Genomics

Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

Over the past 50 years, scientists have made incredible progress in the application of genetic research to human health care and disease treatment. Innovative tools and techniques, including gene therapy and CRISPR-Cas9 editing, can treat inherited disorders that were previously untreatable, or prevent them from happening in the first place. You can take a DNA test to learn where your ancestors are from. Police officers can use genetic evidence to identify criminals—or innocents. And some doctors are using new medical techniques for unprecedented procedures. Genomics: A Revolution in Health and Disease Discovery delves into the history, science, and ethics behind recent breakthroughs in genetic research. Authors Whitney Stewart and Hans Andersson, MD, present fascinating case studies that show how real people have benefitted from genetic research. Though the genome remains full of mysteries, researchers and doctors are working hard to uncover its secrets and find the best ways to treat patients and cure diseases. The discoveries to come will inform how we target disease treatment, how we understand our health, and how we define our very identities.

Clinical Genomics provides an overview of the various next-generation sequencing (NGS) technologies that are currently used in clinical diagnostic laboratories. It presents key bioinformatic challenges and the solutions that must be addressed by clinical genomicists and genomic pathologists, such as specific pipelines for identification of the full range of variants that are clinically important. This book is also focused on the challenges of diagnostic interpretation of NGS results in a clinical setting. Its final sections are devoted to the emerging regulatory issues that will govern clinical use of NGS, and reimbursement paradigms that will affect the way in which laboratory professionals get paid for the testing. Simplifies complexities of NGS technologies for rapid education of clinical genomicists and genomic pathologists towards genomic medicine paradigm Tried and tested practice-based analysis for precision diagnosis and treatment plans Specific pipelines and meta-analysis for full range of clinically important variants

Genomics in Aquaculture is a concise, must-have reference that describes current advances within the field of genomics and their applications to aquaculture. Written in an accessible manner for anyone—non-specialists to experts alike—this book provides in-depth coverage of genomics spanning from genome sequencing, to transcriptomics and proteomics. It provides, for ease of learning, examples from key species most relevant to current intensive aquaculture practice. Its coverage of minority species that have a specific biological interest (e.g., Pleuronectiformes) makes this book useful for countries that are developing such species. It is a robust, practical resource that covers foundational, functional, and applied aspects of genomics in aquaculture, presenting the most current information in a field of research that is rapidly growing. Provides the latest scientific methods and technologies to maximize efficiencies for healthy fish production, with summary tables for quick reference Offers an extended glossary of technical and methodological terms to help readers better understand key biological concepts Describes state-of-the-art technologies, such as transcriptomics and epigenomics, currently under development for future perspective of the field Covers minority species that have a specific biological interest (e.g., Pleuronectiformes), making the book useful to countries developing such species

The aging of the population of the United States is occurring at a time of major economic and social changes. These economic changes include consideration of increases in the age of eligibility for Social Security and Medicare and possible changes in benefit levels. Furthermore, changes in the social context in which older individuals and families function may well affect the nature of key social relationships and institutions that define the environment for older persons. Sociology offers a knowledge base, a number of useful analytic approaches and tools, and unique theoretical perspectives that can facilitate understanding of these demographic, economic, and social changes and, to the extent possible, their causes, consequences and implications. The Future of the Sociology of Aging: An Agenda for Action evaluates the recent contributions of social demography, social epidemiology and sociology to the study of aging and identifies promising new research directions in these sub-fields. Included in this study are nine papers prepared by experts in sociology, demography, social genomics, public health, and other fields, that highlight the broad array of tools and perspectives that can provide the basis for further advancing the understanding of aging processes in ways that can inform policy. This report discusses the role of sociology in what is a wide-ranging and diverse field of study; a proposed three-dimensional conceptual model for studying social processes in aging over the life cycle; a review of existing databases, data needs and opportunities, primarily in the area of measurement of interhousehold and intergenerational transmission of resources, biomarkers and biosocial interactions; and a summary of roadblocks and bridges to transdisciplinary research that will affect the future directions of the field of sociology of aging.

Population genomics has revolutionized various disciplines of biology including population, evolutionary, ecological and conservation genetics, plant and animal breeding, human health, medicine and pharmacology by allowing to address novel and long-standing questions with unprecedented power and accuracy. It employs large-scale or genome-wide genetic information and bioinformatics to address various fundamental and applied aspects in biology and related disciplines, and provides a comprehensive genome-wide perspective and new insights that were not possible before. These advances have become possible due to the development of new and low-cost sequencing and genotyping technologies and novel statistical approaches and software, bioinformatics tools, and models. Population genomics is tremendously advancing our understanding the roles of evolutionary processes, such as mutation, genetic drift, gene flow, and natural selection, in shaping up genetic variation at individual loci and across the genome and populations; improving the assessment of population genetic parameters or processes such as adaptive evolution, effective population size, gene flow, admixture, inbreeding and outbreeding depression, demography, and biogeography; resolving evolutionary histories and phylogenetic relationships of extant, ancient and extinct species; understanding the genomic basis of fitness, adaptation, speciation, complex ecological and economically important traits, and disease and insect resistance; facilitating forensics, genetic medicine and pharmacology; delineating conservation genetic units; and understanding the genetic effects of resource management practices, and assisting conservation and sustainable management of genetic resources. This Population Genomics book discusses the concepts, approaches, applications and promises of population genomics in addressing most of the above fundamental and applied crucial aspects in a variety of organisms from microorganisms to humans. The book provides insights into a range of emerging population genomics topics including population epigenomics, landscape genomics, seascape genomics, paleogenomics, ecological and evolutionary genomics, biogeography, demography, speciation, admixture, colonization and invasion, genomic selection, and plant and animal domestication. This book fills a vacuum in the field and is expected to become a primary reference in Population Genomics world-wide.

Data in the genomics field is booming. In just a few years, organizations such as the National Institutes of Health (NIH) will host 50+ petabytes—or over 50 million gigabytes—of genomic data, and they’re turning to cloud infrastructure to make that data available to the research community. How do you adapt analysis tools and protocols to access and analyze that volume of data in the cloud? With this practical book, researchers will learn how to work with genomics algorithms using open source tools including the Genome Analysis Toolkit (GATK), Docker, WDL, and Terra. Geraldine Van der Auwera, longtime custodian of the GATK user community, and Brian O’Connor of the UC Santa Cruz Genomics Institute, guide you through the process. You’ll learn by working with real data and genomics algorithms from the field. This book covers: Essential genomics and computing technology background Basic cloud computing operations Getting started with GATK, plus three major GATK Best Practices pipelines Automating analysis with scripted workflows using WDL and Cromwell Scaling up workflow execution in the cloud, including parallelization and cost optimization Interactive analysis in the cloud using Jupyter notebooks Secure collaboration and computational reproducibility using Terra