Genomics of Cereal Crops (Springer Protocols Handbooks)

Dedication
Preface
Contents
Contributors
Chapter 1: An Update on Progress and Challenges of Crop Genomes
	1 Introduction
	2 Materials
		2.1 Progress and Challenges of Crop Genomes
	3 Methods
	References
Chapter 2: Updates on Genomic Resources for Crop Improvement
	1 Introduction
	2 Advances in Molecular Marker Discovery and Genotyping
	3 Decoding Plant Genomes
	4 Functional Genomic Studies: Multi-Omics Resources
		4.1 Transcriptomics
		4.2 Proteomics
		4.3 Metabolomics
	5 Translational Genomics: Bridging the Genotype-to-Phenotype  Gap
	6 Conclusion
	References
Chapter 3: Next-Generation Sequencing Technologies: Approaches and Applications for Crop Improvement
	1 Introduction
	2 Evolution of Sequencing Technologies
		2.1 First-Generation Sequencing Technology
			2.1.1 Maxam and Gilbert Method
				Methodology
			2.1.2 Sanger´s Sequencing
				Methodology
				Applications
		2.2 Second/Next-Generation Sequencing Technologies
			2.2.1 Roche/454 Sequencing
				Principle
				Methodology
			2.2.2 Solexa/Illumina Sequencing Technology
				Principle
				Methodology
			2.2.3 ABI/SOLiD Sequencing
				Principle
				Methodology
			2.2.4 Ion Torrent Sequencing
				Principle
				Methodology
			2.2.5 BGI/Complete Genomics DNA Nanoball Sequencing
				Principle
				Methodology
				Limitations of Next-Generation Sequencing Techniques
		2.3 Third-Generation Sequencing (TGS)/Next-NGS Technologies
			2.3.1 Helicos Biosciences/HeliScope Genetic Analysis System
			2.3.2 Pacific Biosciences/PacBio-SMRT Sequencing
				Principle
				Methodology
			2.3.3 Oxford Nanopore Technology/Nanopore Sequencing
				Principle
				Methodology
		2.4 Synthetic Long-Read Sequencing
			2.4.1 Illumina SLR Sequencing
			2.4.2 10x Genomics SLR Sequencing
	3 Alternative Approaches
		3.1 HI-C Sequencing
		3.2 Optical Mapping
	4 Cost of Sequencing
	5 Applications of NGS Technology in Crop Improvement
		5.1 De Novo Sequencing of Crop Plant Genomes
		5.2 Re-sequencing of Related Species/Other Plant Genetic Resources
			5.2.1 Genome-Wide Identification of Structural Variations
			5.2.2 Population Genomics (GWAS and GBS)
			5.2.3 Pan-Genome Analysis
			5.2.4 Marker Development (SSRs, SNPs, and InDels)
			5.2.5 QTL Mapping
			5.2.6 Linkage Mapping/Association Mapping
			5.2.7 Genomics-Assisted Breeding
			5.2.8 Genotyping Arrays
		5.3 Organellar Genome Sequencing
		5.4 Functional Genomics in Crop Improvement
			5.4.1 Transcriptomics
			5.4.2 Epigenomics or Epigenetics
			5.4.3 Methyl C-seq/Whole-Genome Bisulfite Sequencing
			5.4.4 Reduced Representation Bisulfite Sequencing
			5.4.5 Chromatin Immunoprecipitation
			5.4.6 Exome Sequencing
		5.5 Metagenomics
		5.6 Paleogenomics
	6 Future Perspectives
		6.1 Genome Diversity
		6.2 Population Genomics
		6.3 Developmental Biology
		6.4 Portable Real-Time Sequencers
		6.5 Unconventional Application of DNA Sequencing
	References
Chapter 4: Check CRISPR Editing Events in Transgenic Wheat with Next-Generation Sequencing
	1 Introduction
	2 Materials
		2.1 Used by All Procedure
		2.2 PCRs
		2.3 PCR Product Recovery
		2.4 DNA Concentration Measurement
	3 Methods
		3.1 Design Primers
		3.2 First-Round  PCR
		3.3 Second-Round  PCR
		3.4 PCR Cleanup with Homemade Beads
		3.5 Data Analysis
			3.5.1 Filter the Raw Output
			3.5.2 Demultiplex the fastq  file
			3.5.3 Check the Editing Events
			3.5.4 Make bam files for Viewing the Details in  IGV
	4 Notes
	References
Chapter 5: Virus Induced Gene Silencing: A Tool to Study Gene Function in Wheat
	1 Introduction
	2 Materials
		2.1 Plant Material, Bacterial Strains, and Virus Vectors
		2.2 Growth and Culture Media and Antibiotics
		2.3 Kits and Reagents
			2.3.1 The Classical Method
