Genomics Data Analysis for Crop Improvement

✍ Scribed by Priyanka Anjoy (editor), Kuldeep Kumar (editor), Girish Chandra (editor), Kishor Gaikwad (editor)

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 378
• Series: Springer Protocols Handbooks
• Category: Library

✦ Synopsis

This book addresses complex problems associated with crop improvement programs, using a wide range of programming solutions, for genomics data handling and sustainable agriculture. It describes important concepts in genomics data analysis and sequence-based mapping approaches along with references. The book contains 16 chapters on recent developments in several methods of genomic data analysis for crop improvements and sustainable agriculture, all authored by eminent researchers who are experts in their fields. These chapters focus on applications of a wide range of key bioinformatics topics, including assembly, annotation, and visualization of next-generation sequencing (NGS) data; expression profiles of coding and noncoding RNA; statistical and quantitative genetics; trait-based association analysis, quantitative trait loci (QTL) mapping, and artificial intelligence in genomic studies.

Real examples and case studies in the book will come in handy when applying the techniques. The relative scarcity of reference materials covering bioinformatics applications as compared with the readily available books also enhances the utility of this book. The targeted readers of the book are scientists, researchers, and bioinformaticians from genomics and advanced breeding in different areas. The book will appeal to the applied researchers engaged in crop improvements and sustainable agriculture by using bioinformatics tools, students, research project leaders, and practitioners from the various marginal disciplines and interdisciplinary research.

