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 xxiPreface xxvii1 Bioinformatics as a Powerful Tool to Foster Plant Science Research and Crop Breeding Through Its Involvement in a Multidisciplinary Research Activity 1Jemaa Essemine, Zhan Xu, Jen-Tsung Chen, and Mingnan Qu1.1 Introduction 11.2 Bioinformatics as a Powerful Tool for Big Data Analysis in Plant Science 31.3 Role of Bioinformatics in Trait Mapping 31.4 Bioinformatics in Molecular Biology 31.5 Role of Bioinformatics in Genetic Variation 41.6 Bioinformatics in Genome-wide Association Studies (GWAS) 41.7 Implication of Bioinformatics in “Omics” 51.8 Bioinformatics in Computational Biology and Evolutionary Studies 51.9 Role of Bioinformatics in Transcriptomics 61.10 Implication of Bioinformatics in Next-generation Sequencing (NGS) Analysis 61.11 Implication of Bioinformatics in Metabolomics 71.12 Bioinformatics and Epigenetics 81.13 Involvement of Bioinformatics in Synthetic Biology 91.14 How Can Bioinformatics Promote Plant Biotechnology? 91.15 Bioinformatics Use in Biotic and Abiotic Stress Management 101.16 Bioinformatics for the Investigation of Plant Resistance to Pathogens 111.17 Bioinformatics in Crop Breeding and Improvement 121.18 Bioinformatics Impacts on Plant Science 131.19 Application of Bioinformatics in Plant Breeding Programs 131.20 Conclusion 14References 152 Bioinformatics for Molecular Breeding and Enhanced Crop Performance: Applications and Perspectives 21Rahul Lahu Chavhan, Vidya Ramesh Hinge, Dipti Jayvantrao Wankhade, Abhijeet Subhash Deshmukh, Nagrani Mahajan, and Ulhas Sopanrao Kadam2.1 Introduction 212.2 Data Management and Integration 222.3 Genomic Resources for Plant Breeding 262.4 Application of Bioinformatics, Genomics, and Proteomics in Crop Improvement and Breeding 452.5 Challenges and Future Directions 612.6 Conclusions 63References 633 Multi-omics: An Advanced Bioinformatics Approach for Crop Improvement in Agriculture 75Vinay Kumar Dhiman, Devendra Singh, Vivek Kumar Dhiman, and Himanshu Pandey3.1 Multi-omics: A Boon to Crop Improvement 753.2 Genomics: Unlocking the Crop Genome 773.3 Metabolomics: Profiling the Crop’s Metabolic Processes 893.4 Phenomics 903.5 Ionomics 923.6 Omics-Assisted Breeding: Accelerating Crop Improvement 923.7 Conclusion and Future Perspectives 92References 944 Genetic Mapping of Valued Genes with Significant Traits in Crop Plants: Basic Principles, Current Practices, and Future Perspectives 99Prasanta Kumar Majhi, Akansha Guru, Suma C. Mogali, Prachi Pattnaik, Ritik Digamber Bisane, Lopamudra Singha, Partha Pratim Behera, and Prateek Ranjan Behera4.1 Introduction 994.2 Quantitative Trait Loci (QTLs) and Genetic Mapping of Traits 1014.3 The Fundamentals of the QTL Mapping Approach 1024.4 Mapping Populations Used in QTL Mapping Experiments 1044.5 Molecular Markers for QTL Mapping 1194.6 Statistical Approaches for Detection of QTLs 1214.7 Software Used for QTL Mapping 1244.8 QTLs and the Signature of Selection 1254.9 Factors Affecting the Power of QTL Mapping 1254.10 Merits of QTL Mapping 1284.11 Demerits of QTL Mapping 1284.12 Conclusion and Way Forward 129References 1305 Basic Bioinformatics for Identification and Analysis of Candidate Genes in Plants Toward Crop Improvement 135Sadhana Singh5.1 Introduction 1355.2 Candidate Genes such as Transcription Factors and Gene Families 1375.3 Methods 1405.4 Conclusion 154References 1556 Exploring Machine Learning Algorithms for Gene Function Prediction in Crops 159Ruchi Jakhmola‐Mani, Sonali, Aniket Pandey, Dhananjay Raturi, Rishita Singh, Kusala Vanam, Manish D, Ritu Chauhan, Deepshikha Pande Katare, Potshangbam Nongdam, and Angamba Meetei Potshangbam6.1 Introduction 1596.2 Computational Methods for Gene Function Prediction 1646.3 Machine Learning and Crop Improvement 1676.4 Experiment 1736.5 Case Studies and Success Stories 1766.6 