Bioinformatics in Agriculture: Next Generation Sequencing Era is a comprehensive volume presenting an integrated research and development approach to the practical application of genomics to improve agricultural crops. Exploring both the theoretical and applied aspects of computational biology, and focusing on the innovation processes, the book highlights the increased productivity of a translational approach. Presented in four sections and including insights from experts from around the world, the book includes: Section I: Bioinformatics and Next Generation Sequencing Technologies; Section II: Omics Application; Section III: Data mining and Markers Discovery; Section IV: Artificial Intelligence and Agribots.
Bioinformatics in Agriculture: Next Generation Sequencing Era explores deep sequencing, NGS, genomic, transcriptome analysis and multiplexing, highlighting practices forreducing time, cost, and effort for the analysis of gene as they are pooled, and sequenced. Readers will gain real-world information on computational biology, genomics, applied data mining, machine learning, and artificial intelligence.
This book serves as a complete package for advanced undergraduate students, researchers, and scientists with an interest in bioinformatics.
1. Bioinformatics in Agriculture - p. i 2. Cover - p. i 3. Title Page - p. i 4. Copyright - p. iv 5. Table of Contents - p. iv 6. List of Contributors - p. xvii 7. About the Editors - p. xxiii 8. Foreword - p. xxv 9. Preface - p. xxvii 10. Chapter 1 Advances in agricultural bioinformatics: an outlook of multi “omics” approaches - p. 3 11. Chapter 2 Promises and benefits of omics approaches to data-driven science industries - p. 23 12. Chapter 3 Bioinformatics intervention in functional genomics: current status and future perspective—an overview - p. 37 13. Chapter 4 Genome informatics: present status and future prospects in agriculture - p. 47 14. Chapter 5 Genomics and its role in crop improvement - p. 61 15. Chapter 6 Genome-wide predictions, structural and functional annotations of plant transcription factor gene families: a bioinformatics approach - p. 79 16. Chapter 7 Proteomics as a tool to understand the biology of agricultural crops - p. 107 17. Chapter 8 Metabolomics and sustainable agriculture: concepts, applications, and perspectives - p. 123 18. Chapter 9 Plant metabolomics: a new era in the advancement of agricultural research - p. 139 19. Chapter 10 Explore the RNA-sequencing and the next-generation sequencing in crops responding to abiotic stress - p. 161 20. Chapter 11 Identification of novel RNAs in plants with the help of next-generation sequencing technologies - p. 177 21. Chapter 12 Molecular evolution, three-dimensional structural characteristics, mechanism of action, and functions of plant beta-galactosidases - p. 191 22. Chapter 13 Next generation genomics: toward decoding domestication history of crops - p. 209 23. Chapter 14 In-silico identification of small RNAs: a tiny silent tool against agriculture pest - p. 221 24. Chapter 15 Bioinformatics-assisted multiomics approaches to improve the agronomic traits in cotton - p. 233 25. Chapter 16 Omics-assisted understanding of BPH resistance in rice: current updates and future prospective - p. 253 26. Chapter 17 Contemporary genomic approaches in modern agriculture for improving tomato varieties - p. 265 27. Chapter 18 Characterization of drought tolerance in maize: omics approaches - p. 279 28. Chapter 19 Deciphering the genomic hotspots in wheat for key breeding traits using comparative and structural genomics - p. 295 29. Chapter 20 Prospects of molecular markers for wheat improvement in postgenomic era - p. 323 30. Chapter 21 Omics approaches for biotic, abiotic, and quality traits improvement in potato (Solanum tuberosum L.) - p. 341 31. Chapter 22 Tea plant genome sequencing: prospect for crop improvement using genomics tools - p. 361 32. Chapter 23 Next-generation sequencing and viroid research - p. 373 33. Chapter 24 Computational analysis for plant virus analysis using next-generation sequencing - p. 383 34. Chapter 25 Microbial degradation of herbicides in contaminated soils by following computational approaches - p. 399 35. Chapter 26 Chloroplast genome and plant–virus interaction - p. 419 36. Chapter 27 Deciphering soil microbiota using metagenomic approach for sustainable agriculture: an overview - p. 439 37. Chapter 28 Concepts and applications of bioinformatics for sustainable agriculture - p. 455 38. Chapter 29 Application of high-throughput structural and functional genomic technologies in crop nutrition research - p. 491 39. Chapter 30 Bioinformatics approach for whole transcriptomics-based marker prediction in agricultural crops - p. 503 40. Chapter 31 Computational approaches toward single-nucleotide polymorphism discovery and its applications in plant breeding - p. 513 41. Chapter 32 Bioinformatics intervention in identification and development of molecular markers: an overview - p. 537 42. Chapter 33 Deciphering comparative and structural variation that regulates abiotic stress response - p. 561 43. Chapter 34 Deep Learning applied to computational biology and agricultural sciences - p. 589 44. Chapter 35 Image processing–based artificial intelligence system for rapid detection of plant diseases - p. 619 45. Chapter 36 Role of artificial intelligence, sensor technology, big data in agriculture: next-generation farming - p. 625 46. Chapter 37 Artificial intelligence: a way forward for agricultural sciences - p. 641 47. Index - p. 669
Bioinformatics in Agriculture: Next Generation Sequencing Era is a comprehensive volume presenting an integrated research and development approach to the practical application of genomics to improve agricultural crops. Exploring both the theoretical and applied aspects of computational biology, and focusing on the innovation processes, the book highlights the increased productivity of a translational approach. Presented in four sections and including insights from experts from around the world, the book includes: Section I: Bioinformatics and Next Generation Sequencing Technologies; Section II: Omics Application; Section III: Data mining and Markers Discovery; Section IV: Artificial Intelligence and Agribots.