✦ Table of Contents

In the Memory of Dr. Dharmendra Mallick
In the Memory of Dr. Hukum Chandra
Preface
Contents
Chapter 1: Statistical and Biological Data Analysis Using Programming Languages
1 Introduction
2 Perl
2.1 Installation of Perl
2.2 Perl Basics
2.3 Perl Regular Expression
2.4 Application of Perl in Bioinformatics
2.5 Perl Modules
2.6 BioPerl
3 R
3.1 Installation of R
3.2 Statistical Analysis and Visualization Using R
3.3 Application of R in Bioinformatics
4 Conclusion
References
Headings0005713749
Chapter 2: Python for Biologists
1 Introduction
2 Installing Python
3 Installing Anaconda Distribution
3.1 Running Jupyter Notebook
4 Errors in Python
5 Datatypes and Operators
5.1 Datatypes
5.2 Operators
6 Variables
6.1 Rules for Variable Naming
7 Strings
7.1 String Indexing
7.2 Operations on Strings
7.3 Commands in Strings
8 Python Lists and Tuples
8.1 Accessing Values in List
8.1.1 List Indexing
8.1.2 List Slicing
8.2 Tuples
9 Dictionary in Python
10 Conditional Statements
10.1 If and Else Statements
11 Loops in Python
11.1 While Loop
11.2 `For´ Loop
11.3 Breaking a Loop
12 Functions
12.1 Returning Values
13 Modules in Python
14 Classes and Objects
14.1 int() Method
15 File Handling in Python
16 Data Handling
17 Conclusions and Future Prospectors
18 Exercises
References
Chapter 3: Assembly, Annotation and Visualization of NGS Data
1 Introduction
2 Considerations for Next-Generation Sequencing
2.1 Points to Be Considered Before Starting Next Generation Sequencing Project
2.2 Properties of the Genomes to Be Taken into Consideration
3 Next Generation Sequencing Platforms
3.1 Critical Factors Before Choosing the Sequencing Platform
4 Demultiplexing of Raw Sequencing Data
5 Assembly
5.1 Accuracy
5.2 Assembly Finishing
6 Annotation
6.1 Annotation Quality Scoring Parameters
6.2 Functional Annotation
6.2.1 Critical Points to Be Considered for Annotation
7 Visualization
8 Workflow of the NGS According to Applications
8.1 Whole Genome Sequencing (WGS) Data Analysis
8.2 RNA-Seq Data Analysis
8.3 Targeted Sequencing
8.4 Metagenomics
8.5 Variant Discovery
9 Conclusions
References
Chapter 4: Statistical and Quantitative Genetics Studies
1 Introduction
2 History
3 Quantitative Genetics and Plant Breeding
3.1 Tools for Quantitative Genetics and Software Used by Plant Breeders
3.1.1 Assessment of Variability
3.1.2 Softwares for Assessment of Variability
3.2 Determination of Yield Component and Selection of Elite Genotype
3.2.1 Softwares for Assessment of Yield Component and Selection of Elite Genotype
3.3 Choice of Suitable Parents and Breeding Procedures
3.3.1 Softwares for Selection of Suitable Parents
3.4 Assessment of Stability of Genotypes
3.4.1 Models for Stability Analysis
3.4.2 Softwares for Stability Analysis
4 Selection of Quantitative Traits
5 Molecular Quantitative Genetics and Mapping of Quantitative Traits Loci
5.1 QTL Mapping
5.1.1 Approaches for QTL Mapping
5.1.2 Softwares Used in QTL Mapping
5.2 Genome-Wide Association Studies (GWAS)
5.2.1 Workflow of GWAS
5.2.2 Softwares Used for GWAS Analysis
6 Genomic Selection (GS)
6.1 Workflow of Genomic Selection (GS)
6.2 Models Used for Computing GEBVs
6.3 Softwares Used for Genomic Selection
7 Conclusion
References
Chapter 5: Mapping of Quantitative Traits Loci: Harnessing Genomics Revolution for Dissecting Complex Traits
1 Introduction
2 Principle of QTL Mapping
3 Prerequisite of QTL Mapping
3.1 Molecular Markers
3.2 Mapping Populations
3.3 Phenotyping for QTL Mapping
4 Methods of QTL Mapping
4.1 Linkage Map Construction
4.2 Linkage Based QTL Mapping
4.2.1 Single Marker Analysis
4.2.2 Simple Interval Mapping
4.2.3 Composite Interval Mapping
4.2.4 Inclusive Composite Interval Mapping
4.2.5 Multiple Interval Mapping
4.3 Bulk Sequencing Based QTL Mapping
4.3.1 QTL-Seq
4.3.2 QTG-Seq
4.4 Modified Approaches of QTL Mapping
4.5 Meta-QTL Analysis
5 Software and Packages for QTL Mapping
6 Validation of QTL
7 Fine Mapping of QTLs
References
Chapter 6: Trait Based Association Mapping in Plants
1 Introduction
2 Fundamental Concepts in Association Mapping
3 Requirements of AM
4 Major Genotyping Techniques Used for Association Mapping Studies
4.1 PCR Based Genotyping
4.2 Sequencing Based Genotyping
4.2.1 GBS
4.2.2 RAD-Seq
4.2.3 Arrays Based Genotyping
5 Statistical Analysis for Association Mapping
5.1 Basic Models and Cofactors Used in Association Analysis
5.2 Improvements over MLM and the Need for Better Regression Approaches or Data Reduction Techniques in AM
5.3 Multi-locus Models
6 Other Considerations in GWAs Methods
6.1 Marker Imputation
6.2 False Discovery Rate Correction
6.3 Understanding GWAS Results
7 Application of GWAS in Plants
8 Way Forward
References
Chapter 7: Meta-analysis of Mapping Studies: Integrating QTLs Towards Candidate Gene Discovery
1 Introduction
2 Need for Meta-QTL Analysis
2.1 Lack of Robustness of Individual QTLs
2.2 Redundancy of QTL Information
2.3 Common QTLs Across Different Mapping Populations
3 Platforms for Meta-analysis
3.1 Meta-Essentials
3.2 Stata
3.3 MetaFor
4 Platforms for Meta-analysis of QTLs
4.1 MetaQTL
4.2 BioMercator
5 Biomercator: A Case Study
5.1 Data Collection and Input File Preparation