Challenges and Future Directions 178References 1807 Omics and Bioinformatics Approaches for Abiotic Stress Tolerance in Plants 185Santanu Samanta and Aryadeep Roychoudhury7.1 Introduction 1857.2 Genomic Approaches 1867.3 Transcriptomics Approaches 1897.4 Proteomics Approaches 1917.5 Metabolomics Approaches 1947.6 Bioinformatics Approaches 1967.7 Concluding Remarks 197Acknowledgments 198References 1988 Bioinformatics Approaches for Unraveling the Complexities of Plant Stress Physiology 209Sneha Murmu, Himanshushekhar Chaurasia, Ipsita Samal, Tanmaya Kumar Bhoi, and Asit Kumar Pradhan8.1 Introduction 2098.2 Understanding Plant Stress Response Mechanisms 2108.3 Genome and Transcriptome Analysis for Plant Stress Physiology 2128.4 Proteomics and Metabolomics Approaches 2168.5 Data Integration and Systems Biology Approaches 2208.6 Bioinformatics Resources for Plant Stress 2218.7 Conclusion 226References 2269 Bioinformatics Tools for Assessing Drought Stress Tolerance in Crops 233Nageswara Rao Reddy Neelapu and Kolluru Viswanatha Chaitanya9.1 Introduction 2339.2 Bioinformatics for Plant Research and Crop Breeding 2349.3 Genomics and Drought Stress Tolerance 2349.4 Transcriptome Analysis for the Drought Stress Tolerance 2369.5 Proteome and Drought Stress 2399.6 Metabolomics and Drought Stress Tolerance 2419.7 Phenome and Drought Stress 2429.8 Future of the Omics technologies 2449.9 Conclusions 245References 24610 Bioinformatics Tools and Resources for Plant Transcriptomics: Challenges and Opportunities 251Sona Charles and Merlin Lopus10.1 Introduction 25110.2 Evolution of Transcriptomic Technologies 25210.3 Steps in Transcriptomic Data Analysis 25410.4 R/Bioconductor Packages for Transcriptomic Analysis 25910.5 Galaxy Server for Transcriptome Analysis 26010.6 Stress Transcriptomics – A Case Study 26010.7 Conclusion and Way Forward 262References 26211 Development of a Core Set from Large Germplasm Collections in Genebank 269Pradeep Ruperao11.1 Introduction 26911.2 Developing a Core Collection 27011.3 Constructing a Core Collection 27011.4 Assessing the Core Collections 27511.5 Conclusion and Future Considerations 278References 28012 Bioinformatics Approaches to Determine Plant microRNA Targets 283Shree Prakash Pandey12.1 Introduction 28312.2 Characteristic Features and Principles of miRNA-targeting in Plants 28512.3 Tools for miRNA Target Prediction in Plants 28812.4 Bioinformatics Identification of miRNA and mRNA at a Genome-scale 29112.5 Conclusion 292References 29313 Machine Learning for the Discovery of DNA-binding Proteins in Plants 299Upendra Kumar Pradhan, Prabina Kumar Meher, and Pushpendra Kumar Gupta13.1 Introduction 29913.2 Steps Involved in Identification of DBPs Using Machine Learning 30113.3 Assessment of Learning Algorithms for DBP Prediction Using Sequence- and PSSM-derived Features 31113.4 Evaluation of Existing Tools for DBP Prediction in Plants 31313.5 Conclusion and Future Perspectives 314References 31514 Bioinformatics for Gene Identification and Crop Improvement in Wheat 321Pushpendra Kumar Gupta, Jyoti Chaudhary, and Tinku Gautam14.1 Introduction 32114.2 Databases and Tools for Individual Genes and Proteins 32114.3 Identification/Characterization of Genes/Gene Families at the DNA Level 32514.4 Characterization of Genes at the Protein Level 32814.5 Phylogenetic Analysis 33114.6 Present Status of Wheat Genes Identified in silico 33114.7 Utility of Predicted Genes for Crop Improvement 33714.8 Conclusion and Prospects 340References 34015 Bioinformatics for Analyzing the Role of Epigenetics in Plant Disease Resistance 351Kalpana Singh, Harindra Singh Balyan, and Pushpendra Kumar Gupta15.1 Introduction 35115.2 Histone Modifications 35115.3 Chromatin Accessibility 35715.4 DNA Methylation 36015.5 Noncoding RNAs (miRNAs, lncRNA, circRNA) 36515.6 Conclusions and Future Perspectives 370References 