Introduction
Integrative Metagenomics: Techniques, Analysis, and Applications presents a comprehensive and structured exploration of modern metagenomics, bridging foundational principles with advanced analytical methodologies and real-world applications. The book begins with core concepts, the evolution of metagenomic studies, and the growing role of bioinformatics platforms in microbial research.
It systematically guides readers through metagenomic sequencing techniques, including shotgun and 16S rRNA approaches, followed by detailed workflows for data acquisition, preprocessing, quality control, and assembly. Extensive coverage of bioinformatics tools and pipelines equips readers with practical knowledge for sequence alignment, taxonomic and phylogenetic classification, and functional annotation.
The book further explores advanced domains such as metatranscriptomics, multi-omics integration, and metagenome-wide association studies (MWAS), supported by case studies in human microbiome research and environmental systems. Dedicated chapters on wet-lab validation, experimental methodologies, and translational applications in human disease and drug discovery provide a strong link between computational analysis and laboratory science.
Concluding with current research trends and future directions, the book highlights emerging technologies such as AI-driven analytics, long-read sequencing, and personalised medicine, offering a forward-looking perspective on the role of metagenomics in healthcare, agriculture, environmental sustainability, and industrial biotechnology.
Key Features
- - Detailed overview of metagenomic workflows and genome sequencing techniques.
- - Comprehensive guide to bioinformatics tools, pipelines, and platforms for metagenomic analysis.
- - In-depth discussion on taxonomic classification, phylogenetics, and functional annotation.
- - Integration of advanced topics such as metatranscriptomics, multi-omics, and MWAS.
- - Case studies and practical insights on the human microbiome, soil ecosystems, and environmental applications, integrated with wet-lab validation and computational workflows.
- - Exploration of AI, machine learning, and emerging technologies in metagenomics.
Target Readership:
Researchers, academics, postgraduate students and professionals in bioinformatics, microbiology, genomics, biotechnology, and computational biology.