Methods In Statistical Genomics
DOWNLOAD
Download Methods In Statistical Genomics PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Methods In Statistical Genomics book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Statistical Genomics
DOWNLOAD
Author : Ewy Mathé
language : en
Publisher: Humana
Release Date : 2016-03-24
Statistical Genomics written by Ewy Mathé and has been published by Humana this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-24 with Medical categories.
This volume expands on statistical analysis of genomic data by discussing cross-cutting groundwork material, public data repositories, common applications, and representative tools for operating on genomic data. Statistical Genomics: Methods and Protocols is divided into four sections. The first section discusses overview material and resources that can be applied across topics mentioned throughout the book. The second section covers prominent public repositories for genomic data. The third section presents several different biological applications of statistical genomics, and the fourth section highlights software tools that can be used to facilitate ad-hoc analysis and data integration. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, step-by-step, readily reproducible analysis protocols, and tips on troubleshooting and avoiding known pitfalls. Through and practical, Statistical Genomics: Methods and Protocols, explores a range of both applications and tools and is ideal for anyone interested in the statistical analysis of genomic data.
Methods In Statistical Genomics
DOWNLOAD
Author : Philip Chester Cooley
language : en
Publisher: RTI Press
Release Date : 2016-08-29
Methods In Statistical Genomics written by Philip Chester Cooley and has been published by RTI Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-29 with Medical categories.
The objective of this book is to describe procedures for analyzing genome-wide association studies (GWAS). Some of the material is unpublished and contains commentary and unpublished research; other chapters (Chapters 4 through 7) have been published in other journals. Each previously published chapter investigates a different genomics model, but all focus on identifying the strengths and limitations of various statistical procedures that have been applied to different GWAS scenarios.
Handbook Of Statistical Genomics
DOWNLOAD
Author : David J. Balding
language : en
Publisher: John Wiley & Sons
Release Date : 2019-07-02
Handbook Of Statistical Genomics written by David J. Balding and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-02 with Science categories.
A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics.
Principles Of Statistical Genomics
DOWNLOAD
Author : Shizhong Xu
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-09-10
Principles Of Statistical Genomics written by Shizhong Xu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-09-10 with Science categories.
Statistical genomics is a rapidly developing field, with more and more people involved in this area. However, a lack of synthetic reference books and textbooks in statistical genomics has become a major hurdle on the development of the field. Although many books have been published recently in bioinformatics, most of them emphasize DNA sequence analysis under a deterministic approach. Principles of Statistical Genomics synthesizes the state-of-the-art statistical methodologies (stochastic approaches) applied to genome study. It facilitates understanding of the statistical models and methods behind the major bioinformatics software packages, which will help researchers choose the optimal algorithm to analyze their data and better interpret the results of their analyses. Understanding existing statistical models and algorithms assists researchers to develop improved statistical methods to extract maximum information from their data. Resourceful and easy to use, Principles of Statistical Genomics is a comprehensive reference for researchers and graduate students studying statistical genomics.
Model Based Analysis Methods In Statistical Genomics
DOWNLOAD
Author :
language : en
Publisher:
Release Date : 2012
Model Based Analysis Methods In Statistical Genomics written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
Model-based Analysis Methods in Statistical Genomics Qiuling He Under the supervision of Professor Michael A. Newton At the University of Wisconsin-Madison This thesis aims to solve two problems in statistical genomics: (1) how to model agreement among genome-wide RNA interference (RNAi) studies; and (2) how to integrate experimentally derived genomic data with functional annotations. The problems are distinct in their specific elements but share two important features: (1) solutions could have significant implications for the practice of statistical genomics, and (2) our approaches to solve them use common model-based tools and techniques. The RNAi analysis concerns four recent genome-wide studies of influenza virus replication. All studies identified genes whose inactivation alters a cell's ability to produce virus, and they all had a similar experimental design. In total 614 human genes were confirmed to have an affect on viral replication, however there were very limited agreement between the studies. For instance, only one gene was confirmed by all four studies. The apparent lack of agreement raises questions about the rate of false positives and false negatives in genome-wide RNAi. We develop a generative sampling model to describe the RNAi data, and with likelihood methods we use this model to assess the relative magnitude of false positive and false negative effects. The model accommodates many aspects of RNAi, but it is sufficiently simple that closed form inference summaries are available. Evidence points to a relatively high false negative rate. In the second part of the thesis, we investigate the problem of genomic data integration, specifically, the problem of integrating experimentally derived data with data on the known functional profiles of the annotated genes. Such functional category analysis is important to data reduction and for weak-signal identification, though state-of-the-art methodology does not adequately handle the complexity of growing systems of functional categories. We show that a leading model-based empirical Bayesian approach suffers inconsistency and inefficiency, and we propose a new approach to connect these problems.
Statistical Genomics
DOWNLOAD
Author : Brooke Fridley
language : en
Publisher: Springer Nature
Release Date : 2023-03-16
Statistical Genomics written by Brooke Fridley and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-16 with Science categories.
This volume provides a collection of protocols from researchers in the statistical genomics field. Chapters focus on integrating genomics with other “omics” data, such as transcriptomics, epigenomics, proteomics, metabolomics, and metagenomics. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Statistical Genomics hopes that by covering these diverse and timely topics researchers are provided insights into future directions and priorities of pan-omics and the precision medicine era.
