{"product_id":"bioinformatics-for-plant-research-and-crop-breeding-isbn-9781394209934","title":"Bioinformatics for Plant Research and Crop Breeding","description":"\u003cp\u003e\u003cb\u003eExplore and advance bioinformatics and systems biology tools for crop breeding programs in this practical resource for researchers\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003ePlant 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eBioinformatics for Plant Research and Crop Breeding \u003c\/i\u003eoffers 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eBioinformatics for Plant Research and Crop Breeding \u003c\/i\u003ereaders will also find: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eA focus on emerging trends in plant science, sustainable agriculture, and global food security \u003c\/li\u003e\n\u003cli\u003eDetailed discussion of topics including plant diversity, plant stresses, nanotechnology in agriculture, and many others\u003c\/li\u003e\n\u003cli\u003eApplications incorporating artificial intelligence, machine learning, deep learning and more\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eBioinformatics for Plant Research and Crop Breeding\u003c\/i\u003e is ideal for researchers and scientists interested in the potential of OMICs, and bioinformatic tools to aid and develop crop improvement programs. \u003c\/p\u003e\u003cp\u003eList of Contributors xxi\u003c\/p\u003e \u003cp\u003ePreface xxvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Bioinformatics as a Powerful Tool to Foster Plant Science Research and Crop Breeding Through Its Involvement in a Multidisciplinary Research Activity 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJemaa Essemine, Zhan Xu, Jen-Tsung Chen, and Mingnan Qu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Bioinformatics as a Powerful Tool for Big Data Analysis in Plant Science 3\u003c\/p\u003e \u003cp\u003e1.3 Role of Bioinformatics in Trait Mapping 3\u003c\/p\u003e \u003cp\u003e1.4 Bioinformatics in Molecular Biology 3\u003c\/p\u003e \u003cp\u003e1.5 Role of Bioinformatics in Genetic Variation 4\u003c\/p\u003e \u003cp\u003e1.6 Bioinformatics in Genome-wide Association Studies (GWAS) 4\u003c\/p\u003e \u003cp\u003e1.7 Implication of Bioinformatics in “Omics” 5\u003c\/p\u003e \u003cp\u003e1.8 Bioinformatics in Computational Biology and Evolutionary Studies 5\u003c\/p\u003e \u003cp\u003e1.9 Role of Bioinformatics in Transcriptomics 6\u003c\/p\u003e \u003cp\u003e1.10 Implication of Bioinformatics in Next-generation Sequencing (NGS) Analysis 6\u003c\/p\u003e \u003cp\u003e1.11 Implication of Bioinformatics in Metabolomics 7\u003c\/p\u003e \u003cp\u003e1.12 Bioinformatics and Epigenetics 8\u003c\/p\u003e \u003cp\u003e1.13 Involvement of Bioinformatics in Synthetic Biology 9\u003c\/p\u003e \u003cp\u003e1.14 How Can Bioinformatics Promote Plant Biotechnology? 9\u003c\/p\u003e \u003cp\u003e1.15 Bioinformatics Use in Biotic and Abiotic Stress Management 10\u003c\/p\u003e \u003cp\u003e1.16 Bioinformatics for the Investigation of Plant Resistance to Pathogens 11\u003c\/p\u003e \u003cp\u003e1.17 Bioinformatics in Crop Breeding and Improvement 12\u003c\/p\u003e \u003cp\u003e1.18 Bioinformatics Impacts on Plant Science 13\u003c\/p\u003e \u003cp\u003e1.19 Application of Bioinformatics in Plant Breeding Programs 13\u003c\/p\u003e \u003cp\u003e1.20 Conclusion 14\u003c\/p\u003e \u003cp\u003eReferences 15\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Bioinformatics for Molecular Breeding and Enhanced Crop Performance: Applications and Perspectives 21\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRahul Lahu Chavhan, Vidya Ramesh Hinge, Dipti Jayvantrao Wankhade, Abhijeet Subhash Deshmukh, Nagrani Mahajan, and Ulhas Sopanrao Kadam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 