5.2 Detailed Format of QTL File
5.3 Detailed Format of Map File
5.4 Connectivity of Maps and Visualization of Common Markers: Infomap and MMapView
5.5 Consensus Map
5.6 Projection of QTLs
5.7 Meta-QTL Analysis
5.8 Gerber and Goffinet Algorithm
5.9 Veyrieras Algorithm
5.10 Output and Its Interpretation
6 Meta-QTL Analysis: Way Forward
6.1 Validation of Meta-QTLs and Prediction of Candidate Genes
6.2 Fine Mapping of the Interval and Development of Functional Markers
6.3 Integration with Expression Dataset to Identify Promising Candidate Genes
6.4 Haplotype Analysis and Functional Validation of Candidate Genes
7 Conclusion
References
Chapter 8: Role of Databases and Bioinformatics Tools in Crop Improvement
1 Introduction
2 Role of Bioinformatics in Crop Improvement
3 Computational Biological Tools and Databases for Crop Improvement
3.1 Software and Tools
3.2 Biological Databases
4 Future Perspectives
5 Conclusion
References
Chapter 9: Overview of the Bioinformatics Databases and Tools for Genome Research and Crop Improvement
1 Introduction
2 Generic Databases
3 Crop Specific Databases
3.1 Rice Genome Databases
3.2 Maize Genomic Databases
3.3 Wheat Genome Databases
3.4 Other Plant Genome Databases
4 Genomics Tools for Crop Improvements
4.1 Genome Assembly Tools
4.1.1 De Novo Genome Assembly
4.1.2 Reference-Based Genome Assembly Tools
4.2 SNP/Indels Identification Tools
4.3 Tool for Candidate Gene Association Studies
4.3.1 Multiple Sequence Alignment
4.3.2 Haplotyping
4.3.3 Association Studies
5 Conclusion
References
Chapter 10: Public Domain Databases: A Gold Mine for Identification and Genome Reconstruction of Plant Viruses and Viroids
1 Plant Viruses/Viroids: An Overview
2 NGS Vs Traditional Virus Detection Methods
3 Sampling Factors that Influence Plant Virus Identification
4 Library Preparation and Sequencing for Plant Virome Studies
4.1 Nucleic Acid Pool and Nucleic Acid Enrichment Method
4.2 Library Type and Sequencing Platforms
5 BioinformaticAnalysis of NGS Data for Plant Virus Detection
5.1 Pre-processing of Raw Reads
5.2 Assembly of Clean Reads
5.3 Analysis of Assembled Contigs for Virus Identification
5.4 Viral Genome Reconstruction
5.5 Bioinformatic Analysis of Recovered Viral Genomes
6 Advantages and Disadvantages in Using Public Domain Datasets for Plant Virus Identification
7 Conclusion and Future Prospects
References
Chapter 11: Tree Genome Databases: A New Era in the Development of Cyber-Infrastructures for Forest Trees
1 Introduction
2 Genomic Databases: An Overview
3 Status of Forest Tree Genomics Research
4 Cyber-Infrastructures for Forest Trees
4.1 Generalized Databases
4.1.1 TreeGenes
4.1.2 Hardwood Genome Project
4.1.3 Plant Genome Integrative Explorer
4.1.4 Genome Database for Rosaceae and Phytozome
4.2 Specialized Databases
5 Submission Standards for Tree Genome Databases
6 Prospects of Comparative Genomic Approaches in Forest Trees
7 Challenges in Forest Tree Genomic Research
8 Conclusive Remarks
References
Chapter 12: Development of Biological Databases for Genomic Research
1 Introduction
2 Types of Biological Databases
2.1 Primary Databases
2.2 Secondary Databases
2.3 Composite Databases
3 Designing a Biological Database
3.1 Biological Database Development Steps
4 Multi-Criteria-Based Sorting
5 Conclusion
References
Chapter 13: Artificial Intelligence in Genomic Studies
1 Introduction
1.1 KNN
1.2 Decision Tree
1.2.1 Advantages
1.2.2 Disadvantages
1.3 Naive Bayes
1.3.1 Bayes´ Theorem
1.3.2 Working of Naive Bayes Model
1.3.3 Advantages
1.3.4 Disadvantages
1.4 Artificial Neural Network
1.5 CNN
1.6 Association Rule Mining
2 Application of Artificial Intelligence in Genomics
2.1 Genome-Wide Association Studies
2.2 DNA Sequence Data Mining
2.2.1 DNA Sequence Alignment
2.2.2 DNA Classification
2.2.3 DNA Sequence Clustering
2.2.4 DNA Sequence Pattern Mining
2.3 Protein Structure Prediction
2.4 Protein Function Prediction
2.5 Identifying the Genes and QTL for Crop Improvement
2.6 Predicting ncRNA-Protein Interactions
2.7 Genomic Prediction
3 Case Studies
3.1 Case Study 1: Gene Expression Data Classification Using Support Vector Machine (SVM)- [15]
3.2 Case Study 2: ASRmiRNA: Abiotic Stress-Responsive miRNA Prediction in Plants by Using Machine Learning Algorithms with Pse...
4 Future Aspects of Artificial Intelligence in Different Types of Genomics
4.1 Structural Genomics
4.2 Functional Genomics
4.3 Regulatory Genomics
5 Conclusion
References
Chapter 14: Basics of the Molecular Biology: From Genes to Its Function
1 Introduction
2 Basics of Molecular Biology
2.1 Major Biomolecules in Molecular Biology
3 DNA (Deoxyribonucleic Acid)
3.1 Physical Properties of DNA [4]
4 RNA (Ribonucleic Acid)
4.1 Messenger RNA (mRNA)
4.2 Ribosomal RNA (rRNA)
4.3 Transfer RNA (tRNA)
4.4 Small and Long Non-coding RNA
5 Protein
5.1 Primary Structure
5.2 Secondary Structure
5.3 Tertiary Structure
5.4 Quaternary Structure
6 The Central Dogma
7 Genes and Alleles
8 Molecular Biology Tools
9 Applications of Basic Knowledge Related to Molecular Biology for Biologists and Plant Breeders
9.1 Forward, Reverse and Mutation Breeding
9.2 Genome-Wide Association Studies (GWAS)
10 Conclusion
References