Bioinformatics for Plant Research and Crop Breeding

Jen-Tsung Chen, PhD, is currently a professor at the National University of Kaohsiung in Taiwan. His research interests include bioactive compounds, chromatography techniques, in vitro culture, medicinal plants, phytochemicals, plant physiology, and plant biotechnology.

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 Jakhmola‐Mani, 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

17.3 Molecular Docking as a Key Component of SBDD: A Bridge Between Computational and Experimental Approaches 417

17.4 Molecular Docking and Natural Product Database 419

17.5 Case Studies: Successful Applications of In Silico Molecular Docking in Plant Research for Diverse Applications 423

17.6 Concluding Remarks and Future Considerations 429

References 430

18 Exploring Secondary Metabolites in Plants Through Bioinformatics 435
Sneha Murmu, Ritwika Das, Bharati Pandey, Soumya Sharma, and Mohammad Samir Farooqi

18.1 Introduction 435

18.2 Classification of Plant Secondary Metabolites 436

18.3 Secondary Metabolites Pathways in Plants 438

18.4 Mining of Omics Data 440

18.5 Bioinformatics Tools for Analysis of Secondary Metabolites and Pathways 447

18.6 Conclusion 452

References 452

19 Understanding Plant Secondary Metabolism Using Bioinformatics Tools: Recent Advances and Prospects 459
Dola Mukherjee and Ashutosh Mukherjee

19.1 Introduction 459

19.2 Secondary Metabolic Gene Clusters 461

19.3 Sequencing Techniques and Analytical Tools for Plant Metabolomics Study 462

19.4 Bioinformatics Tools for the Elucidation of Secondary Metabolism in Plants 465

19.5 Medicinal Plant Genome and/or Metabolome Databases 465

19.6 Automation of Natural Product Detection by Identification of Metabolic Gene Cluster 469

19.7 The Big Data and Systems Biology Approach 470

19.8 Application of Machine Learning in Plant Secondary Metabolism 471

19.9 Artificial Intelligence (AI) 472

19.10 Machine Learning (ML) 472

19.11 Deep Learning (DL) 473

19.12 Conclusion and Future Perspective 475

References 477

20 An Appraisal of Flavonoids Through Bioinformatics 489
Manoj Kumar Mishra and Vibha Pandey

20.1 Overview of Flavonoids 489

20.2 Identification of Flavonoid Biosynthetic Genes and Enzymes by Computational Tools 490

20.3 Prediction of the Potential Biological Activities of Flavonoids Based on Their Chemical Structure 494

20.4 Chalcone Synthase 495

20.5 Sequence Retrieval 496

20.6 Localization 497

20.7 Homology Search 497

20.8 Conserved Domain 497

20.9 Sequence Alignment and Phylogeny 498

20.10 Chromosome Location 499

20.11 Characterization 500

20.12 Three-dimensional Structure 500

20.13 Docking 501

20.14 Conclusion and Prospects 501

References 501

21 Golden Opportunities: Harnessing Bioinformatics to Revolutionize Plant Research and Unleash the Power of Golden Rice in Crop Breeding 505
Poulami Majumder

21.1 Introduction 505

21.2 Background 510

21.3 Bioinformatics Tools and Resources for Plant Research 512

21.4 Genetic Engineering and Breeding Strategies for Golden Rice 517

21.5 Case Study: Development and Improvement of Golden Rice 523

21.6 Bioinformatics-guided Identification of Target Genes for Provitamin A Enhancement 525

21.7 Ethical and IP Issues 529

21.8 Conclusion 533

Declaration 534

References 535

22 Going Wild: Genomics of Forest Plants and the Future of Crop Improvement 539
Ajinkya Bharatraj Patil, Debojyoti Kar, Sourav Datta, and Nagarjun Vijay

22.1 Introduction 539

22.2 Repetitive Genomic Elements as Phenotypic Trait Variation Machinery 542

22.3 Naturally Acquired Traits Can Be Effectively Characterized Using Population Genomics 545

22.4 Demographic Forces Are Crucial in Shaping Genome Dynamics 549

22.5 Conclusion 552

References 552

Index 559