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Author: Matan Hofree
Publisher:
ISBN: 9781321532371
Category :
Languages : en
Pages : 146
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Book Description
Genome-wide measurements of genomic state offer unprecedented opportunities for biological discovery, with potential to make dramatic impact on medicine and life. One fundamental challenge is associating complex phenotypes with genetic cause. Here, I will describe efforts to advance solutions to this challenge via analysis of gene networks. Genome-wide association studies are designed link between a phenotype and genomic loci anywhere in the genome; however, applying standard statistics to such data has fallen far short of building accurate predictive models for disease. We use Adaboost, a large-margin classification algorithm, to predict disease status in two cohorts of diabetes and suggest a method for overcoming limitations arising from correlation between genetic variants. We uncover a novel set of 163 disease-associations, missed by `classic' statistics. Classification of cancer remains predominantly organ based and fails to account for considerable heterogeneity of outcomes. Tumor genomes provide a new source of data for uncovering subtypes, but are difficult to compare, as tumors share few mutations in common. We introduce network-based stratification (NBS), a method for integrating somatic genomes with networks encoding biological knowledge. This allows for identification of cancer subtypes by clustering tumors with mutations in similar network regions. We demonstrate NBS in multiple cancer cohorts, identifying subtypes predictive of clinical features and outcomes, and highlighting sub-networks characteristic of each. Current approaches for identifying cancer genes rely on the idea that particular perturbations, occurring in a subset of genes unique to each cancer type, are selected for by conferring a survival advantage to tumor cells. Such genes are expected to be enriched for mutations when examined across a population. Here we show that 30-50% of well-known cancer genes are not significantly elevated in mutation frequency. Despite this lack of enrichment, known cancer genes are enriched for mutations causing changes in amino-acid composition, protein structure properties and conservation. Furthermore, we observe 15-30% of cancer genes have altered mutation rates conditioned on other genes, each individually spanning the range of single-gene mutation frequencies, implicating a large genetic interaction network underlying human cancer. This suggests a substantial number of cancer genes will never be identified by frequency alone.
Author: Matan Hofree
Publisher:
ISBN: 9781321532371
Category :
Languages : en
Pages : 146
Get Book Here
Book Description
Genome-wide measurements of genomic state offer unprecedented opportunities for biological discovery, with potential to make dramatic impact on medicine and life. One fundamental challenge is associating complex phenotypes with genetic cause. Here, I will describe efforts to advance solutions to this challenge via analysis of gene networks. Genome-wide association studies are designed link between a phenotype and genomic loci anywhere in the genome; however, applying standard statistics to such data has fallen far short of building accurate predictive models for disease. We use Adaboost, a large-margin classification algorithm, to predict disease status in two cohorts of diabetes and suggest a method for overcoming limitations arising from correlation between genetic variants. We uncover a novel set of 163 disease-associations, missed by `classic' statistics. Classification of cancer remains predominantly organ based and fails to account for considerable heterogeneity of outcomes. Tumor genomes provide a new source of data for uncovering subtypes, but are difficult to compare, as tumors share few mutations in common. We introduce network-based stratification (NBS), a method for integrating somatic genomes with networks encoding biological knowledge. This allows for identification of cancer subtypes by clustering tumors with mutations in similar network regions. We demonstrate NBS in multiple cancer cohorts, identifying subtypes predictive of clinical features and outcomes, and highlighting sub-networks characteristic of each. Current approaches for identifying cancer genes rely on the idea that particular perturbations, occurring in a subset of genes unique to each cancer type, are selected for by conferring a survival advantage to tumor cells. Such genes are expected to be enriched for mutations when examined across a population. Here we show that 30-50% of well-known cancer genes are not significantly elevated in mutation frequency. Despite this lack of enrichment, known cancer genes are enriched for mutations causing changes in amino-acid composition, protein structure properties and conservation. Furthermore, we observe 15-30% of cancer genes have altered mutation rates conditioned on other genes, each individually spanning the range of single-gene mutation frequencies, implicating a large genetic interaction network underlying human cancer. This suggests a substantial number of cancer genes will never be identified by frequency alone.
Author: Ju Han Kim
Publisher: Springer
ISBN: 9811319421
Category : Science
Languages : en
Pages : 367
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Book Description
This textbook describes recent advances in genomics and bioinformatics and provides numerous examples of genome data analysis that illustrate its relevance to real world problems and will improve the reader’s bioinformatics skills. Basic data preprocessing with normalization and filtering, primary pattern analysis, and machine learning algorithms using R and Python are demonstrated for gene-expression microarrays, genotyping microarrays, next-generation sequencing data, epigenomic data, and biological network and semantic analyses. In addition, detailed attention is devoted to integrative genomic data analysis, including multivariate data projection, gene-metabolic pathway mapping, automated biomolecular annotation, text mining of factual and literature databases, and integrated management of biomolecular databases. The textbook is primarily intended for life scientists, medical scientists, statisticians, data processing researchers, engineers, and other beginners in bioinformatics who are experiencing difficulty in approaching the field. However, it will also serve as a simple guideline for experts unfamiliar with the new, developing subfield of genomic analysis within bioinformatics.
