Biofortification hotspots 19.3.2 Genomic hotspots for biotic stress resistance 19.3.3 Genomic hotspots for drought stress tolerance 19.3.4 Genomic hotspots for heat tolerance in wheat 19.4 Genomic sequences to genomic hotspot 19.5 Conclusion References 20 Prospects of molecular markers for wheat improvement in postgenomic era 20.1 Introduction 20.2 Overview of molecular marker systems in wheat 20.3 Genome-wide markers for gene mapping 20.4 Wheat genomics for development of marker and its utilization 20.5 Status of genotyping platform of bread wheat and its progenitors 20.5.1 High-throughput SNP genotyping: microarray-based genotyping 20.5.2 High-throughput SNP genotyping: genotyping-by-sequencing 20.6 Utility and achievement of high-throughput genotyping approaches in wheat 20.7 Conversion of trait-linked SNPs to user-friendly markers 20.8 Conclusions and future directions References 21 Omics approaches for biotic, abiotic, and quality traits improvement in potato (Solanum tuberosum L.) 21.1 Introduction 21.2 Potato genomics 21.2.1 Whole-genome sequencing and resequencing 21.2.2 Molecular markers 21.2.3 Quantitative trait loci mapping, bulked segregant analysis, and GWAS 21.3 Potato transcriptomic 21.3.1 Biotic stress 21.3.2 Abiotic stress 21.3.3 Quality traits 21.3.4 miRNAs in potato 21.4 Potato proteomics 21.4.1 Biotic stress 21.4.2 Abiotic stress 21.4.3 Quality traits 21.5 Potato metabolomics 21.5.1 Biotic traits 21.5.2 Abiotic traits 21.5.3 Quality traits 21.6 Potato ionomics 21.7 Phenomics 21.8 Potato omics resources and integration of technologies 21.9 Conclusions References 22 Tea plant genome sequencing: prospect for crop improvement using genomics tools 22.1 Introduction 22.2 Whole-genome sequencing of tea plant 22.3 Identification and characterization of gene families 22.4 Tea transcriptome sequencing 22.5 Discovery of single-nucleotide polymorphism 22.6 Conclusion References 23 Next-generation sequencing and viroid research 23.1 Introduction 23.2 Next-generation sequencing technology 23.3 Impact of next-generation sequencing on viroid discovery 23.4 Role of next-generation sequencing in unraveling viroid RNA biology 23.4.1 Characterization of viroid sequence variants 23.4.2 Viroid pathogenesis 23.4.3 Mutational analyses of the viroids 23.5 Bioinformatic intervention in next-generation sequencing 23.6 Conclusion References 24 Computational analysis for plant virus analysis using next-generation sequencing 24.1 Introduction 24.2 Development of next-generation sequencing technology 24.3 Next-generation sequencing data analysis by bioinformatics tools 24.4 Next-generation sequencing in plant virology 24.5 Challenges 24.6 Conclusion and future prospective References 25 Microbial degradation of herbicides in contaminated soils by following computational approaches 25.1 Herbicides: use and impact on environment 25.2 Microbial degradation of herbicides 25.3 Strategies to improve biodegradation of herbicides 25.4 Integration of computational biology to improve biodegradation of herbicides 25.5 Bioremediation of atrazine by following metabolic modeling method 25.6 Conclusion Acknowledgments References 26 Chloroplast genome and plant–virus interaction 26.1 Introduction 26.2 Chloroplast genome 26.2.1 Structure and gene content 26.2.2 Genomic advances 26.2.3 Bioinformatic approaches and plastomes 26.2.4 Status of chloroplast genome sequencing in plants 26.3 Viral infection symptoms in plants 26.4 Role of chloroplasts in plant–virus life cycle 26.4.1 Changes in chloroplast structure upon viral infection 26.4.2 Virus factors involved in structural and functional changes of chloroplast 26.5 Role of chloroplast in the defense against plant pathogenic viruses 26.6 Plant–virus metagenomics 26.7 Conclusion References III. Data mining, markers discovery 27 Deciphering soil microbiota using metagenomic approach for sustainable agriculture: an overview 27.1 Introduction 27.2 Sustainable