Integrative Omics

Concept, Methodology, and Application

1st Edition - May 3, 2024
Imprint: Academic Press
Editors: Manish Kumar Gupta, Pramod Katara, Sukanta Mondal, Ram Lakhan Singh
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 1 6 0 9 2 - 9
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 1 6 0 9 3 - 6

Integrative Omics: Concept, Methodology and Application provides a holistic and integrated view of defining and applying network approaches, integrative tools, and methods t… Read more

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Integrative Omics: Concept, Methodology and Application provides a holistic and integrated view of defining and applying network approaches, integrative tools, and methods to solve problems for the rationalization of genotype to phenotype relationships. The reference provides systemic ‘step-by-step’ coverage that begins with basic concepts from Omic to Multi Integrative Omics approaches followed by applications and emerging and future trends. All areas of Omics are covered, including biological databases, sequence alignment, pharmacogenomics, nutrigenomics and microbial omics, integrated omics for Food Science and Identification of genes associated with disease, clinical data integration and data warehousing, translational omics, technology policy, and society research. This book covers recent concepts, methodologies, advancements in technologies and is also well-suited for researchers from both academic and industry background, undergraduate and graduate students who are mainly working in the area of computational systems biology, integrative omics and translational science.

Cover image
Title page
Table of Contents
Copyright
Dedication
List of contributors
Preface
Chapter 1. From omic to multi-integrative omics approach
1. Introduction
2. From omics to multi-integrative omics
3. Potential of multi-integrative omics
4. Integration and interdependencies of omics
5. Data for multi-integrative omics
6. Data mining and exploitation for omics data
7. Data mining tools/software
8. Integrative data mining challenges and possibilities
9. Scope for data science in multi-integrative omics
10. Conclusion
Chapter 2. Types of omics data: Genomics, metagenomics, epigenomics, transcriptomics, proteomics, metabolomics, and phenomics
1. Introduction
2. Genomics in medicine
3. Metagenomics
4. Epigenomics
5. Transcriptomics
6. Proteomics
7. Metabolomics
8. Phenomics
Chapter 3. Biological omics databases and tools
1. Introduction
2. Omics datasets study based on different technology
3. Specific omics datasets based on sequencing approach
4. Specific omics datasets based on MS approach
5. Specific omics datasets based on knowledge
6. Integration of omics datasets
Chapter 4. Systematic benchmarking of omics computational tools
1. Introduction
2. Methodology for setting up the benchmarking study
3. Experimental setup
4. Case studies
5. The future of benchmarking challenges
6. Conclusion
7. Tools for omics data analysis
Chapter 5. Pharmacogenomics, nutrigenomics, and microbial omics
1. Introduction
2. Pharmacogenomics
3. Nutrigenomics
4. Microbial omics
5. Correlation between nutrigenomics, pharmacogenomics, and microbial omics
Chapter 6. Proteomics: Present and future prospective
1. Introduction
2. Label-free quantitative proteomics
3. Techniques used in proteomics
4. Present prospective of proteomics
5. Future prospective of proteomics
6. Advance tools used for proteomics
7. Conclusion
Chapter 7. Foodomics: Integrated omics for the food and nutrition science
1. Introduction
2. Major four areas of omics that are involved in foodomics are
3. Challenges of foodomics
4. Conclusions
Chapter 8. Vaccinomics: Structure based drug designing computational approaches for the designing of novel vaccines
1. Introduction
2. Tools for vaccinomics
3. T- and B-cell epitope identification
4. The procedure of vaccine antigen designing
5. Immunoinformatics in COVID-19 vaccine development
6. Vaccinomics limitations for vaccine design
7. Conclusion
Chapter 9. Integrative omics approach for identification of genes associated with disease
1. Introduction
2. Various types of omics data
3. Methods of omics data analysis
4. Limitations in omics data integration and interpretation
5. Future research directions
6. Conclusion
Chapter 10. Integrative omics approaches for identification of biomarkers
1. Introduction
2. Multimodal omics methods for a single cell
3. Approaches to integrating metabolomics and multiomics data
4. Integration of multiomics data
5. Advantages of multiomics integration
6. Application of omics approaches in cohort studies
7. Computational methods for biomarker identification in complex disease
8. Network analysis and visualization with igraph and cystoscope
9. Conclusion
Chapter 11. Omics approach for personalized and diagnostics medicine
1. Introduction
2. Multiomics approaches in diseases and medicine
3. Integration of clinical data of personnel and group with patient-specific omics datasets
4. Personalized medicine and its importance
5. Challenges, opportunities, and prospects for personalized diagnostics
Chapter 12. Role of bioinformatics in genome analysis
1. Applications of bioinformatics
2. Assembly
3. Identification of mutants
4. Visualization tools for genomic data
5. Pipeline for the transcriptome data analysis
