Human genome informatics : translating genes into health /

Human Genome Informatics: Translating Genes into Health examines the most commonly used electronic tools for translating genomic information into clinically meaningful formats. By analyzing and comparing interpretation methods of whole genome data, the book discusses the possibilities of their appli...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Lambert, Christophe G. (Editor ), Baker, Darrol J. (Editor ), Patrinos, George P. (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London, United Kingdom : Academic Press, an imprint of Elsevier, [2018]
Colección:Translational and applied genomics series.
Temas:
Genomics.
Human genome.
Genome, Human
Medical Informatics
Databases, Genetic
Big Data
Models, Genetic
Computational Biology > methods
Genomics
G�enomique.
G�enome humain.
M�edecine > Informatique.
SCIENCE > Life Sciences > Biochemistry.
Human genome
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Front Cover; Human Genome Informatics: Translating Genes into Health; Copyright; Contents; Contributors; Preface; Chapter 1: Human Genome Informatics: Coming of Age; 1.1. Introduction; 1.2. From Informatics to Bioinformatics and Genome Informatics; 1.3. Informatics in Genomics Research and Clinical Applications; 1.3.1. Genome Informatics Analysis; 1.3.2. Genomics Data Sharing; 1.3.3. Genomic Variant Reporting and Annotation Tools; 1.4. Pharmacogenomics and Genome Informatics; 1.5. Databases, Artificial Intelligence, and Big-Data in Genomics; 1.6. Conclusions; Acknowledgments; References
  • Part 1: Human Genome Informatics ApplicationsChapter 2: Creating Transparent and Reproducible Pipelines: Best Practices for Tools, Data, and Workflow Management Systems; 2.1. Introduction; 2.2. Existing Workflow Environment; 2.3. What Software Should Be Part of a Scientific Workflow?; 2.4. Preparing Data for Automatic Workflow Analysis; 2.5. Quality Criteria for Modern Workflow Environments; 2.5.1. Being Able to Embed and to Be Embedded; 2.5.2. Support Ontologies; 2.5.3. Support Virtualization; 2.5.4. Offer Easy Access to Commonly Used Datasets
  • 2.5.5. Support and Standardize Data Visualization2.5.6. Enable ""Batteries Included"" Workflow Environments; 2.5.7. Facilitate Data Integration, Both for Import and Export; 2.5.8. Offer Gateways for High Performance Computing Environments; 2.5.9. Engage Users in Collaborative Experimentation and Scientific Authoring; 2.6. Benefits From Integrated Workflow Analysis in Bioinformatics; 2.6.1. Enable Meta-Studies, Combine Datasets, and Increase Statistical Power; 2.6.2. Include Methods and Data From Other Research Disciplines; 2.6.3. Fight the Reproducibility Crisis
  • 2.6.4. Spread of Open Source Policies in Genetics and Privacy Protection2.6.5. Help Clinical Genetics Research; 2.7. Discussion; References; Chapter 3: How Cytogenetics Paradigms Shape Decision Making in Translational Genomics; 3.1. Introduction; 3.2. Clinical Cytogenetic Testing; 3.2.1. Karyotyping; 3.2.2. Chromosomal Microarrays; 3.2.2.1. Resolving the Conflict; 3.2.2.2. Capitalizing on the Conflict; 3.3. From Cytogenetics to Cytogenomics in the Era of Next-Generation Sequencing; 3.4. Conclusions; References
  • Chapter 4: An Introduction to Tools, Databases, and Practical Guidelines for NGS Data Analysis4.1. Introduction; 4.2. Data Formats; 4.3. Data Sources; 4.4. Variant Data; 4.5. NGS Pipelines; 4.5.1. Read Alignment; 4.5.2. Variant Calling; 4.5.3. Downstream Analysis; 4.5.4. RNA-seq; 4.5.5. ChIP-Seq; 4.6. Discussion; References; Chapter 5: Proteomics and Metabolomics Data Analysis for Translational Medicine; 5.1. Introduction; 5.2. The Need to Bridge the Gaps in the Era of Precision Medicine; 5.3. Clinical Proteomics; 5.3.1. The Power of the Proteome

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