agriculture 27.3 Soil microbiomes 27.4 Soil microbial diversity 27.5 Analysis of the rhizosphere microbial community 27.6 Metagenomics in agriculture 27.6.1 Metagenomics based techniques for rhizosphere analysis 27.6.1.1 Sample collection and isolation of metagenomic DNA 27.6.1.2 Library preparation 27.6.1.3 Library screening 27.6.1.3.1 Sequence-based screening 27.6.1.3.2 Screening-based on function 27.7 Metagenomics for sustainable agriculture 27.8 Concluding remarks References 28 Concepts and applications of bioinformatics for sustainable agriculture 28.1 Introduction—a conceptual framework for sustainable agriculture 28.2 Database resources for agricultural bioinformatics 28.3 Genome mapping 28.3.1 Molecular marker systems and populations used for genetic mapping 28.3.2 Genetic mapping, physical mapping, and genome sequencing 28.3.3 Comparative mapping 28.3.4 Practical applications of genetic mapping 28.4 DNA marker development and application to genotyping 28.4.1 DNA marker types, their advantages and disadvantages 28.4.1.1 Restriction fragment length polymorphism 28.4.1.2 Random amplified polymorphic DNA 28.4.1.3 Amplified fragment length polymorphisms 28.4.1.4 Simple sequence repeats 28.4.1.5 Sequence characterized amplified region 28.4.1.6 Cleaved amplified polymorphic sequences/derived cleaved amplified polymorphic sequences 28.4.2 Shift to single-nucleotide polymorphism and insertion/deletion markers 28.4.3 Genotyping technologies and their application in breeding programs 28.4.4 Medium-throughput genotyping technologies 28.4.4.1 High-resolution melting 28.4.4.2 TaqMan—the 5′ nuclease assay 28.4.4.3 Kompetitive allele-specific polymerase chain reaction 28.4.4.4 RNase H2 enzyme-based amplification 28.4.4.5 Accuracy of single-nucleotide polymorphism genotyping 28.4.5 High-throughput genotyping technologies 28.4.5.1 Diversity arrays technology 28.4.5.2 High-throughput (HTP) fixed single-nucleotide polymorphism microarrays 28.4.5.3 Fluidigm 28.4.5.4 Array tape 28.4.5.5 OpenArray 28.4.5.6 iPLEX Gold assay 28.4.5.7 Genotyping-by-sequencing 28.4.6 Increased automation and throughput while reducing cost per data point 28.4.7 Single-nucleotide polymorphism genotyping for sustainable agriculture in a complex genome—bread wheat 28.5 Genome-wide association studies 28.5.1 Using single-nucleotide polymorphism markers for genome-wide association studies 28.5.2 Genome-wide association studies’ design and analysis 28.5.3 Applications of genome-wide association studies to plant and animal breeding 28.6 Emerging strategies for breeding and genetics 28.6.1 Gene expression regulation by noncoding RNA 28.6.2 Translation of “omics” data to agriculture 28.6.3 Bioinformatic resources for sustainable crop and livestock production 28.7 Conclusion and future prospects References 29 Application of high-throughput structural and functional genomic technologies in crop nutrition research 29.1 Introduction 29.2 Structural genomics 29.3 Application of structural genomics 29.3.1 To determine each single protein structure encrypted by the genome 29.3.2 Identification of three-dimensional structure and folding of novel protein functions 29.3.3 Gene and protein interactions: the role of protein structure prediction in structural genomics 29.4 Dynamic expression of functional genomics 29.5 Functional genomics approaches 29.6 Developing genomic technologies for enhancing food crops security 29.7 Application of high-throughput genomics technologies in nutrition research References Further reading 30 Bioinformatics approach for whole transcriptomics-based marker prediction in agricultural crops 30.1 Introduction to transcriptomics 30.1.1 Transcriptome 30.2 Markers 30.2.1 Phenotypic markers 30.2.2 Biochemical markers 30.2.3 Cytological markers 30.2.4 Molecular markers 30.3 Markers in plants 30.4 Expressed sequence tags and simple sequence repeats 30.5 Tools for generating transcriptomic data 30.5.1 Serial analysis of gene expression technology 30.5.2 Microarrays 30.5.3 RNA sequencing 30.6 Why transcriptomic markers? 