				Plasmid DNA Isolation and Purification
				Ligation
				In Vitro Transcription
				Plant Inoculations Using In Vitro Transcribed Viral RNAs
			2.3.2 Agrobacterium Based Method
				Plasmid DNA Isolation and Purification
				Plant RNA Isolation and cDNA Synthesis
				PCR Product Purification
			2.3.3 Other Reagents and Solutions
		2.4 Computational Tools
	3 Methods
		3.1 Designing of Gene Silencing Constructs
			3.1.1 Selection of Target Region
			3.1.2 Design of Gene Silencing Construct
				The Classical Method
				Agrobacterium Based Method
		3.2 Cloning of Gene Silencing Constructs
			3.2.1 Ligation-Dependent Cloning
				Preparation of Plasmid DNAs
				Purification of the Plasmid DNAs
				Preparation of Gene Silencing Constructs
				Cloning of Gene Silencing Construct into the γ Plasmid
			3.2.2 Ligation-Independent Cloning
				Preparation of Plasmid DNAs
					When Plasmid DNAs Are Available at Initial Step
					Agrobacterium Glycerol Stocks Are Available for Each Plasmid at Initial Step
				Preparation of Gene Silencing Constructs
					Synthetic Hairpin Gene Silencing Construct Method
					PCR Product Method for Sense or Antisense Gene Silencing Construct
				Ligation-Independent Cloning of Gene Silencing Construct into the BSMV γ Plasmid
		3.3 Production of Recombinant BSMV and Inoculations of Wheat Plants
			3.3.1 The Classical Method
				Linearization of α, β, Recombinant pγ.PDShp and pγ42-MCS
				In Vitro Transcription Using T7 DNA-Dependent RNA Polymerase
				Inoculations of Wheat Plants
			3.3.2 Agrobacterium Based Method
				In Planta Production of Infectious BSMV in N. Benthamiana
				Inoculations of Wheat Plants
		3.4 Assessment of Virus Induced Gene Silencing
	4 Notes
	References
Chapter 6: Common Genomic Tools and Their Implementations in Genetic Improvement of Cereals
	1 Introduction
	2 Materials
		2.1 Molecular markers
		2.2 Linkage and QTL mapping
			2.2.1 Requirements for QTL mapping
		2.3 Genome-Wide Association Study
		2.4 Next-Generation Sequencing
	3 Methods
		3.1 Marker-Assisted Selection
		3.2 Transgenic Approaches
	4 Conclusion
	5 Notes
	References
Chapter 7: Protocol for Identification and Annotation of Differentially Expressed Genes Using Reference-Based Transcriptomic A...
	1 Introduction
	2 Materials
		2.1 Computational Facility
		2.2 Available Open Source Software and Tools for Reference Transcriptome Assembly and Functional Annotation of DEGs
	3 Methodology
		3.1 Sequence Retrieval from Public Resources
		3.2 Quality Check of the Raw  Data
		3.3 Trimming of Raw Reads
		3.4 Indexing of the Reference Genome
		3.5 Mapping of Raw Reads to Reference Genome
		3.6 Identification of Differentially Expressed Genes
		3.7 Annotation of Identified  DEGs
	4 Notes
	References
Chapter 8: Transcriptome Data Analysis Using a De Novo Assembly Approach
	1 Introduction
		1.1 Candidate Gene Approach
		1.2 Global Transcriptome Profiling
	2 Materials
		2.1 Hardware Requirements
		2.2 Data Analysis Tools
	3 Methods
		3.1 Experimental Design
		3.2 Generation of Sequencing Reads
		3.3 Downloading of Raw Transcriptome  Data
		3.4 Dumping of SRA Data into Fastq File Format
		3.5 Quality Assessment and Preprocessing of Raw Reads
		3.6 De Novo RNA-Seq Assembly
			3.6.1 De Novo Assembly Construction
		3.7 Transcript Abundance Estimation
		3.8 Normalization of Raw Count Data and Identification of Differentially Expressed Genes
			3.8.1 FPKM (Fragments per Kilobase per Million) and RPKM (Reads per Kilobase per Million)
		3.9 Functional Annotation and KEGG Pathway Analysis
		3.10 Data Validation
	4 Notes
	References
Chapter 9: Protocol for In Silico Identification and Functional Annotation of Abiotic Stress-Responsive MicroRNAs in Crop Plan...
