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List of Contributors xxi Preface xxvii 1 Bioinformatics as a Powerful Tool to Foster Plant Science Research and Crop Breeding Through Its Involvement in a Multidisciplinary Research Activity 1 Jemaa Essemine, Zhan Xu, Jen-Tsung Chen, and Mingnan Qu
1.1 Introduction 1
1.2 Bioinformatics as a Powerful Tool for Big Data Analysis in Plant Science 3
1.3 Role of Bioinformatics in Trait Mapping 3
1.4 Bioinformatics in Molecular Biology 3
1.5 Role of Bioinformatics in Genetic Variation 4
1.6 Bioinformatics in Genome-wide Association Studies (GWAS) 4
1.7 Implication of Bioinformatics in "Omics" 5
1.8 Bioinformatics in Computational Biology and Evolutionary Studies 5
1.9 Role of Bioinformatics in Transcriptomics 6
1.10 Implication of Bioinformatics in Next-generation Sequencing (NGS) Analysis 6
1.11 Implication of Bioinformatics in Metabolomics 7
1.12 Bioinformatics and Epigenetics 8
1.13 Involvement of Bioinformatics in Synthetic Biology 9
1.14 How Can Bioinformatics Promote Plant Biotechnology? 9
1.15 Bioinformatics Use in Biotic and Abiotic Stress Management 10
1.16 Bioinformatics for the Investigation of Plant Resistance to Pathogens 11
1.17 Bioinformatics in Crop Breeding and Improvement 12
1.18 Bioinformatics Impacts on Plant Science 13
1.19 Application of Bioinformatics in Plant Breeding Programs 13
1.20 Conclusion 14 References 15 2 Bioinformatics for Molecular Breeding and Enhanced Crop Performance: Applications and Perspectives 21 Rahul Lahu Chavhan, Vidya Ramesh Hinge, Dipti Jayvantrao Wankhade, Abhijeet Subhash Deshmukh, Nagrani Mahajan, and Ulhas Sopanrao Kadam
2.1 Introduction 21
2.2 Data Management and Integration 22
2.3 Genomic Resources for Plant Breeding 26
2.4 Application of Bioinformatics, Genomics, and Proteomics in Crop Improvement and Breeding 45
2.5 Challenges and Future Directions 61
2.6 Conclusions 63 References 63 3 Multi-omics: An Advanced Bioinformatics Approach for Crop Improvement in Agriculture 75 Vinay Kumar Dhiman, Devendra Singh, Vivek Kumar Dhiman, and Himanshu Pandey
3.1 Multi-omics: A Boon to Crop Improvement 75
3.2 Genomics: Unlocking the Crop Genome 77
3.3 Metabolomics: Profiling the Crop''s Metabolic Processes 89
3.4 Phenomics 90
3.5 Ionomics 92
3.6 Omics-Assisted Breeding: Accelerating Crop Improvement 92
3.7 Conclusion and Future Perspectives 92 References 94 4 Genetic Mapping of Valued Genes with Significant Traits in Crop Plants: Basic Principles, Current Practices, and Future Perspectives 99 Prasanta Kumar Majhi, Akansha Guru, Suma C. Mogali, Prachi Pattnaik, Ritik Digamber Bisane, Lopamudra Singha, Partha Pratim Behera, and Prateek Ranjan Behera
4.1 Introduction 99
4.2 Quantitative Trait Loci (QTLs) and Genetic Mapping of Traits 101
4.3 The Fundamentals of the QTL Mapping Approach 102
4.4 Mapping Populations Used in QTL Mapping Experiments 104
4.5 Molecular Markers for QTL Mapping 119
4.6 Statistical Approaches for Detection of QTLs 121
4.7 Software Used for QTL Mapping 124
4.8 QTLs and the Signature of Selection 125
4.9 Factors Affecting the Power of QTL Mapping 125
4.10 Merits of QTL Mapping 128
4.11 Demerits of QTL Mapping 128
4.12 Conclusion and Way Forward 129 References 130 5 Basic Bioinformatics for Identification and Analysis of Candidate Genes in Plants Toward Crop Improvement 135 Sadhana Singh
5.1 Introduction 135
5.2 Candidate Genes such as Transcription Factors and Gene Families 137
5.3 Methods 140
5.4 Conclusion 154 References 155 6 Exploring Machine Learning Algorithms for Gene Function Prediction in Crops 159 Ruchi JakhmolaMani, Sonali, Aniket Pandey, Dhananjay Raturi, Rishita Singh, Kusala Vanam, Manish D, Ritu Chauhan, Deepshikha Pande Katare, Potshangbam Nongdam, and Angamba Meetei Potshangbam
6.1 Introduction 159
6.2 Computational Methods for Gene Function Prediction 164
6.3 Machine Learning and Crop Improvement 167
6.4 Experiment 173
6.5 Case Studies and Success Stories 176
6.6 Challenges and Future Directions 178 References 180 7 Omics and Bioinformatics Approaches for Abiotic Stress Tolerance in Plants 185 Santanu Samanta and Aryadeep Roychoudhury
7.1 Introduction 185
7.2 Genomic Approaches 186
7.3 Transcriptomics Approaches 189
7.4 Proteomics Approaches 191
7.5 Metabolomics Approaches 194