6. Analysis of differential gene expression
7. Gene enrichment analysis
8. Conclusion
Chapter 13. Data management in cross-omics
1. Introduction
2. Why cross-omics data management required?
3. Hardware setups for managing cross-omics data
4. Software required for cross-omics data analysis
5. File formats used in cross-omics data management
6. How does the biological database work?
7. Cross-omics data management
8. Cross-omics data integration
9. Quality control for cross-omics data management
10. Challenges in cross-omics data management
11. Data sources (Table 13.2)
12. Conclusion
Chapter 14. Omics and clinical data integration and data warehousing
1. Clinical data
2. Sources of clinical data
3. Case report form
4. Data integration
5. Data warehouse
6. Clinical data integration in health care
7. Data security and confidentiality
8. Benefits of healthcare data integration and interoperability
9. Applications and future prospects
Chapter 15. Integrative omics data mining: Challenges and opportunities
1. Introduction
2. High-throughput multiomics in human health and diseases
3. Learning and use of various programming languages
4. Database resources in multiomics analysis
5. Multiomics data integration methods
6. Computational infrastructure, data sharing, and benchmarking
7. Data mining methods
8. Challenges in multiomics data integration and data mining
Chapter 16. Data science and analytics, modeling, simulation, and issues of omics dataset
1. What is data science?
2. What is data analytics?
3. Difference between data science and data analytics
4. What is omics?
5. Application of data science in omics data analysis
6. How to apply data science in omics data analysis?
7. How to collect data to use in omics data processing?
8. Pathway modeling, visualization, and simulation
9. Statistical method for genomics data analysis
10. Machine learning–based genomics software and tools
11. Omics data visualization
12. Omics data integration
13. Hardware acceleration in omics data analysis
14. Issues in omics data visualization
15. Issues in omics dataset
16. Overcoming challenges in omics data analytics
17. Conclusion
Chapter 17. Emerging trends in translational omics
1. Omics data sources and analysis
2. Translational omics
3. Omics-based tests and precision medicine
4. Omics test development process
5. Completion of the discovery phase of omics-based test development
6. Issues and limitations of translational omics analysis
7. Summary
Chapter 18. Omics technologies for crop improvement
1. Introduction
2. OMICS-based technologies
3. How omics technologies help in crop improvement?
4. Systems biology approaches for crop improvement
5. Databases and software tools for crop omics analysis
6. Network-based approaches in crop improvement
7. Artificial intelligence in crop improvement
8. Challenges and complexities associated with OMICS-based approach for crop improvement
9. Conclusion and future prospects
Chapter 19. Ecology and environmental omics
1. Introduction
2. High-throughput molecular technologies in environmental research
3. Environmental omics in the context of toxicology
4. Role of omics in assessing ecosystems
5. Omics approaches in ecotoxicology and stress biology
6. Organism's genome related to dietary and environmental exposures
7. Environmental omics of single chemicals versus chemical mixtures
8. Traditional versus omics-based environmental toxicology
9. Environmental monitoring and health risk assessment
10. Future directions of ecology and environmental omics
Chapter 20. Current trends and approaches in clinical metagenomics
1. Introduction
2. Clinical microbiome
3. Clinical metagenomics
4. Clinical metagenomic projects
5. Role of clinical metagenomics in human health
6. Concern's and issues of clinical data handling
7. Applications of clinical metagenomics
8. Future aspects
Chapter 21. Biomolecular networks
1. Introduction
2. What are networks?
3. What are biological networks?
4. Network modules and its importance in the network
5. Network models
6. Visualization of biomolecular network tools used for network analysis
7. Network medicine
8. Biological network study by deep machine learning
9. Scope and application of biomolecular network in biological sciences
10. Conclusion
Chapter 22. Machine learning fundamentals to explore complex omics data
1. Backdrop
2. An easy explanation of machine learning from commonly used data standardization process
3. Application of classifiers for predicting the secondary structure of a protein from its sequence
4. Protein secondary structure prediction using artificial neural network–based classifier
5. A simple description of deep learning method
6. Use of multiple ML models (multiple classifiers) to analyze multiomics data
7. Concluding words
Chapter 23. Omics technology policy and society research
1. Introduction
2. What are omics technology policies?
3. Omics technology policy debates
4. Current methodologies and tools in omics policy
5. Various types of data
6. Genomics policy issues
7. DNA technology (use and application) in India
8. Limitations of omics technology policy
9. Omics technology for human health
10. Omics technology for livestock and crop improvement
11. Challenges in omics technology and policy framing
12. Ethical concern in omics technology and policy framing
13. Conclusion
Index

