Integrative omics : concepts, 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...

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Bibliographic Details
Corporate Author:	ScienceDirect (Online service)
Other Authors:	Gupta, Manish Kumar (Editor), Katara, Pramod (Editor), Mondal, Sukanta (Editor), Singh, Ram Lakhan (Editor)
Format:	eBook
Language:	English
Published:	London, United Kingdom : San Diego, CA : Academic Press, [2024]
Subjects:	Genomics. Génomique. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Front Cover
Integrative Omics
Integrative Omics: Concept, Methodology and Application
Copyright
Dedication
Contents
List of contributors
Preface
Organization of the book
1
From omic to multi-integrative omics approach
1. Introduction
1.1 Omics
1.2 Genomics
1.3 Transcriptomics
1.4 Proteomics
1.5 Metabolomics
1.6 Epigenomics
1.7 Other potential omics
1.7.1 Fluxomics
1.7.2 Interactomics
1.7.3 Metagenomics
1.7.4 Pharmacogenomics
1.7.5 Phenomics
1.7.6 Foodomics
2. From omics to multi-integrative omics
3. Potential of multi-integrative omics
4. Integration and interdependencies of omics
4.1 Postanalysis data integration approaches
4.2 Integrated data analysis approaches
5. Data for multi-integrative omics
5.1 Multiomics data
5.2 Integration of multiomics data
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
References
2
Types of omics data: Genomics, metagenomics, epigenomics, transcriptomics, proteomics, metabolomics, and phenom ...
