Plant biology and crop breeding have produced an immense amount of data in recent years, from genomics to interactome and beyond. Bioinformatics tools, which aim at analyzing the vast quantities of data produced by biological research and processes, have developed at a rapid pace to meet the challenges of this vast data trove. The resulting field of bioinformatics and systems biology is producing increasingly rich and transformative research.
Bioinformatics for Plant Research and Crop Breeding offers an overview of this field, its recent advances, and its wider applications. Drawing on a range of analytical and data-science tools, its foundation on an in-silico platform acquired multi-omics makes it indispensable for scientists and researchers alike. It promises to become ever more relevant as new techniques for generating and organizing data continue to transform the field.
Bioinformatics for Plant Research and Crop Breeding readers will also find: - A focus on emerging trends in plant science, sustainable agriculture, and global food security - Detailed discussion of topics including plant diversity, plant stresses, nanotechnology in agriculture, and many others- Applications incorporating artificial intelligence, machine learning, deep learning and more
Bioinformatics for Plant Research and Crop Breeding is ideal for researchers and scientists interested in the potential of OMICs, and bioinformatic tools to aid and develop crop improvement programs.
Table of Contents
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