	1 Introduction
	2 Materials
		2.1 Computational Facility
		2.2 Databases and Analysis Tools
	3 Methods
		3.1 Retrieval of Reference Plant miRNAs Sequences from miRBase Database
		3.2 Retrieval of Precursor Sequences and BLAST Analysis
		3.3 Secondary Structure Prediction of Pre-miRNAs Using mfold Web Server
		3.4 Prediction of miRNA Targets Using psRNATarget
	4 Notes
	References
Chapter 10: Functional Annotation of miRNAs in Rice Using ARMOUR
	1 Introduction
	2 Materials
		2.1 Database Content
	3 Methods
		3.1 Key Features of the Database
		3.2 Database Design and Utility
		3.3 Database Access
		3.4 Nomenclature and Gene Annotation
		3.5 miRNA or Transcript Identification and Scoring
		3.6 Expression Analysis
	4 Conclusion
	References
Chapter 11: Identification of ceRNAs in Cereal Crops: A Computational Approach
	1 Introduction
	2 Material
		2.1 Computational Facility
		2.2 Database and Analysis Tools
		2.3 Data Set Required for ceRNA Identification
	3 Methods
		3.1 Retrieval of miRNAs Sequences from miRBase Database
		3.2 Retrieval of Transcript Sequence
		3.3 Identification of ceRNAs
	4 Notes
	References
Chapter 12: Genotyping-by-Sequencing (GBS) Method for Accelerating Marker-Assisted Selection (MAS) Program
	1 Introduction
	2 Materials
		2.1 Molecular Markers
		2.2 Marker-Assisted Selection
		2.3 Marker-Assisted Backcross Breeding
		2.4 Gene/QTL Pyramiding
		2.5 Marker-Assisted Recurrent Selection
		2.6 Genomic Selection
		2.7 Next-Generation Sequencing (NGS)
	3 Methods
		3.1 Genotyping-by-Sequencing (GBS)
		3.2 GBS Methodology (Fig. 1)
	4 Notes
	References
Chapter 13: Genomic Selection Using Bayesian Methods: Models, Software, and Application
	1 Introduction
	2 Material
		2.1 Installation of R-Packages
		2.2 Dataset
	3 Methods
		3.1 BayesA
		3.2 BayesB
		3.3 BayesC
		3.4 Bayesian LASSO (BLASSO)
		3.5 Bayesian Ridge Regression (BRR)
		3.6 BayesCπ
		3.7 BayesU
		3.8 BayesHP
		3.9 BayesHE
		3.10 Implementing BayesA, BayesB, BayesC, BLASSO, and BRR
			3.10.1 Model with Only Random Effect of Markers
			3.10.2 Model with Both Fixed and Random Effects
			3.10.3 Model with Fixed Effects, Random Effects, and Covariates
			3.10.4 Estimation of Heritability
			3.10.5 Estimating Marker Effects, Variance of the Marker Effects, and Error Variance and Breeding Values
		3.11 Application with Real Dataset
	References
Chapter 14: Approaches of Single-Cell Analysis in Crop Improvement
	1 Introduction
		1.1 Challenges of Single Cell Analysis in Crop Plants
		1.2 Types of Single Cell Omics Approaches in Crop Plants
	2 Materials
		2.1 Transcriptomic Approaches or Single-Cell RNA Sequence Analysis
			2.1.1 Isolation of Single Cells from Crop Plants
			2.1.2 Estimation of the Gene Expression Profiles in Individual Cells
			2.1.3 Dimension Reduction, Visualization, and Clustering
			2.1.4 Identification of Cluster Markers and Cluster Cell Type Annotation
			2.1.5 Single-Cell Trajectory and Pseudotime Analysis
			2.1.6 GO Enrichment and Pathway Analysis in Crops
			2.1.7 Transcription Factor (TF) Analysis of DEGs and Weighted Gene Co-expression Network Analysis
			2.1.8 Correlation Analysis Between scRNA-seq and Bulk RNA-seq   Data
	3 Methods
		3.1 Single-Cell Metabolomics in Crops
			3.1.1 Application and Techniques for Single-Cell Metabolomics