7.6 Bioinformatics Approaches 196
7.7 Concluding Remarks 197 Acknowledgments 198 References 198 8 Bioinformatics Approaches for Unraveling the Complexities of Plant Stress Physiology 209 Sneha Murmu, Himanshushekhar Chaurasia, Ipsita Samal, Tanmaya Kumar Bhoi, and Asit Kumar Pradhan
8.1 Introduction 209
8.2 Understanding Plant Stress Response Mechanisms 210
8.3 Genome and Transcriptome Analysis for Plant Stress Physiology 212
8.4 Proteomics and Metabolomics Approaches 216
8.5 Data Integration and Systems Biology Approaches 220
8.6 Bioinformatics Resources for Plant Stress 221
8.7 Conclusion 226 References 226 9 Bioinformatics Tools for Assessing Drought Stress Tolerance in Crops 233 Nageswara Rao Reddy Neelapu and Kolluru Viswanatha Chaitanya
9.1 Introduction 233
9.2 Bioinformatics for Plant Research and Crop Breeding 234
9.3 Genomics and Drought Stress Tolerance 234
9.4 Transcriptome Analysis for the Drought Stress Tolerance 236
9.5 Proteome and Drought Stress 239
9.6 Metabolomics and Drought Stress Tolerance 241
9.7 Phenome and Drought Stress 242
9.8 Future of the Omics technologies 244
9.9 Conclusions 245 References 246 10 Bioinformatics Tools and Resources for Plant Transcriptomics: Challenges and Opportunities 251 Sona Charles and Merlin Lopus
10.1 Introduction 251
10.2 Evolution of Transcriptomic Technologies 252
10.3 Steps in Transcriptomic Data Analysis 254
10.4 R/Bioconductor Packages for Transcriptomic Analysis 259
10.5 Galaxy Server for Transcriptome Analysis 260
10.6 Stress Transcriptomics - A Case Study 260
10.7 Conclusion and Way Forward 262 References 262 11 Development of a Core Set from Large Germplasm Collections in Genebank 269 Pradeep Ruperao
11.1 Introduction 269
11.2 Developing a Core Collection 270
11.3 Constructing a Core Collection 270
11.4 Assessing the Core Collections 275
11.5 Conclusion and Future Considerations 278 References 280 12 Bioinformatics Approaches to Determine Plant microRNA Targets 283 Shree Prakash Pandey
12.1 Introduction 283
12.2 Characteristic Features and Principles of miRNA-targeting in Plants 285
12.3 Tools for miRNA Target Prediction in Plants 288
12.4 Bioinformatics Identification of miRNA and mRNA at a Genome-scale 291
12.5 Conclusion 292 References 293 13 Machine Learning for the Discovery of DNA-binding Proteins in Plants 299 Upendra Kumar Pradhan, Prabina Kumar Meher, and Pushpendra Kumar Gupta
13.1 Introduction 299
13.2 Steps Involved in Identification of DBPs Using Machine Learning 301
13.3 Assessment of Learning Algorithms for DBP Prediction Using Sequence- and PSSM-derived Features 311
13.4 Evaluation of Existing Tools for DBP Prediction in Plants 313
13.5 Conclusion and Future Perspectives 314 References 315 14 Bioinformatics for Gene Identification and Crop Improvement in Wheat 321 Pushpendra Kumar Gupta, Jyoti Chaudhary, and Tinku Gautam
14.1 Introduction 321
14.2 Databases and Tools for Individual Genes and Proteins 321
14.3 Identification/Characterization of Genes/Gene Families at the DNA Level 325
14.4 Characterization of Genes at the Protein Level 328
14.5 Phylogenetic Analysis 331
14.6 Present Status of Wheat Genes Identified in silico 331
14.7 Utility of Predicted Genes for Crop Improvement 337
14.8 Conclusion and Prospects 340 References 340 15 Bioinformatics for Analyzing the Role of Epigenetics in Plant Disease Resistance 351 Kalpana Singh, Harindra Singh Balyan, and Pushpendra Kumar Gupta
15.1 Introduction 351
15.2 Histone Modifications 351
15.3 Chromatin Accessibility 357
15.4 DNA Methylation 360
15.5 Noncoding RNAs (miRNAs, lncRNA, circRNA) 365
15.6 Conclusions and Future Perspectives 370 References 371 Weblinks 390 16 The Evolution of Auxin-Binding Protein 1 391 Siarhei A. Dabravolski and Stanislav V. Isayenkov
16.1 Abundance of Auxin and Auxin-binding Proteins in Nature 391
16.2 Auxin in Plants 392
16.3 Domain Organization 393
16.4 ABP1 Active Sites/Structure/Sequence Analysis 395
16.5 ABP1 Evolution 398
16.6 Future Prospective 403
16.7 Conclusion 405 References 405 17 Exploring the Potential of Molecular Docking and In Silico Studies in Secondary Metabolite and Bioactive Compound Discovery for Plant Research 413 Amine Elbouzidi, Mohamed Taibi, and Mohamed Addi
17.1 Introduction 413
17.2 Importance of Structure-based Drug Design from Natural Sources 415