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Bioinformatics for Plant Research and Crop Breeding

1. Aufl.

von: Jen-Tsung Chen
165,99 €
Verlag:	Wiley
Format:	EPUB
Veröffentl.:	22.07.2024
ISBN/EAN:	9781394209958
Sprache:	englisch
Anzahl Seiten:	608

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Titelbeschreibung

Explore and advance bioinformatics and systems biology tools for crop breeding programs in this practical resource for researchers 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: <ul><li>A focus on emerging trends in plant science, sustainable agriculture, and global food security </li><li>Detailed discussion of topics including plant diversity, plant stresses, nanotechnology in agriculture, and many others</li><li>Applications incorporating artificial intelligence, machine learning, deep learning and more</li></ul> 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.

Inhaltsverzeichnis

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

Autorenportrait

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.