Statistical Genomics
DOWNLOAD
Author : Ben Hui Liu
language : en
Publisher: CRC Press
Release Date : 2017-11-22
Statistical Genomics written by Ben Hui Liu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-22 with Mathematics categories.
Genomics, the mapping of the entire genetic complement of an organism, is the new frontier in biology. This handbook on the statistical issues of genomics covers current methods and the tried-and-true classical approaches.
Mathematical And Statistical Methods For Genetic Analysis
DOWNLOAD
Author : Kenneth Lange
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Mathematical And Statistical Methods For Genetic Analysis written by Kenneth Lange and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Medical categories.
During the past decade, geneticists have cloned scores of Mendelian disease genes and constructed a rough draft of the entire human genome. The unprecedented insights into human disease and evolution offered by mapping, cloning, and sequencing will transform medicine and agriculture. This revolution depends vitally on the contributions of applied mathematicians, statisticians, and computer scientists. Mathematical and Statistical Methods for Genetic Analysis is written to equip students in the mathematical sciences to understand and model the epidemiological and experimental data encountered in genetics research. Mathematical, statistical, and computational principles relevant to this task are developed hand in hand with applications to population genetics, gene mapping, risk prediction, testing of epidemiological hypotheses, molecular evolution, and DNA sequence analysis. Many specialized topics are covered that are currently accessible only in journal articles. This second edition expands the original edition by over 100 pages and includes new material on DNA sequence analysis, diffusion processes, binding domain identification, Bayesian estimation of haplotype frequencies, case-control association studies, the gamete competition model, QTL mapping and factor analysis, the Lander-Green-Kruglyak algorithm of pedigree analysis, and codon and rate variation models in molecular phylogeny. Sprinkled throughout the chapters are many new problems.
Statistical Methods In Molecular Evolution
DOWNLOAD
Author : Rasmus Nielsen
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-04-21
Statistical Methods In Molecular Evolution written by Rasmus Nielsen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-04-21 with Science categories.
In the field of molecular evolution, inferences about past evolutionary events are made using molecular data from currently living species. With the availability of genomic data from multiple related species, molecular evolution has become one of the most active and fastest growing fields of study in genomics and bioinformatics. Most studies in molecular evolution rely heavily on statistical procedures based on stochastic process modelling and advanced computational methods including high-dimensional numerical optimization and Markov Chain Monte Carlo. This book provides an overview of the statistical theory and methods used in studies of molecular evolution. It includes an introductory section suitable for readers that are new to the field, a section discussing practical methods for data analysis, and more specialized sections discussing specific models and addressing statistical issues relating to estimation and model choice. The chapters are written by the leaders of field and they will take the reader from basic introductory material to the state-of-the-art statistical methods. This book is suitable for statisticians seeking to learn more about applications in molecular evolution and molecular evolutionary biologists with an interest in learning more about the theory behind the statistical methods applied in the field. The chapters of the book assume no advanced mathematical skills beyond basic calculus, although familiarity with basic probability theory will help the reader. Most relevant statistical concepts are introduced in the book in the context of their application in molecular evolution, and the book should be accessible for most biology graduate students with an interest in quantitative methods and theory. Rasmus Nielsen received his Ph.D. form the University of California at Berkeley in 1998 and after a postdoc at Harvard University, he assumed a faculty position in Statistical Genomics at Cornell University. He is currently an Ole Rømer Fellow at the University of Copenhagen and holds a Sloan Research Fellowship. His is an associate editor of the Journal of Molecular Evolution and has published more than fifty original papers in peer-reviewed journals on the topic of this book. From the reviews: "...Overall this is a very useful book in an area of increasing importance." Journal of the Royal Statistical Society "I find Statistical Methods in Molecular Evolution very interesting and useful. It delves into problems that were considered very difficult just several years ago...the book is likely to stimulate the interest of statisticians that are unaware of this exciting field of applications. It is my hope that it will also help the 'wet lab' molecular evolutionist to better understand mathematical and statistical methods." Marek Kimmel for the Journal of the American Statistical Association, September 2006 "Who should read this book? We suggest that anyone who deals with molecular data (who does not?) and anyone who asks evolutionary questions (who should not?) ought to consult the relevant chapters in this book." Dan Graur and Dror Berel for Biometrics, September 2006 "Coalescence theory facilitates the merger of population genetics theory with phylogenetic approaches, but still, there are mostly two camps: phylogeneticists and population geneticists. Only a few people are moving freely between them. Rasmus Nielsen is certainly one of these researchers, and his work so far has merged many population genetic and phylogenetic aspects of biological research under the umbrella of molecular evolution. Although Nielsen did not contribute a chapter to his book, his work permeates all its chapters. This book gives an overview of his interests and current achievements in molecular evolution. In short, this book should be on your bookshelf." Peter Beerli for Evolution, 60(2), 2006
Computational And Statistical Approaches To Genomics
DOWNLOAD
Author : Wei Zhang
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-05-08
Computational And Statistical Approaches To Genomics written by Wei Zhang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-05-08 with Science categories.
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.