30.7 How are markers developed/selected? 30.8 What has been done 30.9 Future prospects References 31 Computational approaches toward single-nucleotide polymorphism discovery and its applications in plant breeding 31.1 Introduction 31.2 Single-nucleotide polymorphism discovery 31.2.1 Reference-based single-nucleotide polymorphism mining 31.2.1.1 Sample preprocessing and DNA or RNA extraction 31.2.1.2 Library preparation 31.2.1.3 Next-generation sequencing 31.2.1.4 Quality control and alignment to the reference genome 31.2.1.5 Single-nucleotide polymorphism calling 31.2.2 De novo single-nucleotide polymorphism discovery 31.2.2.1 Quality control and de novo assembly 31.2.2.2 Alignment or mapping of high-quality raw read to the mock reference genome 31.3 Single-nucleotide polymorphism annotation 31.4 Single-nucleotide polymorphism database 31.5 Single-nucleotide polymorphism genotyping 31.5.1 Gel-based single-nucleotide polymorphism genotyping 31.5.1.1 Cleaved amplified polymorphic sequence markers 31.5.1.2 Single-stranded conformation polymorphism 31.5.2 Nongel-based single-nucleotide polymorphism genotyping 31.5.2.1 TaqMan assay 31.5.2.2 Minisequencing 31.6 Application of single-nucleotide polymorphisms in plants 31.6.1 Genetic diversity 31.6.2 Genetic mapping 31.6.3 Phylogenetic analysis 31.6.4 Marker-assisted selection 31.7 Conclusion and prospects Acknowledgment References 32 Bioinformatics intervention in identification and development of molecular markers: an overview 32.1 Introduction 32.2 Genetic markers 32.2.1 Classical markers: The classical markers are further divided that include morphological markers, cytological markers... 32.2.1.1 Morphological markers 32.2.1.2 Cytological markers 32.2.1.3 Biochemical markers 32.2.2 Molecular markers 32.3 Restriction fragment length polymorphism (RFLP) 32.3.1 Application of restriction fragment length polymorphism 32.3.1.1 Restriction fragment length polymorphism in DNA fingerprinting 32.3.1.2 Restriction fragment length polymorphism in species identification 32.3.1.3 Restriction fragment length polymorphism in comparative mapping 32.3.1.4 Linkage mapping with restriction fragment length polymorphism markers 32.3.1.5 Elucidating the genetic traits 32.3.1.6 Restriction fragment length polymorphism in back crossing 32.4 Random amplified polymorphic DNA (RAPD) 32.4.1 Applications of random amplified polymorphic DNA 32.4.1.1 Genetic mapping 32.4.1.2 In development of genetic markers 32.4.1.3 In population genetics 32.4.1.4 Plant breeding 32.5 Amplified fragment length polymorphism (AFLP) 32.5.1 Advantages of amplified fragment length polymorphism 32.5.2 Disadvantages of amplified fragment length polymorphism 32.5.3 Techniques for amplified fragment length polymorphism data analysis 32.5.3.1 Linkage mapping 32.5.3.2 Population-based methods 32.5.3.3 Phylogenetic methods 32.5.4 Application of amplified fragment length polymorphism 32.6 Simple sequence repeats (SSR) 32.6.1 Distribution of simple sequence repeats 32.6.2 Isolation of simple sequence repeats markers 32.6.3 Applications of microsatellite 32.6.3.1 Simple sequence repeats in the mapping of gene 32.6.3.2 Simple sequence repeats in functional diversity 32.6.3.3 Simple sequence repeats in comparative mapping 32.7 Intersimple sequence repeat (ISSR) 32.7.1 Advantages of intersimple sequence repeat markers 32.7.2 Disadvantages of intersimple sequence repeat markers 32.7.3 Application of intersimple sequence repeat markers 32.8 Single-nucleotide polymorphism (SNP) 32.8.1 Single-nucleotide polymorphism detection 32.8.2 In vitro techniques 32.8.3 Single-nucleotide polymorphism application 32.8.4 Diversity array technology (DArT Seq) 32.9 Quantitative trait loci (QTL) 32.9.1 Molecular markers 32.9.2 Construction of genetic linkage maps 32.9.3 Mapping population 32.9.4 Identification