Bioinformatics in Agriculture: Next Generation Sequencing Era explores deep sequencing, NGS, genomic, transcriptome analysis and multiplexing, highlighting practices forreducing time, cost, and effort for the analysis of gene as they are pooled, and sequenced. Readers will gain real-world information on computational biology, genomics, applied data mining, machine learning, and artificial intelligence.
This book serves as a complete package for advanced undergraduate students, researchers, and scientists with an interest in bioinformatics.
1. Bioinformatics in Agriculture - p. i 2. Cover - p. i 3. Title Page - p. i 4. Copyright - p. iv 5. Table of Contents - p. iv 6. List of Contributors - p. xvii 7. About the Editors - p. xxiii 8. Foreword - p. xxv 9. Preface - p. xxvii 10. Chapter 1 Advances in agricultural bioinformatics: an outlook of multi “omics” approaches - p. 3 11. Chapter 2 Promises and benefits of omics approaches to data-driven science industries - p. 23 12. Chapter 3 Bioinformatics intervention in functional genomics: current status and future perspective—an overview - p. 37 13. Chapter 4 Genome informatics: present status and future prospects in agriculture - p. 47 14. Chapter 5 Genomics and its role in crop improvement - p. 61 15. Chapter 6 Genome-wide predictions, structural and functional annotations of plant transcription factor gene families: a bioinformatics approach - p. 79 16. Chapter 7 Proteomics as a tool to understand the biology of agricultural crops - p. 107 17. Chapter 8 Metabolomics and sustainable agriculture: concepts, applications, and perspectives - p. 123 18. Chapter 9 Plant metabolomics: a new era in the advancement of agricultural research - p. 139 19. Chapter 10 Explore the RNA-sequencing and the next-generation sequencing in crops responding to abiotic stress - p. 161 20. Chapter 11 Identification of novel RNAs in plants with the help of next-generation sequencing technologies - p. 177 21. Chapter 12 Molecular evolution, three-dimensional structural characteristics, mechanism of action, and functions of plant beta-galactosidases - p. 191 22. Chapter 13 Next generation genomics: toward decoding domestication history of crops - p. 209 23. Chapter 14 In-silico identification of small RNAs: a tiny silent tool against agriculture pest - p. 221 24. Chapter 15 Bioinformatics-assisted multiomics approaches to improve the agronomic traits in cotton - p. 233 25. Chapter 16 Omics-assisted understanding of BPH resistance in rice: current updates and future prospective - p. 253 26. Chapter 17 Contemporary genomic approaches in modern agriculture for improving tomato varieties - p. 265 27. Chapter 18 Characterization of drought tolerance in maize: omics approaches - p. 279 28. Chapter 19 Deciphering the genomic hotspots in wheat for key breeding traits using comparative and structural genomics - p. 295 29. Chapter 20 Prospects of molecular markers for wheat improvement in postgenomic era - p. 323 30. Chapter 21 Omics approaches for biotic, abiotic, and quality traits improvement in potato (Solanum tuberosum L.) - p. 341 31. Chapter 22 Tea plant genome sequencing: prospect for crop improvement using genomics tools - p. 361 32. Chapter 23 Next-generation sequencing and viroid research - p. 373 33. Chapter 24 Computational analysis for plant virus analysis using next-generation sequencing - p. 383 34. Chapter 25 Microbial degradation of herbicides in contaminated soils by following computational approaches - p. 399 35. Chapter 26 Chloroplast genome and plant–virus interaction - p. 419 36. Chapter 27 Deciphering soil microbiota using metagenomic approach for sustainable agriculture: an overview - p. 439 37. Chapter 28 Concepts and applications of bioinformatics for sustainable agriculture - p. 455 38. Chapter 29 Application of high-throughput structural and functional genomic technologies in crop nutrition research - p. 491 39. Chapter 30 Bioinformatics approach for whole transcriptomics-based marker prediction in agricultural crops - p. 503 40. Chapter 31 Computational approaches toward single-nucleotide polymorphism discovery and its applications in plant breeding - p. 513 41. Chapter 32 Bioinformatics intervention in identification and development of molecular markers: an overview - p. 537 42. Chapter 33 Deciphering comparative and structural variation that regulates abiotic stress response - p. 561 43. Chapter 34 Deep Learning applied to computational biology and agricultural sciences - p. 589 44. Chapter 35 Image processing–based artificial intelligence system for rapid detection of plant diseases - p. 619 45. Chapter 36 Role of artificial intelligence, sensor technology, big data in agriculture: next-generation farming - p. 625 46. Chapter 37 Artificial intelligence: a way forward for agricultural sciences - p. 641 47. Index - p. 669