1. Introduction
1.1 Genomics
1.2 Transcriptomics
1.3 Proteomics
1.4 Metabolomics
1.5 Epigenomics
2. Genomics in medicine
2.1 Current trends of genomics in medicine
2.1.1 Precision medicine
2.1.2 Pharmacogenomics
2.1.3 Genome editing
2.1.4 Gene therapy
2.2 Computational pipeline for genomics using R code
2.3 Applications of genomics
3. Metagenomics
3.1 Computational pipeline for metagenomics
3.2 Metagenomics omics tools and packages
4. Epigenomics
4.1 Publicly available datasets for epigenomics studies
4.1.1 The ENCODE project
4.1.2 The Roadmap Epigenomics project
4.1.3 The BLUEPRINT project.
4.1.4 The NIH Epigenomics Data Analysis and Coordination Center
4.1.5 The Gene Expression Omnibus
4.2 Epigenomics omics approaches
4.2.1 DNA methylation omics approaches
4.2.2 R code for DNA methylation data analysis pipeline
4.3 Epigenetic gene ontology and pathway analyses
4.4 Epigenomics databases
4.5 R codes for epigenomics data analysis
5. Transcriptomics
5.1 Transcriptomics studies in medicine
5.2 Publicly available transcriptomics datasets
5.2.1 GEO
5.2.2 The Cancer Genome Atlas
5.2.3 ArrayExpress
5.3 Transcriptomics computational pipelines
6. Proteomics
6.1 Omics approaches for proteomics
6.1.1 Quantitative proteomics
6.1.2 Structural proteomics
6.1.3 Posttranslational modification proteomics
6.1.4 Functional proteomics
7. Metabolomics
7.1 Types of metabolomics
7.1.1 Untargeted metabolomics
7.1.2 Targeted metabolomics
7.2 Various approaches commonly used in metabolomics
7.3 R codes for metabolomics pipeline
7.4 Publicly available metabolomics datasets
7.5 Application of omics-based metabolomics tools
8. Phenomics
8.1 Phenomics example datasets for using in R
8.2 Omics pipeline for phenomics analysis
References
3
Biological omics databases and tools
1. Introduction
2. Omics datasets study based on different technology
2.1 Next-generation sequencing
2.2 Microarray
2.3 RNA-Seq
2.4 Mass spectrometry
2.5 GC-MS (gas chromatography MS), LC-MS (liquid chromatography MS), and CE-MS (capillary electrophoresis MS)
3. Specific omics datasets based on sequencing approach
3.1 Genomics
3.2 Epigenomics
3.3 Metagenomics
3.4 Transcriptomics
3.5 Epitranscriptomics
4. Specific omics datasets based on MS approach
4.1 Proteomics
4.2 Epiproteomics
4.3 Metabolomics
4.4 Glycomics
4.5 Lipidomics.
5. Specific omics datasets based on knowledge
5.1 Microbiomics
5.2 Immunomics
6. Integration of omics datasets
6.1 Methods of integration of omics data
6.2 Techniques used for integration of multiomics data
6.2.1 Machine learning
6.2.2 Deep learning and its application in types of omics data
6.3 Biological tools and databases used in omics data analysis
6.4 Challenges of integration of omics data
6.5 Application of integrated omics data
References
4
Systematic benchmarking of omics computational tools
1. Introduction
2. Methodology for setting up the benchmarking study
2.1 Selection of reference model
2.2 Construction of gold standard dataset
2.3 Default parameters selection and optimization
2.4 Selection of benchmarking datasets
2.5 Selection of computational tools
2.6 Computational efficiency and scalability
2.7 Integration and interoperability
3. Experimental setup
3.1 QIAGEN Genomics Workbench
3.2 Partek Genomics Suite
3.3 Golden Helix
3.4 Genomatix
3.5 Biodatomics
3.6 Basepair
3.7 DNAnexus
3.8 Lasergene Genomics Suite
3.9 NextGENe
4. Case studies
4.1 Case study 1: Hybrid assembly strategy on genomics data analysis
4.1.1 Data collection
4.1.2 Genome assembly
4.1.3 Scaffolding
4.1.4 Metrics for quality control
4.1.5 Genome consistency plots
4.2 Case study 2: Computational cost analysis by assessing Oxford nanopore read assemblers
4.3 Case study 3: Genome assembly methods on metagenomic sequencing data
4.3.1 Metagenome assembly
4.3.2 Contig statistics
4.3.3 Contig binning and MAG qualities
4.3.4 Annotate MAGs into species
4.4 Case study 4: Data-independent acquisition mass spectrometry for immunopeptidomics
4.4.1 Library search-based DIA data analysis.
4.4.2 Software tools for peptide sequence and statistical data analysis
4.5 Case study 5: Long-read RNA-sequencing analysis tools using in silico mixtures
4.5.1 Long-read isoform detection
4.5.2 Transcript-level quantification of detected isoforms
4.5.3 Read count processing
4.5.4 Transcriptomic alignment
4.5.5 Differential transcript expression and usage analysis
5. The future of benchmarking challenges
6. Conclusion
7. Tools for omics data analysis
References
5
Pharmacogenomics, nutrigenomics, and microbial omics
1. Introduction
2. Pharmacogenomics
2.1 Why pharmacogenomics?
2.2 Discovery of pharmacogenomics
2.2.1 Cholinesterase inhibitors
2.2.2 Memantine
2.2.3 BACE inhibitors
2.3 The promise of pharmacogenomics
2.3.1 Personalized medicine
2.3.2 Drug selection and dosing
2.3.3 Adverse drug reactions
2.3.4 Drug development and clinical trials
2.3.5 Cost-effectiveness
2.4 Pharmacogenomics as a powerful tool
2.4.1 Improving the safety and effectiveness of medications
2.4.2 Reducing health-care costs
2.4.3 Improving the accuracy of clinical guidelines and treatment recommendations
2.4.4 Enhancing the understanding of the underlying causes of diseases
2.5 Pharmacogenomics-A solution
2.6 Role of pharmacogenomics in drug development
2.7 Research scopes in pharmacogenomics
2.7.1 Identifying genetic predictors of drug response
2.7.2 Investigating the role of genetic variation in adverse drug reactions
2.7.3 Exploring the use of pharmacogenomics in personalized medicine
2.7.4 Improving drug development and regulatory processes
3. Nutrigenomics
3.1 Why nutrigenomics?
3.2 Discovery of nutrigenomics
3.3 The promise of nutrigenomics
3.4 Nutrigenomics as a powerful tool
3.4.1 Personalized dietary recommendations.
3.4.2 Gene-nutrient interactions
3.4.3 Chronic disease prevention and management
3.4.4 Ethical considerations and challenges
3.5 Nutrigenomics as a solution
4. Microbial omics
4.1 Why microbial omics?
4.1.1 Metagenomics
4.1.2 Metatranscriptomics
4.1.3 Metaproteomics
4.1.4 Metabolomics
4.2 Discovery of microbial omics
4.2.1 Genomics
4.2.2 Metagenomics Fig. 5.2
4.2.3 Transcriptomics
4.2.4 Proteomics
4.2.5 Metabolomics
4.3 Promise of microbial omics
4.3.1 Genomics
4.3.2 Metagenomics
4.3.3 Transcriptomics
4.3.4 Proteomics
4.3.5 Metabolomics
5. Correlation between nutrigenomics, pharmacogenomics, and microbial omics
References
6
Proteomics: Present and future prospective
1. Introduction
2. Label-free quantitative proteomics
2.1 Relative quantification by peak intensity of LCMS
2.2 Relative quantification by spectral count
2.3 Absolute label-free quantification
2.4 Commercially available software for label-free quantitative proteomics
3. Techniques used in proteomics
3.1 Isotope coded affinity tag (ICAT) labeling
3.2 Stable isotope labeling with amino acids in cell culture (SILAC)
3.3 Isobaric tag for relative and absolute quantitation (iTRAQ)
3.4 2D gel electrophoresis
3.5 2D differential gel electrophoresis
3.6 Protein microarray
3.7 Reverse phased protein microarray
3.8 Mass spectrometry
3.9 LCMS/MS
3.10 MALDI TOF/TOF
3.11 NMR spectroscopy
4. Present prospective of proteomics
5. Future prospective of proteomics
6. Advance tools used for proteomics
7. Conclusion
References
7
Foodomics: Integrated omics for the food and nutrition science
1. Introduction
2. Major four areas of omics that are involved in foodomics are
2.1 Genomics
2.1.1 Nutritional genomics.