371Weblinks 39016 The Evolution of Auxin-Binding Protein 1 391Siarhei A. Dabravolski and Stanislav V. Isayenkov16.1 Abundance of Auxin and Auxin-binding Proteins in Nature 39116.2 Auxin in Plants 39216.3 Domain Organization 39316.4 ABP1 Active Sites/Structure/Sequence Analysis 39516.5 ABP1 Evolution 39816.6 Future Prospective 40316.7 Conclusion 405References 40517 Exploring the Potential of Molecular Docking and In Silico Studies in Secondary Metabolite and Bioactive Compound Discovery for Plant Research 413Amine Elbouzidi, Mohamed Taibi, and Mohamed Addi17.1 Introduction 41317.2 Importance of Structure-based Drug Design from Natural Sources 41517.3 Molecular Docking as a Key Component of SBDD: A Bridge Between Computational and Experimental Approaches 41717.4 Molecular Docking and Natural Product Database 41917.5 Case Studies: Successful Applications of In Silico Molecular Docking in Plant Research for Diverse Applications 42317.6 Concluding Remarks and Future Considerations 429References 43018 Exploring Secondary Metabolites in Plants Through Bioinformatics 435Sneha Murmu, Ritwika Das, Bharati Pandey, Soumya Sharma, and Mohammad Samir Farooqi18.1 Introduction 43518.2 Classification of Plant Secondary Metabolites 43618.3 Secondary Metabolites Pathways in Plants 43818.4 Mining of Omics Data 44018.5 Bioinformatics Tools for Analysis of Secondary Metabolites and Pathways 44718.6 Conclusion 452References 45219 Understanding Plant Secondary Metabolism Using Bioinformatics Tools: Recent Advances and Prospects 459Dola Mukherjee and Ashutosh Mukherjee19.1 Introduction 45919.2 Secondary Metabolic Gene Clusters 46119.3 Sequencing Techniques and Analytical Tools for Plant Metabolomics Study 46219.4 Bioinformatics Tools for the Elucidation of Secondary Metabolism in Plants 46519.5 Medicinal Plant Genome and/or Metabolome Databases 46519.6 Automation of Natural Product Detection by Identification of Metabolic Gene Cluster 46919.7 The Big Data and Systems Biology Approach 47019.8 Application of Machine Learning in Plant Secondary Metabolism 47119.9 Artificial Intelligence (AI) 47219.10 Machine Learning (ML) 47219.11 Deep Learning (DL) 47319.12 Conclusion and Future Perspective 475References 47720 An Appraisal of Flavonoids Through Bioinformatics 489Manoj Kumar Mishra and Vibha Pandey20.1 Overview of Flavonoids 48920.2 Identification of Flavonoid Biosynthetic Genes and Enzymes by Computational Tools 49020.3 Prediction of the Potential Biological Activities of Flavonoids Based on Their Chemical Structure 49420.4 Chalcone Synthase 49520.5 Sequence Retrieval 49620.6 Localization 49720.7 Homology Search 49720.8 Conserved Domain 49720.9 Sequence Alignment and Phylogeny 49820.10 Chromosome Location 49920.11 Characterization 50020.12 Three-dimensional Structure 50020.13 Docking 50120.14 Conclusion and Prospects 501References 50121 Golden Opportunities: Harnessing Bioinformatics to Revolutionize Plant Research and Unleash the Power of Golden Rice in Crop Breeding 505Poulami Majumder21.1 Introduction 50521.2 Background 51021.3 Bioinformatics Tools and Resources for Plant Research 51221.4 Genetic Engineering and Breeding Strategies for Golden Rice 51721.5 Case Study: Development and Improvement of Golden Rice 52321.6 Bioinformatics-guided Identification of Target Genes for Provitamin A Enhancement 52521.7 Ethical and IP Issues 52921.8 Conclusion 533Declaration 534References 53522 Going Wild: Genomics of Forest Plants and the Future of Crop Improvement 539Ajinkya Bharatraj Patil, Debojyoti Kar, Sourav Datta, and Nagarjun Vijay22.1 Introduction 53922.2 Repetitive Genomic Elements as Phenotypic Trait Variation Machinery 54222.3 Naturally Acquired Traits Can Be Effectively Characterized Using Population Genomics 54522.4 Demographic Forces Are Crucial in Shaping Genome Dynamics 54922.5 Conclusion 552References 552Index 559