21\u003c\/p\u003e \u003cp\u003e2.2 Data Management and Integration 22\u003c\/p\u003e \u003cp\u003e2.3 Genomic Resources for Plant Breeding 26\u003c\/p\u003e \u003cp\u003e2.4 Application of Bioinformatics, Genomics, and Proteomics in Crop Improvement and Breeding 45\u003c\/p\u003e \u003cp\u003e2.5 Challenges and Future Directions 61\u003c\/p\u003e \u003cp\u003e2.6 Conclusions 63\u003c\/p\u003e \u003cp\u003eReferences 63\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Multi-omics: An Advanced Bioinformatics Approach for Crop Improvement in Agriculture 75\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eVinay Kumar Dhiman, Devendra Singh, Vivek Kumar Dhiman, and Himanshu Pandey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Multi-omics: A Boon to Crop Improvement 75\u003c\/p\u003e \u003cp\u003e3.2 Genomics: Unlocking the Crop Genome 77\u003c\/p\u003e \u003cp\u003e3.3 Metabolomics: Profiling the Crop’s Metabolic Processes 89\u003c\/p\u003e \u003cp\u003e3.4 Phenomics 90\u003c\/p\u003e \u003cp\u003e3.5 Ionomics 92\u003c\/p\u003e \u003cp\u003e3.6 Omics-Assisted Breeding: Accelerating Crop Improvement 92\u003c\/p\u003e \u003cp\u003e3.7 Conclusion and Future Perspectives 92\u003c\/p\u003e \u003cp\u003eReferences 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Genetic Mapping of Valued Genes with Significant Traits in Crop Plants: Basic Principles, Current Practices, and Future Perspectives 99\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePrasanta Kumar Majhi, Akansha Guru, Suma C. Mogali, Prachi Pattnaik, Ritik Digamber Bisane, Lopamudra Singha, Partha Pratim Behera, and Prateek Ranjan Behera\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 99\u003c\/p\u003e \u003cp\u003e4.2 Quantitative Trait Loci (QTLs) and Genetic Mapping of Traits 101\u003c\/p\u003e \u003cp\u003e4.3 The Fundamentals of the QTL Mapping Approach 102\u003c\/p\u003e \u003cp\u003e4.4 Mapping Populations Used in QTL Mapping Experiments 104\u003c\/p\u003e \u003cp\u003e4.5 Molecular Markers for QTL Mapping 119\u003c\/p\u003e \u003cp\u003e4.6 Statistical Approaches for Detection of QTLs 121\u003c\/p\u003e \u003cp\u003e4.7 Software Used for QTL Mapping 124\u003c\/p\u003e \u003cp\u003e4.8 QTLs and the Signature of Selection 125\u003c\/p\u003e \u003cp\u003e4.9 Factors Affecting the Power of QTL Mapping 125\u003c\/p\u003e \u003cp\u003e4.10 Merits of QTL Mapping 128\u003c\/p\u003e \u003cp\u003e4.11 Demerits of QTL Mapping 128\u003c\/p\u003e \u003cp\u003e4.12 Conclusion and Way Forward 129\u003c\/p\u003e \u003cp\u003eReferences 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Basic Bioinformatics for Identification and Analysis of Candidate Genes in Plants Toward Crop Improvement 135\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSadhana Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 135\u003c\/p\u003e \u003cp\u003e5.2 Candidate Genes such as Transcription Factors and Gene Families 137\u003c\/p\u003e \u003cp\u003e5.3 Methods 140\u003c\/p\u003e \u003cp\u003e5.4 Conclusion 154\u003c\/p\u003e \u003cp\u003eReferences 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Exploring Machine Learning Algorithms for Gene Function Prediction in Crops 159\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRuchi Jakhmola‐Mani, Sonali, Aniket Pandey, Dhananjay Raturi, Rishita Singh, Kusala Vanam, Manish D, Ritu Chauhan, Deepshikha Pande Katare, Potshangbam Nongdam, and Angamba Meetei Potshangbam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 159\u003c\/p\u003e \u003cp\u003e6.2 Computational Methods for Gene Function Prediction 164\u003c\/p\u003e \u003cp\u003e6.3 Machine Learning and Crop Improvement 167\u003c\/p\u003e \u003cp\u003e6.4 Experiment 173\u003c\/p\u003e \u003cp\u003e6.5 Case Studies and Success Stories 176\u003c\/p\u003e \u003cp\u003e6.6 Challenges and