		3.2 Computational Challenges in scATAC-seq Analysis
			3.2.1 Advantages of scATAC-seq Approaches
			3.2.2 Disadvantage of scATAC-seq Approaches
		3.3 Genomics Approaches
		3.4 Epigenomics and Its Approaches
		3.5 Integration of Omics Approaches
			3.5.1 Omics Approaches for Engineering Wheat Production Under Abiotic Stresses
			3.5.2 Advances in Omics Approaches for Abiotic Stress Tolerance in Tomato
			3.5.3 Integrating Omic Approaches for Abiotic Stress Tolerance in Soybean
		3.6 Batch Effect Correction in Integrated Omics   Data
	4 Notes
	5 Future Prospects
	6 Conclusions
	References
Chapter 15: Genome-Wide Association Study (GWAS) for Trait Analysis in Crops
	1 Introduction
	2 Materials and Methods
		2.1 Materials
		2.2 Methods
			2.2.1 Genome-Wide Association Mapping (GWAS) or Genome Scan
				Biology of GWAS
				Approach and Practice of GWAS
			2.2.2 Candidate Gene Association Mapping
	3 Achievements of Genome-Wide Association Studies (GWAS) in Plant Traits
		3.1 Biotic Stress Resistance
		3.2 Abiotic Tolerance
		3.3 Yield Associated Traits
		3.4 Metabolic Composition
	4 Future Prospects of GWAS
	References
Chapter 16: QTL Interval Mapping for Agronomic and Quality Traits in Crops
	1 Introduction
	2 Materials
		2.1 Mapping Populations
		2.2 Genotyping of Mapping Population
		2.3 Software and Tools
			2.3.1 For Statistical Analysis of Phenotyping  Data
			2.3.2 For Linkage Map Construction
			2.3.3 For QTL Interval Mapping
	3 Methods
		3.1 Experimental Design and Phenotyping
		3.2 Phenotyping Data Analysis
		3.3 Genotyping
		3.4 Linkage Map Construction
			3.4.1 Input  Data
			3.4.2 Linkage Map Construction Using MAPMAKER
		3.5 QTL Mapping
			3.5.1 Input Data Files
			3.5.2 QTL Interval Mapping with QTL Cartographer (the Following Steps Are Involved)
	4 Notes
	References
Chapter 17: Whole-Genome Bisulfite Sequencing for Detection of DNA Methylation in Crops
	1 Introduction
	2 Materials
	3 Methods
		3.1 DNA Extraction Using CTAB Method
		3.2 Library Preparation
		3.3 Fragmented DNA A-tailing
		3.4 Adapter Ligation and Fragment Size Selection for Sequencing
		3.5 Bisulfite Conversion
		3.6 PCR Amplification of Bisulfite-Converted Library
		3.7 Sequencing Data Analysis
	4 Notes
	References
Chapter 18: Tools and Techniques for Genomic Imprinting
	1 Introduction
	2 Genomic Imprinting Mechanisms
		2.1 Endospermic DNA Methylation
		2.2 Genome-Wide Endospermic Repeat Sequences Demethylation
		2.3 Imprinted Gene Expression by Polycomb Group Proteins
		2.4 siRNA Mediated Maternal Specific Expression
	3 Tools and Techniques Available for Genomic Imprinting
		3.1 Approaches and Data Requirements
		3.2 Statistical Model for Characterizing Genomic Imprinting Effects
		3.3 Computational Methods and Repositories for Identification of Imprinted Genes
	4 Relevance of Genomic Imprinting in the Era of OMICS Research
		4.1 Relevance to Genetics and Plant Breeding Studies
		4.2 Few Examples of Imprinting Targets
	5 Conclusion and Future Prospects
	References
Chapter 19: Computational Methods for Receptor-Metabolite Interaction Studies in Crops
	1 Introduction
	2 Materials
		2.1 Molecular Dynamic Simulation
	3 Methods
		3.1 Energy Minimization
		3.2 Initialization and Heating
		3.3 Equilibration
		3.4 Production Run
		3.5 Analysis
		3.6 MM/PBSA
		3.7 MM/PBSA Results Analysis
	References
Index