Genomics Data Analysis for Crop Improvement [electronic resource] / edited by Priyanka Anjoy, Kuldeep Kumar, Girish Chandra, Kishor Gaikwad.
Material type:
TextSeries: Springer Protocols HandbooksPublisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2024Edition: 1st ed. 2024Description: XI, 374 p. 1 illus. online resourceContent type: - text
- computer
- online resource
- 9789819969135
- 630 23
- S1-972
Chapter 1. Statistical and Biological Data Analysis using Programming Languages -- Chapter 2. Python for Biologists -- Chapter 3. Assembly, Annotation and Visualization of NGS Data -- Chapter 4. Statistical and Quantitative Genetics Studies -- Chapter 5. Mapping of Quantitative Traits Loci: Harnessing Genomics Revolution for Dissecting Complex Traits -- Chapter 6. Trait Based Association Mapping in Plants -- Chapter 7. META-Analysis of Mapping Studies: Integrating QTLs Towards Candidate Gene Discovery -- Chapter 8. Role of Databases and Bioinformatics Tools in Crop Improvement -- Chapter 9. Overview of the Bioinformatics Databases and Tools for Genome Research and Crop Improvement -- Chapter 10. Public Domain Databases - A Gold Mine for Identification and Genome Reconstruction of Plant Viruses and Viroids -- Chapter 11. Tree Genome Databases: A New Era in the Development of Cyber-infrastructures for Forest Trees -- Chapter 12. Development of Biological Databases for Genomic Research -- Chapter 13. Artificial Intelligence in Genomic Studies -- Chapter 14. Basics of the Molecular Biology: From Genes to its Function.
This book addresses complex problems associated with crop improvement programs, using a wide range of programming solutions, for genomics data handling and sustainable agriculture. It describes important concepts in genomics data analysis and sequence-based mapping approaches along with references. The book contains 16 chapters on recent developments in several methods of genomic data analysis for crop improvements and sustainable agriculture, all authored by eminent researchers who are experts in their fields. These chapters focus on applications of a wide range of key bioinformatics topics, including assembly, annotation, and visualization of next-generation sequencing (NGS) data; expression profiles of coding and noncoding RNA; statistical and quantitative genetics; trait-based association analysis, quantitative trait loci (QTL) mapping, and artificial intelligence in genomic studies. Real examples and case studies in the book will come in handy when applying the techniques. The relative scarcity of reference materials covering bioinformatics applications as compared with the readily available books also enhances the utility of this book. The targeted readers of the book are scientists, researchers, and bioinformaticians from genomics and advanced breeding in different areas. The book will appeal to the applied researchers engaged in crop improvements and sustainable agriculture by using bioinformatics tools, students, research project leaders, and practitioners from the various marginal disciplines and interdisciplinary research.
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