Manish Kumar Gupta

Dr. Manish Kumar Gupta is Associate Professor in the Department of Biotechnology at Uttar Pradesh. Dr. Gupta has published more than twelve research papers, published one edited book, five book chapters and given more than twenty-seven invited lectures. Dr. Gupta is life member of various academic societies such as Federation of Asian Biotechnology Association (FABA), Association of Microbiologist of India (AMI), Society of Environmental Sustainability (Founder member) and Indian Science Congress Association (ISCA). Dr. Gupta’s focus of the research work is to extract the key regulatory elements participating in the disease pathways through integrative biology approach. To investigate those, biological tools & databases with big scale data sets generated by state-of-the-art high-throughput technology integrated for computational analysis.

Affiliations and expertise

Associate Professor, Department of Biotechnology, Veer Bahadur Singh Purvanchal University, Jaunpur, India

Pramod Katara

Pramod Katara is an Assistant Professor at the Centre of Bioinformatics, University of Allahabad. He has 14 years of teaching and research experience. He has published several peer-reviewed papers and has authored several book chapters. He is a member/life member of several national and international scientific societies and attended many symposia/conferences at national and international levels and presented invited lectures/papers there as well as chairing sessions. He has been actively working in the field of bioinformatics to solve various biological issues with the help of computational omics approaches. His current research is focused on the population-specific human genome and transcriptome analysis to predict the pharmacogenomic variant and solve pharmacogenomics related issues).

Affiliations and expertise

Assistant Professor, Centre of Bioinformatics, University of Allahabad, Prayagraj, India

Sukanta Mondal

Sukanta Mondal is currently working as Principal Scientist, Physiology Division, ICAR-National Institute of Animal Nutrition and Physiology. His major research interests involve CRISPR/Cas 9 guided targeted genome editing, gene silencing, molecular cloning, characterization and expression of genes regulating early embryonic loss, molecular characterization and expression of hormone receptors, regulation of prostaglandin production, identification and characterization of gene expression and protein processing in endometrium in association with recognition and establishment of pregnancy, and impact of stress on maternal recognition of pregnancy. Dr. Mondal has published more than 130 research publications in various journals and presented more than 30 papers at various national and international conferences.

Affiliations and expertise

Principal Scientist, National Institute of Animal Nutrition and Physiology, Adugodi, Bangalore, India

Ram Lakhan Singh

Professor Ram Lakhan Singh is Vice-Chancellor of Nilamber-Pitamber University, Medininagar, India. He held position of Professor and Chair of Biochemistry at Dr. Rammanohar Lohia Avadh University, Ayodhya, India prior to joining this assignment. He has 31 years of experience as teacher and 39 years as researcher in the area of Environmental and Nutraceutical Biochemistry. Professor Singh published more than 87 research papers, written 17 book chapters and edited 6 books published by Springer, CRC and Elsevier. He guided 25 PhD students and has been invited as editorial board member of International journals. He has been awarded IUTOX Senior Fellowship in 2007 and admitted to the Fellowships of the Society of Toxicology in 2011 and Academy of Environmental Biology in 2015.

Affiliations and expertise

Vice-Chancellor, Nilamber-Pitamber University, Medininagar, Jharkhand, India

Life Sciences

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Integrative Omics

Concept, Methodology, and Application

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Manish Kumar Gupta

Pramod Katara

Sukanta Mondal

Ram Lakhan Singh

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