Future Directions 178\u003c\/p\u003e \u003cp\u003eReferences 180\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Omics and Bioinformatics Approaches for Abiotic Stress Tolerance in Plants 185\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSantanu Samanta and Aryadeep Roychoudhury\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 185\u003c\/p\u003e \u003cp\u003e7.2 Genomic Approaches 186\u003c\/p\u003e \u003cp\u003e7.3 Transcriptomics Approaches 189\u003c\/p\u003e \u003cp\u003e7.4 Proteomics Approaches 191\u003c\/p\u003e \u003cp\u003e7.5 Metabolomics Approaches 194\u003c\/p\u003e \u003cp\u003e7.6 Bioinformatics Approaches 196\u003c\/p\u003e \u003cp\u003e7.7 Concluding Remarks 197\u003c\/p\u003e \u003cp\u003eAcknowledgments 198\u003c\/p\u003e \u003cp\u003eReferences 198\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Bioinformatics Approaches for Unraveling the Complexities of Plant Stress Physiology 209\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSneha Murmu, Himanshushekhar Chaurasia, Ipsita Samal, Tanmaya Kumar Bhoi, and Asit Kumar Pradhan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 209\u003c\/p\u003e \u003cp\u003e8.2 Understanding Plant Stress Response Mechanisms 210\u003c\/p\u003e \u003cp\u003e8.3 Genome and Transcriptome Analysis for Plant Stress Physiology 212\u003c\/p\u003e \u003cp\u003e8.4 Proteomics and Metabolomics Approaches 216\u003c\/p\u003e \u003cp\u003e8.5 Data Integration and Systems Biology Approaches 220\u003c\/p\u003e \u003cp\u003e8.6 Bioinformatics Resources for Plant Stress 221\u003c\/p\u003e \u003cp\u003e8.7 Conclusion 226\u003c\/p\u003e \u003cp\u003eReferences 226\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Bioinformatics Tools for Assessing Drought Stress Tolerance in Crops 233\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNageswara Rao Reddy Neelapu and Kolluru Viswanatha Chaitanya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 233\u003c\/p\u003e \u003cp\u003e9.2 Bioinformatics for Plant Research and Crop Breeding 234\u003c\/p\u003e \u003cp\u003e9.3 Genomics and Drought Stress Tolerance 234\u003c\/p\u003e \u003cp\u003e9.4 Transcriptome Analysis for the Drought Stress Tolerance 236\u003c\/p\u003e \u003cp\u003e9.5 Proteome and Drought Stress 239\u003c\/p\u003e \u003cp\u003e9.6 Metabolomics and Drought Stress Tolerance 241\u003c\/p\u003e \u003cp\u003e9.7 Phenome and Drought Stress 242\u003c\/p\u003e \u003cp\u003e9.8 Future of the Omics technologies 244\u003c\/p\u003e \u003cp\u003e9.9 Conclusions 245\u003c\/p\u003e \u003cp\u003eReferences 246\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Bioinformatics Tools and Resources for Plant Transcriptomics: Challenges and Opportunities 251\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSona Charles and Merlin Lopus\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 251\u003c\/p\u003e \u003cp\u003e10.2 Evolution of Transcriptomic Technologies 252\u003c\/p\u003e \u003cp\u003e10.3 Steps in Transcriptomic Data Analysis 254\u003c\/p\u003e \u003cp\u003e10.4 R\/Bioconductor Packages for Transcriptomic Analysis 259\u003c\/p\u003e \u003cp\u003e10.5 Galaxy Server for Transcriptome Analysis 260\u003c\/p\u003e \u003cp\u003e10.6 Stress Transcriptomics – A Case Study 260\u003c\/p\u003e \u003cp\u003e10.7 Conclusion and Way Forward 262\u003c\/p\u003e \u003cp\u003eReferences 262\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Development of a Core Set from Large Germplasm Collections in Genebank 269\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePradeep Ruperao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 269\u003c\/p\u003e \u003cp\u003e11.2 Developing a Core Collection 270\u003c\/p\u003e \u003cp\u003e11.3 Constructing a Core Collection 270\u003c\/p\u003e \u003cp\u003e11.4 Assessing the Core Collections 