Bioinformatics in Agriculture: Next Generation Sequencing Era

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
36.5.1 Machine learning
36.5.1.1 Livestock management
36.5.1.1.1 Livestock production
36.5.1.1.2 Animal welfare
36.5.1.2 Water management
36.5.1.3 Soil management
36.5.2 Cloud platforms
36.5.3 Geographic information systems
36.5.4 Vegetation indices
36.6 Role of big data in agriculture ecosystem: for smart farming
Acknowledgments
Conflict of interest
Author contributions
References
37 Artificial intelligence: a way forward for agricultural sciences
37.1 Introduction of artificial intelligence
37.2 History of artificial intelligence
37.3 Methods and approaches in artificial intelligence
37.3.1 Machine learning
37.3.1.1 Supervised learning
37.3.1.2 Unsupervised learning
37.3.1.3 Reinforcement learning
37.3.1.4 Semisupervised learning
37.3.2 Artificial neural network
37.3.3 Deep learning
37.4 Technological advancements in artificial intelligence
37.4.1 Hardware
37.4.1.1 Processor
37.4.1.2 Memory device
37.4.1.3 Storage device
37.4.2 Software
37.4.2.1 Artificial intelligence platform
37.4.2.1.1 Google artificial intelligence platform
37.4.2.1.2 TensorFlow
37.4.2.1.3 Amazon artificial intelligence services
37.4.2.1.4 Microsoft Azure
37.4.2.1.5 Rainbird
37.4.2.1.6 Infosys Nia
37.4.2.1.7 Wipro HOLMES
37.4.2.2 Artificial intelligence solution
37.4.2.3 Big data
37.5 Application of artificial intelligence
37.5.1 Agriculture/farming
37.5.1.1 Field mapping
37.5.1.2 Yield monitoring
37.5.1.3 Irrigation management
37.5.1.4 Crop scouting
37.5.1.5 Disease detection and diagnosis
37.5.1.6 Agriculture robot and drones
37.5.1.7 Crop phenotyping
37.5.1.8 Soil management
37.5.1.9 Nutrient monitoring
37.5.1.10 Smart greenhouse management
37.5.1.11 Weather tracking and forecasting
37.5.2 As a service industry
37.5.3 Biological sciences
37.5.3.1 Bioinformatics
37.5.3.2 Molecular biology and omics data mining
37.5.3.3 System and synthetic biology
37.6 Future perspective and challenges
37.7 Conclusion
References
Index
Back Cover