Author: Eleftheria Zeggini
Publisher: Springer
ISBN: 1493928244
Category : Medical
Languages : en
Pages : 262
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Book Description
This book is unique in covering a wide range of design and analysis issues in genetic studies of rare variants, taking advantage of collaboration of the editors with many experts in the field through large-scale international consortia including the UK10K Project, GO-T2D and T2D-GENES. Chapters provide details of state-of-the-art methodology for rare variant detection and calling, imputation and analysis in samples of unrelated individuals and families. The book also covers analytical issues associated with the study of rare variants, such as the impact of fine-scale population structure, and with combining information on rare variants across studies in a meta-analysis framework. Genetic association studies have in the last few years substantially enhanced our understanding of factors underlying traits of high medical importance, such as body mass index, lipid levels, blood pressure and many others. There is growing empirical evidence that low-frequency and rare variants play an important role in complex human phenotypes. This book covers multiple aspects of study design, analysis and interpretation for complex trait studies focusing on rare sequence variation. In many areas of genomic research, including complex trait association studies, technology is in danger of outstripping our capacity to analyse and interpret the vast amounts of data generated. The field of statistical genetics in the whole-genome sequencing era is still in its infancy, but powerful methods to analyse the aggregation of low-frequency and rare variants are now starting to emerge. The chapter Functional Annotation of Rare Genetic Variants is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Author: Jan Engels
Publisher: Cambridge University Press
ISBN: 9780521384568
Category : Science
Languages : en
Pages : 404
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Book Description
One of the world centers of crop evolution and origin, Ethiopia has long been recognized as an important area of diversity for several major and various minor crops. Based on an international conference held in Addis Ababa, this book describes how plant genetic diversity in Ethiopia is of vital importance in breeding new varieties of crops with desirable characteristics, such as increased resistance to pests and diseases and greater adaptation to heat and drought. The three main sections in the book consider the Ethiopian center of diversity, germ plasm or genetic material collection and conservation in Ethiopia, and the evaluation and utilization of Ethiopian genetic resources. A broad range of food and feed crops and plants of medicinal and industrial importance are discussed, both at a national and international level. A brief account of conservation strategies and gene bank problems unique to Ethiopia is also given. The importance of Ethiopia's plant genetic resources to world agriculture has been demonstrated on more than one occasion. Plant breeders, geneticists, and botanists throughout the world will, therefore, find this unique book a valuable source of information and an essential reference work.
Author: D. Frishman
Publisher: Springer Science & Business Media
ISBN: 3211751238
Category : Science
Languages : en
Pages : 491
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Book Description
An accurate description of current scientific developments in the field of bioinformatics and computational implementation is presented by research of the BioSapiens Network of Excellence. Bioinformatics is essential for annotating the structure and function of genes, proteins and the analysis of complete genomes and to molecular biology and biochemistry. Included is an overview of bioinformatics, the full spectrum of genome annotation approaches including; genome analysis and gene prediction, gene regulation analysis and expression, genome variation and QTL analysis, large scale protein annotation of function and structure, annotation and prediction of protein interactions, and the organization and annotation of molecular networks and biochemical pathways. Also covered is a technical framework to organize and represent genome data using the DAS technology and work in the annotation of two large genomic sets: HIV/HCV viral genomes and splicing alternatives potentially encoded in 1% of the human genome.
Author: Daniel Edmund Newburger
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
The study of genomic variation within human populations is critical for elucidating the genetic factors that contribute to disease. Identifying and characterizing the genetic architecture of disease advances clinical care by facilitating the development of novel diagnostic tools, the identification of new therapeutic targets, and the practice of personalized treatment for genetic syndromes. The massive volume of genetic data generated by modern genotyping technologies, combined with the informatics challenges of filtering and interpreting these noisy measurements, represent significant obstacles to genomic research. These technical issues necessitate the development of computationally efficient methodologies that leverage raw genotype data for the comparative genomic analysis of complex phenotypes across human subpopulations. In this dissertation, I describe my contributions towards the biomedical study of genetic syndromes using high-throughput genotyping technologies. First, I discuss methods for studying the genome evolution of pre-malignant cancer lesions during progression to breast cancer. Second, I describe algorithms for performing highly accurate variant validation in genomic studies using next generation sequencing. Finally, I present methods for identifying novel disease susceptibility loci in complex diseases using identity by descent mapping in large case-control cohorts.