of polymorphism 32.9.5 Linkage analysis of markers 32.9.6 Genetic distance and mapping functions 32.9.7 Quantitative trait loci analysis 32.9.8 Quantitative trait loci detection 32.9.9 Advantages and disadvantages of quantitative trait loci mapping 32.10 Association mapping 32.10.1 Linkage disequilibrium 32.10.2 Methods of association mapping 32.10.3 Class of association mapping 32.10.3.1 Candidate-gene-based 32.10.3.2 Genome-wide association study 32.10.4 Association mapping in the breeding program 32.11 Marker-assisted selection (MAS) 32.11.1 Application of marker-assisted selection 32.12 Bioinformatics intervention in molecular markers 32.13 Software for simple sequence repeats discovery 32.14 Software for single-nucleotide polymorphism discovery References 33 Deciphering comparative and structural variation that regulates abiotic stress response 33.1 Introduction 33.2 Expression quantitative trait loci and their functional significance 33.2.1 Molecular marker system for genotyping 33.2.2 Transcript abundance measurement by RNA sequence 33.2.3 Connecting genomic variation to expression variation 33.3 Regulatory small RNAs 33.3.1 Discovery and annotation of small RNAs based on deep sequencing 33.3.2 Detection of small RNA targets 33.3.3 Natural variation in small RNAs and their targets 33.3.4 Integrating small RNA sequencing with quantitative trait loci mapping 33.4 Epigenomic regulation of gene expression in plant 33.4.1 DNA methylation and its role in transcriptional regulation 33.4.2 The role of histone modification for the regulation of gene expression 33.5 Protein structure provides vital information of function during salt stress 33.5.1 Variation in protein structure contributing to salinity tolerance 33.5.2 Future prospect in substitution-mediated enhanced salt tolerance 33.6 High performance computing in comparative genomics 33.7 Conclusion References IV. Artificial intelligence and agribots 34 Deep Learning applied to computational biology and agricultural sciences 34.1 Introduction 34.2 Deep Learning and Convolutional Neural Network 34.3 Deep Learning applications in computational biology 34.3.1 Omics 34.3.2 Biological image processing 34.3.3 Multiomic data integration 34.3.4 Single-cell RNA sequencing 34.3.5 Pharmacogenomics 34.3.6 Modeling biological data in a Deep Neural Network 34.3.6.1 Deep Leaning for regulatory genomics 34.4 Deep Learning applications in agricultural sciences 34.4.1 Example of Deep Learning applied to agriculture 34.4.2 Convolutional Neural Networks in agriculture 34.4.3 Recurrent Neural Network for agricultural classification 34.5 Conclusion References 35 Image processing–based artificial intelligence system for rapid detection of plant diseases 35.1 Introduction 35.2 Visual symptoms of diseases in plant 35.3 Imaging 35.4 Database creation 35.5 Disease identification using feature extraction and classification 35.6 Disease identification using convolutional neural network 35.7 Determination of the accuracy of the system 35.8 Severity estimation 35.9 Conclusion References 36 Role of artificial intelligence, sensor technology, big data in agriculture: next-generation farming 36.1 Introduction 36.2 Characteristics of big data 36.2.1 Volume 36.2.2 Velocity 36.2.3 Variety 36.2.4 Veracity 36.3 Big data and smart agriculture 36.3.1 Digital soil and crop mapping 36.3.2 Weather prediction 36.3.3 Fertilizers recommendation 36.3.4 Disease detection and pest management 36.3.5 Adaptation to climate change 36.3.6 Automated irrigation system 36.4 Sources of big data 36.4.1 Sensors 36.4.1.1 Remote sensing platforms: satellites 36.4.1.2 Airborne platform systems: unmanned aerial vehicles and remotely piloted aircraft 36.4.1.3 Ground platform systems: unmanned ground vehicle 36.4.2 Statistical data 36.4.3 Remote sensing 36.4.4 Cloud data source 36.4.5 Internet of things database source 36.4.6 Media source 36.5 Techniques and tool use in big data analysis