Harnessing Genomic Tools for Crop Improvement

A Software User Manual for Transforming Geneticists to Genomicists
Herausgeber: Vemireddy, Lakshmi Narayana R; Amaravathi, Yellari; Reddyyamini, Bommisetty

• Broschiertes Buch

Produktbeschreibung

Harnessing Genomic Tools for Crop Improvement: A Software User Manual for Transforming Geneticists to Genomicists is an important resource for crop improvement software users. This book provides essential information to understand the molecular data stems from the molecular genetics and breeding experiments. It presents various genomics-related software encompassing genetic diversity (DARWIN), QTL mapping (QTL Cartographer), Genome-wide association mapping (GAPIT), Haplotype analysis (DNAsp), Marker-assisted breeding (GGT), and others in a step-by-step format. It also looks at the latest bioinformatic tools such as databases, BLAST, primer designing and sequence alignment. Furthermore, this book provides definitions and clarifies the terminology and interpretation of the outputs (Tables and Figures) of each software through case studies. This book provides a single comprehensive user manual for crop improvement software tool users, making it an invaluable asset for scientists, students, and post-doctoral fellows with an interest in plant and animal genetics.

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Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 310
• Erscheinungstermin: 10. August 2025
• Englisch
• ISBN-13: 9780443292811
• ISBN-10: 0443292817
• Artikelnr.: 72648407

Inhaltsangabe

1. Basic bioinformatics software
2. Molecular marker development
3. ARMS-PCR, CAPS, dCAPS, etc 3. NGS analysis
4. Genetic diversity and population structure analysis
5. QTL mapping
6. Genome-wide association mapping
7. Statistical models for Genomic selection
8. Haplotype analysis
9. Analysis of Gene regulatory networks
10. Transcriptome analysis
11. MiRNA structure and target prediction tools
12. Designing of guide RNA for CRISPR gene editing
13. Protein Interaction Network Analysis: Essential Tools and Resources
14. Navigating in silico drug discovery: A hands-on guide with Auto Dock
Vina, ChemDraw and Chimera for structure simulation
15. Machine learning techniques for gene prediction