Author: Wei Zhang
Publisher: Springer Science & Business Media
ISBN: 0306478250
Category : Science
Languages : en
Pages : 345
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Book Description
Computational and Statistical Genomics aims to help researchers deal with current genomic challenges. Topics covered include: overviews of the role of supercomputers in genomics research, the existing challenges and directions in image processing for microarray technology, and web-based tools for microarray data analysis; approaches to the global modeling and analysis of gene regulatory networks and transcriptional control, using methods, theories, and tools from signal processing, machine learning, information theory, and control theory; state-of-the-art tools in Boolean function theory, time-frequency analysis, pattern recognition, and unsupervised learning, applied to cancer classification, identification of biologically active sites, and visualization of gene expression data; crucial issues associated with statistical analysis of microarray data, statistics and stochastic analysis of gene expression levels in a single cell, statistically sound design of microarray studies and experiments; and biological and medical implications of genomics research.
Author: Edenilson Brandl
Publisher: Edenilson Brandl
ISBN:
Category : Health & Fitness
Languages : en
Pages : 267
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Book Description
It is with great enthusiasm that I present to you the book "Genomic Biointelligence". This book is a fascinating journey through the ever-evolving world of genomics and artificial intelligence, exploring their intersection and the role of the genomic biointelligence within this context. Genomics has revolutionized our understanding of the genetic code and brought with it a vast volume of data that challenges our ability to analyze and interpret. On the other hand, artificial intelligence has emerged as a powerful tool to deal with this complexity and extract valuable information from genomic data. Within the pages of this book, you will be guided on a comprehensive journey through key topics related to the application of artificial intelligence in genomics. From the history and evolution of artificial intelligence in genomics research to the latest applications in diagnostics, drug discovery, precision medicine and disease research, each chapter presents an important aspect of this rapidly expanding field. You will learn about genetic algorithms and their application in genomics, mathematical modeling of genomic regulatory networks, the use of neural networks in predicting protein structures, and much more. We will also discuss the challenges and limitations of using artificial intelligence in genomics, as well as ethical issues and the importance of data privacy. In addition, we will highlight the fundamental role of the genomic biointelligencist, a multidisciplinary professional who combines knowledge in genomics, artificial intelligence, bioinformatics and other related areas. The genomic biointelligence plays a crucial role in applying artificial intelligence to advance genomic research, discover new treatments, develop personalized therapies, and drive precision medicine. As we progress through this book, you will be invited to explore recent advances and the exciting possibilities that arise from the combination of genomics and artificial intelligence. Through practical examples, case studies and in-depth discussions, we hope to provide you with a solid understanding of the concepts and applications of this rapidly expanding field. Finally, I would like to express my gratitude to all the experts and researchers who contributed their unique knowledge and insights to this book. Their efforts and dedication are instrumental in advancing the field of genomics and artificial intelligence. I hope you will find this book a valuable source of information and inspiration. May it arouse your curiosity, stimulate discussions and motivate you to further explore the frontiers of knowledge in the field of genomics and artificial intelligence.
Author: Klaus Schughart
Publisher: Humana
ISBN: 9781493964253
Category : Medical
Languages : en
Pages : 0
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Book Description
This volume focuses on the use of system genetic methods and the use of murine models to study the role of gene variants and environmental factors on human health and disease—what is now often called personalized or precision health care. The protocols in this book will help readers analyze genetic causes of heritable variation across a wide range of systems and traits using rodent models. The chapters in this book are separated into three sections that cover: 1) resources for systems genetics; 2) tools for analysis and integration in systems genetics; and 3) systems genetics use cases. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and tools, step-by-step, readily reproducible protocols, and tips on troubleshooting and avoiding known pitfalls. Practical and thorough, Systems Genetics: Methods and Protocols is a valuable resource for anyone who is interested in this diverse field.
Author: Ibrokhim Y. Abdurakhmonov
Publisher: BoD – Books on Demand
ISBN: 1789232686
Category : Medical
Languages : en
Pages : 190
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Book Description
Bioinformatics has evolved significantly in the era of post genomics and big data. Huge advancements were made toward storing, handling, mining, comparing, extracting, clustering and analysis as well as visualization of big macromolecular data using novel computational approaches, machine and deep learning methods, and web-based server tools. There are extensively ongoing world-wide efforts to build the resources for regional hosting, organized and structured access and improving the pre-existing bioinformatics tools to efficiently and meaningfully analyze day-to-day increasing big data. This book intends to provide the reader with updates and progress on genomic data analysis, data modeling and network-based system tools.
