Omics Approaches, Technologies And Applications

Name: Omics Approaches, Technologies And Applications
Author: Preeti Arivaradarajan

Integrative Approaches For Understanding OMICS Data

Gebonden Engels 2019 9789811329241

216,99

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Samenvatting

This book is a concerted effort to put together the rapidly growing facets of biological data. It provides a platform for the readers to think about integrative approaches to solve complex biological problems. This fundamental book deals with the simplest concepts of omics to recent advancements in the field. The content is divided into seven chapters that provide insight into various omics approaches, omics technologies, and its applications. Each chapter delves into different molecular scales: genomics, transcriptomics, proteomics, and metabolomics. Further to provide a holistic view a chapter detailing microbiome has been included in the book. The sub-sections in the chapters is dedicated to introducing the various analytical tools such as next generation sequencing, chromatin immunoprecipitation, mass spectrometry, peptide mass fingerprinting, RNA Seq and NMR spectroscopy. It entails a chapter focused on the bioinformatics resources for analysis of the omics data. In summary, this comprehensive book emphasizes the recent advancements in the study of biomolecules spanning from DNA to metabolites.

Specificaties

ISBN13:9789811329241

Taal:Engels

Bindwijze:gebonden

Uitgever:Springer Nature Singapore

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Inhoudsopgave

<p>Chapter 1. Introduction to omics.- Chapter 1.1. Background.- Chapter 1.2. Overview of omics.- Chapter 1.3. Overview of systems biology.- Chapter 1.4. Application of R language in omics analysis.- Chapter 2. Genomics.- Chapter 2.1. Introduction.- Chapter 2.2. The Human Genome Project.- Chapter 2.2.1. Mapping of the human genome.- Chapter 2.2.2. DNA sequencing.- Chapter 2.2.3. Genome Annotation.- Chapter 2.2.4. Genomic databases.- Chapter 2.3. Genomic variations.- Chapter 2.4. Functional Genomics.- Chapter 2.4.1. The ENCODE project.- Chapter 2.4.2. Gene expression profiling (DNA microarrays) .- Chapter 2.5. The Non-coding Genome.- Chapter 2.6. Comparative genomics.- Chapter 2.7. Epigenome and Epigenetics.- Chapter 2.7.1. DNA methylation.- Chapter 2.7.2. Histone modifications.- Chapter 2.7.3. Non-coding RNAs.- Chapter 2.7.4. Epigenetic mechanisms (X chromosome inactivation, Genomic imprinting).- Chapter 2.8. Genomic methods for studying complex diseases.- Chapter 2.8.1. GWAS.- Chapter 2.8.2. Next Generation Sequencing.- Chapter 2.8.3. Chromatin immunoprecipitation (ChIP) .- Chapter 2.8.4. Clinical genomics.- Chapter 3. Transcriptomics.- Chapter 3.1. RNA to transcriptome.- Chapter 3.1.1. Transcriptome and Transcriptomics.- Chapter 3.1.2. Principles of Transcriptomics.- Chapter 3.1.3. Technological approach to study Transcriptomes.- Chapter 3.1.3.1. Serial/Cap analysis of gene expression.- Chapter 3.1.3.2. Expression Sequence Tag.- Chapter 3.1.3.3. Microarray.- Chapter 3.1.3.4. RNA-seq.- Chapter 3.2. Metatranscriptome.- Chapter 3.2.1. Gene activity diversity.- Chapter 3.2.2. Gene expression analysis.- Chapter 3.3. Applications.- Chapter 3.3.1. Disease profiling.- Chapter 3.3.2. Ecology.- Chapter 3.3.3. Evolution.- Chapter 3.3.4. Gene function annotation.- Chapter 4. Proteomics.- Chapter 4.1. Protein to proteome.- Chapter 4.1.1. Proteome and Proteomics.- Chapter 4.1.2. Principles of Proteomics.- Chapter 4.1.3. Technological approach to study Proteomes.- Chapter 4.1.3.1. Mass spectrometry.- Chapter 4.1.3.2. Peptide Mass Fingerprinting.- Chapter 4.2. Metaproteome.- Chapter 4.2.1. Protein activity diversity.- Chapter 4.2.2. Protein expression analysis.- Chapter 4.3. Applications.- Chapter 4.3.1. Biomarker discovery.- Chapter 4.3.2. Lead identification.- Chapter 4.3.3. Mapping interaction network.- Chapter 5. Metabolomics.- Chapter 5.1. Metabolites to metabolome.- Chapter 5.2. Data Resources for Metabolomics.- Chapter 5.2.1. EMBL-EBI.- Chapter 5.2.2. BRENDA.- Chapter 5.2.3. HMDD.- Chapter 5.2.4. Sabio RK.- Chapter 5.3. Computational approaches for Metabolomics analysis.- Chapter 5.3.1. Network analysis metabolic pathway integration.- Chapter 5.3.2. Flux analysis.- Chapter 5.4. Applications.- Chapter 6. Microbiome .- Chapter 6.1. Microbe to Microbiome.- Chapter 6.1.1. Soil Microbiome.- Chapter 6.1.2. Plant Microbiome.- Chapter 6.1.3. Marine Microbiome.- Chapter 6.1.4. Human Microbiome.- Chapter 6.2. Host-microbiome interactions.- Chapter 6.2.1. Bacteriome.- Chapter 6.2.2. Mycobiome. .- Chapter 6.2.3. Virome.- Chapter 6.3. Microbiome in health and disease.- Chapter 6.4. Shaping the microbiome.- Chapter 6.5. Sequencing technologies for studying microbiome.- Chapter 6.5.1. 454.- Chapter 6.5.2  .Illumina.- Chapter 6.5.3. SOLiD.- Chapter 6.5.4. Ion Torrent.- Chapter 6.5.5. PacBio.- Chapter 6.6. Future perspectives.- Chapter 6.6.1. Prebiotics.- Chapter 6.6.2. Personalized Medicine.- Chapter 7. Bioinformatics resources .- Chapter 7.1. Bioinformatics approaches in Genomics.- Chapter 7.1.1. Structural genomics.- Chapter 7.1.1.1. Genome sequence assembly.- Chapter 7.1.1.2. Genome annotation.- Chapter 7.1.1.3. Comparative genomics.- Chapter 7.1.2. Functional genomics.- Chapter 7.1.2.1. Sequence-based approaches.- Chapter 7.1.2.2. Microarray-based approaches.- Chapter 7.2. Bioinformatics approaches in Proteomics.- Chapter 7.2.1. Protein expression analysis.- Chapter 7.2.2. Post-translational modifications.- Chapter 7.2.3. Protein-protein interactions.- Chapter 7.3. Bioinformatics approaches in Transciptomics.- Chapter 7.4. Bioinformatics approaches in Metablomics.- Chapter 7.4.1. Metabolomics tools.- Chapter 7.4.2. Metabolomics software.</p>

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