275\u003c\/p\u003e \u003cp\u003e11.5 Conclusion and Future Considerations 278\u003c\/p\u003e \u003cp\u003eReferences 280\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Bioinformatics Approaches to Determine Plant microRNA Targets 283\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eShree Prakash Pandey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 283\u003c\/p\u003e \u003cp\u003e12.2 Characteristic Features and Principles of miRNA-targeting in Plants 285\u003c\/p\u003e \u003cp\u003e12.3 Tools for miRNA Target Prediction in Plants 288\u003c\/p\u003e \u003cp\u003e12.4 Bioinformatics Identification of miRNA and mRNA at a Genome-scale 291\u003c\/p\u003e \u003cp\u003e12.5 Conclusion 292\u003c\/p\u003e \u003cp\u003eReferences 293\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Machine Learning for the Discovery of DNA-binding Proteins in Plants 299\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eUpendra Kumar Pradhan, Prabina Kumar Meher, and Pushpendra Kumar Gupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 299\u003c\/p\u003e \u003cp\u003e13.2 Steps Involved in Identification of DBPs Using Machine Learning 301\u003c\/p\u003e \u003cp\u003e13.3 Assessment of Learning Algorithms for DBP Prediction Using Sequence- and PSSM-derived Features 311\u003c\/p\u003e \u003cp\u003e13.4 Evaluation of Existing Tools for DBP Prediction in Plants 313\u003c\/p\u003e \u003cp\u003e13.5 Conclusion and Future Perspectives 314\u003c\/p\u003e \u003cp\u003eReferences 315\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Bioinformatics for Gene Identification and Crop Improvement in Wheat 321\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePushpendra Kumar Gupta, Jyoti Chaudhary, and Tinku Gautam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 321\u003c\/p\u003e \u003cp\u003e14.2 Databases and Tools for Individual Genes and Proteins 321\u003c\/p\u003e \u003cp\u003e14.3 Identification\/Characterization of Genes\/Gene Families at the DNA Level 325\u003c\/p\u003e \u003cp\u003e14.4 Characterization of Genes at the Protein Level 328\u003c\/p\u003e \u003cp\u003e14.5 Phylogenetic Analysis 331\u003c\/p\u003e \u003cp\u003e14.6 Present Status of Wheat Genes Identified in silico 331\u003c\/p\u003e \u003cp\u003e14.7 Utility of Predicted Genes for Crop Improvement 337\u003c\/p\u003e \u003cp\u003e14.8 Conclusion and Prospects 340\u003c\/p\u003e \u003cp\u003eReferences 340\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Bioinformatics for Analyzing the Role of Epigenetics in Plant Disease Resistance 351\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKalpana Singh, Harindra Singh Balyan, and Pushpendra Kumar Gupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 351\u003c\/p\u003e \u003cp\u003e15.2 Histone Modifications 351\u003c\/p\u003e \u003cp\u003e15.3 Chromatin Accessibility 357\u003c\/p\u003e \u003cp\u003e15.4 DNA Methylation 360\u003c\/p\u003e \u003cp\u003e15.5 Noncoding RNAs (miRNAs, lncRNA, circRNA) 365\u003c\/p\u003e \u003cp\u003e15.6 Conclusions and Future Perspectives 370\u003c\/p\u003e \u003cp\u003eReferences 371\u003c\/p\u003e \u003cp\u003eWeblinks 390\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 The Evolution of Auxin-Binding Protein 1 391\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSiarhei A. Dabravolski and Stanislav V. Isayenkov\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Abundance of Auxin and Auxin-binding Proteins in Nature 391\u003c\/p\u003e \u003cp\u003e16.2 Auxin in Plants 392\u003c\/p\u003e \u003cp\u003e16.3 Domain Organization 393\u003c\/p\u003e \u003cp\u003e16.4 ABP1 Active Sites\/Structure\/Sequence Analysis 395\u003c\/p\u003e \u003cp\u003e16.5 ABP1 Evolution 398\u003c\/p\u003e \u003cp\u003e16.6 Future Prospective 403\u003c\/p\u003e \u003cp\u003e16.7 Conclusion 405\u003c\/p\u003e \u003cp\u003eReferences 405\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Exploring the Potential of Molecular Docking and In Silico Studies in Secondary Metabolite and Bioactive Compound Discovery for Plant Research 413\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAmine Elbouzidi, Mohamed Taibi, and Mohamed Addi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 413\u003c\/p\u003e \u003cp\u003e17.2 Importance of Structure-based Drug Design from Natural Sources 415\u003c\/p\u003e \u003cp\u003e17.3 Molecular Docking as a Key Component of SBDD: A Bridge Between Computational and Experimental Approaches 417\u003c\/p\u003e \u003cp\u003e17.4 Molecular Docking and Natural Product Database 419\u003c\/p\u003e \u003cp\u003e17.5 Case Studies: Successful Applications of In Silico Molecular Docking in Plant Research for Diverse Applications 423\u003c\/p\u003e \u003cp\u003e17.6 Concluding Remarks and Future Considerations 429\u003c\/p\u003e \u003cp\u003eReferences 430\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Exploring Secondary Metabolites in Plants Through Bioinformatics 435\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSneha Murmu, Ritwika Das, Bharati Pandey, Soumya Sharma, and Mohammad Samir Farooqi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 435\u003c\/p\u003e \u003cp\u003e18.2 Classification of Plant Secondary Metabolites 436\u003c\/p\u003e \u003cp\u003e18.3 Secondary Metabolites Pathways in Plants 438\u003c\/p\u003e \u003cp\u003e18.4 Mining of Omics Data 440\u003c\/p\u003e \u003cp\u003e18.5 Bioinformatics Tools for Analysis of Secondary Metabolites and Pathways 447\u003c\/p\u003e \u003cp\u003e18.6 Conclusion 452\u003c\/p\u003e \u003cp\u003eReferences 452\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Understanding Plant Secondary Metabolism Using Bioinformatics Tools: Recent Advances and Prospects 459\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDola Mukherjee and Ashutosh Mukherjee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 459\u003c\/p\u003e \u003cp\u003e19.2 Secondary Metabolic Gene Clusters 461\u003c\/p\u003e \u003cp\u003e19.3 Sequencing Techniques and Analytical Tools for Plant Metabolomics Study 462\u003c\/p\u003e \u003cp\u003e19.4 Bioinformatics Tools for the Elucidation of Secondary Metabolism in Plants 465\u003c\/p\u003e \u003cp\u003e19.5 Medicinal Plant Genome and\/or Metabolome Databases 465\u003c\/p\u003e \u003cp\u003e19.6 Automation of Natural Product Detection by Identification of Metabolic Gene Cluster 469\u003c\/p\u003e \u003cp\u003e19.7 The Big Data and Systems Biology Approach 470\u003c\/p\u003e \u003cp\u003e19.8 Application of Machine Learning in Plant Secondary Metabolism 471\u003c\/p\u003e \u003cp\u003e19.9 Artificial Intelligence (AI) 472\u003c\/p\u003e \u003cp\u003e19.10 Machine Learning (ML) 472\u003c\/p\u003e \u003cp\u003e19.11 Deep Learning (DL) 473\u003c\/p\u003e \u003cp\u003e19.12 Conclusion and Future Perspective 475\u003c\/p\u003e \u003cp\u003eReferences 477\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 An Appraisal of Flavonoids Through Bioinformatics 489\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eManoj Kumar Mishra and Vibha Pandey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Overview of Flavonoids 489\u003c\/p\u003e \u003cp\u003e20.2 Identification of Flavonoid Biosynthetic Genes and Enzymes by Computational Tools 490\u003c\/p\u003e \u003cp\u003e20.3 Prediction of the Potential Biological Activities of Flavonoids Based on Their Chemical Structure 494\u003c\/p\u003e \u003cp\u003e20.4 Chalcone Synthase 495\u003c\/p\u003e \u003cp\u003e20.5 Sequence Retrieval 496\u003c\/p\u003e \u003cp\u003e20.6 Localization 497\u003c\/p\u003e \u003cp\u003e20.7 Homology Search 497\u003c\/p\u003e \u003cp\u003e20.8 Conserved Domain 497\u003c\/p\u003e \u003cp\u003e20.9 Sequence Alignment and Phylogeny 498\u003c\/p\u003e \u003cp\u003e20.10 Chromosome Location 499\u003c\/p\u003e \u003cp\u003e20.11 Characterization 500\u003c\/p\u003e \u003cp\u003e20.12 Three-dimensional Structure 500\u003c\/p\u003e \u003cp\u003e20.13 Docking 501\u003c\/p\u003e \u003cp\u003e20.14 Conclusion and Prospects 501\u003c\/p\u003e \u003cp\u003eReferences 501\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Golden Opportunities: Harnessing Bioinformatics to Revolutionize Plant Research and Unleash the Power of Golden Rice in Crop Breeding 505\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePoulami Majumder\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 505\u003c\/p\u003e \u003cp\u003e21.2 Background 510\u003c\/p\u003e \u003cp\u003e21.3 Bioinformatics Tools and Resources for Plant Research 512\u003c\/p\u003e \u003cp\u003e21.4 Genetic Engineering and Breeding Strategies for Golden Rice 517\u003c\/p\u003e \u003cp\u003e21.5 Case Study: Development and Improvement of Golden Rice 523\u003c\/p\u003e \u003cp\u003e21.6 Bioinformatics-guided Identification of Target Genes for Provitamin A Enhancement 525\u003c\/p\u003e \u003cp\u003e21.7 Ethical and IP Issues 529\u003c\/p\u003e \u003cp\u003e21.8 Conclusion 533\u003c\/p\u003e \u003cp\u003eDeclaration 534\u003c\/p\u003e \u003cp\u003eReferences 535\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Going Wild: Genomics of Forest Plants and the Future of Crop Improvement 539\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAjinkya Bharatraj Patil, Debojyoti Kar, Sourav Datta, and Nagarjun Vijay\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 539\u003c\/p\u003e \u003cp\u003e22.2 Repetitive Genomic Elements as Phenotypic Trait Variation Machinery 542\u003c\/p\u003e \u003cp\u003e22.3 Naturally Acquired Traits Can Be Effectively Characterized Using Population Genomics 545\u003c\/p\u003e \u003cp\u003e22.4 Demographic Forces Are Crucial in Shaping Genome Dynamics 549\u003c\/p\u003e \u003cp\u003e22.5 Conclusion 552\u003c\/p\u003e \u003cp\u003eReferences 552\u003c\/p\u003e \u003cp\u003eIndex 559\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eJen-Tsung Chen, PhD,\u003c\/b\u003e 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.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eExplore and advance bioinformatics and systems biology tools for crop breeding programs in this practical resource for researchers\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003ePlant 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eBioinformatics for Plant Research and Crop Breeding \u003c\/i\u003eoffers 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eBioinformatics for Plant Research and Crop Breeding \u003c\/i\u003ereaders will also find: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eA focus on emerging trends in plant science, sustainable agriculture, and global food security \u003c\/li\u003e\n\u003cli\u003eDetailed discussion of topics including plant diversity, plant stresses, nanotechnology in agriculture, and many others\u003c\/li\u003e\n\u003cli\u003eApplications incorporating artificial intelligence, machine learning, deep learning and more\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eBioinformatics for Plant Research and Crop Breeding\u003c\/i\u003e is ideal for researchers and scientists interested in the potential of OMICs, and bioinformatic tools to aid and develop crop improvement programs.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988821197029,"sku":"NP9781394209934","price":200.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394209934.jpg?v=1761781708","url":"https:\/\/k12savings.com\/es\/products\/bioinformatics-for-plant-research-and-crop-breeding-isbn-9781394209934","provider":"K12savings","version":"1.0","type":"link"}