Statistical Methods For High Throughput Genomics

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Optimal High-Throughput Screening

Author : Xiaohua Douglas Zhang
Publisher : Cambridge University Press
ISBN 13 : 1139498371
Total Pages : 223 pages
Book Rating : 4.1/5 (394 download)

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Book Synopsis Optimal High-Throughput Screening by : Xiaohua Douglas Zhang

Download or read book Optimal High-Throughput Screening written by Xiaohua Douglas Zhang and published by Cambridge University Press. This book was released on 2011-02-21 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This concise, self-contained and cohesive book focuses on commonly used and recently developed methods for designing and analyzing high-throughput screening (HTS) experiments from a statistically sound basis. Combining ideas from biology, computing and statistics, the author explains experimental designs and analytic methods that are amenable to rigorous analysis and interpretation of RNAi HTS experiments. The opening chapters are carefully presented to be accessible both to biologists with training only in basic statistics and to computational scientists and statisticians with basic biological knowledge. Biologists will see how new experiment designs and rudimentary data-handling strategies for RNAi HTS experiments can improve their results, whereas analysts will learn how to apply recently developed statistical methods to interpret HTS experiments.

Computational Genomics with R

Author : Altuna Akalin
Publisher : CRC Press
ISBN 13 : 1498781861
Total Pages : 463 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Computational Genomics with R by : Altuna Akalin

Download or read book Computational Genomics with R written by Altuna Akalin and published by CRC Press. This book was released on 2020-12-16 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

High-dimensional Data Analysis

Author : Tony Cai;Xiaotong Shen
Publisher :
ISBN 13 : 9787894236326
Total Pages : 318 pages
Book Rating : 4.2/5 (363 download)

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Book Synopsis High-dimensional Data Analysis by : Tony Cai;Xiaotong Shen

Download or read book High-dimensional Data Analysis written by Tony Cai;Xiaotong Shen and published by . This book was released on with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last few years, significant developments have been taking place in highdimensional data analysis, driven primarily by a wide range of applications in many fields such as genomics and signal processing. In particular, substantial advances have been made in the areas of feature selection, covariance estimation, classification and regression. This book intends to examine important issues arising from highdimensional data analysis to explore key ideas for statistical inference and prediction. It is structured around topics on multiple hypothesis testing, feature selection, regression, cla.

Bioinformatics for High Throughput Sequencing

Author : Naiara Rodríguez-Ezpeleta
Publisher : Springer Science & Business Media
ISBN 13 : 1461407826
Total Pages : 258 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Bioinformatics for High Throughput Sequencing by : Naiara Rodríguez-Ezpeleta

Download or read book Bioinformatics for High Throughput Sequencing written by Naiara Rodríguez-Ezpeleta and published by Springer Science & Business Media. This book was released on 2011-10-26 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Next generation sequencing is revolutionizing molecular biology. Owing to this new technology it is now possible to carry out a panoply of experiments at an unprecedented low cost and high speed. These go from sequencing whole genomes, transcriptomes and small non-coding RNAs to description of methylated regions, identification protein – DNA interaction sites and detection of structural variation. The generation of gigabases of sequence information for each of this huge bandwidth of applications in just a few days makes the development of bioinformatics applications for next generation sequencing data analysis as urgent as challenging.

Statistical Analysis of Next Generation Sequencing Data

Author : Somnath Datta
Publisher : Springer
ISBN 13 : 9783319379050
Total Pages : 0 pages
Book Rating : 4.3/5 (79 download)

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Book Synopsis Statistical Analysis of Next Generation Sequencing Data by : Somnath Datta

Download or read book Statistical Analysis of Next Generation Sequencing Data written by Somnath Datta and published by Springer. This book was released on 2016-09-17 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians working with NGS data. The topics range from basic preprocessing and analysis with NGS data to more complex genomic applications such as copy number variation and isoform expression detection. Research statisticians who want to learn about this growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected to deal with genomic data in basic biomedical research, genomic clinical trials and personalized medicine. About the editors: Somnath Datta is Professor and Vice Chair of Bioinformatics and Biostatistics at the University of Louisville. He is Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics and Elected Member of the International Statistical Institute. He has contributed to numerous research areas in Statistics, Biostatistics and Bioinformatics. Dan Nettleton is Professor and Laurence H. Baker Endowed Chair of Biological Statistics in the Department of Statistics at Iowa State University. He is Fellow of the American Statistical Association and has published research on a variety of topics in statistics, biology and bioinformatics.

Handbook of Statistical Genomics

Author : David J. Balding
Publisher : John Wiley & Sons
ISBN 13 : 1119429250
Total Pages : 1740 pages
Book Rating : 4.1/5 (194 download)

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Book Synopsis Handbook of Statistical Genomics by : David J. Balding

Download or read book Handbook of Statistical Genomics written by David J. Balding and published by John Wiley & Sons. This book was released on 2019-07-09 with total page 1740 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Fundamentals of Data Mining in Genomics and Proteomics

Author : Werner Dubitzky
Publisher : Springer Science & Business Media
ISBN 13 : 0387475095
Total Pages : 300 pages
Book Rating : 4.3/5 (874 download)

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Book Synopsis Fundamentals of Data Mining in Genomics and Proteomics by : Werner Dubitzky

Download or read book Fundamentals of Data Mining in Genomics and Proteomics written by Werner Dubitzky and published by Springer Science & Business Media. This book was released on 2007-04-13 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents state-of-the-art analytical methods from statistics and data mining for the analysis of high-throughput data from genomics and proteomics. It adopts an approach focusing on concepts and applications and presents key analytical techniques for the analysis of genomics and proteomics data by detailing their underlying principles, merits and limitations.

Statistical Theory and Methods for Evolutionary Genomics

Author : Xun Gu
Publisher : OUP Oxford
ISBN 13 : 0199213267
Total Pages : 272 pages
Book Rating : 4.1/5 (992 download)

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Book Synopsis Statistical Theory and Methods for Evolutionary Genomics by : Xun Gu

Download or read book Statistical Theory and Methods for Evolutionary Genomics written by Xun Gu and published by OUP Oxford. This book was released on 2010-11-04 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summarises the statistical framework of evolutionary genomics, and illustrates how statistical modelling and testing can enhance our understanding of functional genomic evolution.

Sample Size Calculations in Clinical Research

Author : Shein-Chung Chow
Publisher : CRC Press
ISBN 13 : 1351727117
Total Pages : 825 pages
Book Rating : 4.3/5 (517 download)

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Book Synopsis Sample Size Calculations in Clinical Research by : Shein-Chung Chow

Download or read book Sample Size Calculations in Clinical Research written by Shein-Chung Chow and published by CRC Press. This book was released on 2017-08-15 with total page 825 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for the Second Edition: "... this is a useful, comprehensive compendium of almost every possible sample size formula. The strong organization and carefully defined formulae will aid any researcher designing a study." -Biometrics "This impressive book contains formulae for computing sample size in a wide range of settings. One-sample studies and two-sample comparisons for quantitative, binary, and time-to-event outcomes are covered comprehensively, with separate sample size formulae for testing equality, non-inferiority, and equivalence. Many less familiar topics are also covered ..." – Journal of the Royal Statistical Society Sample Size Calculations in Clinical Research, Third Edition presents statistical procedures for performing sample size calculations during various phases of clinical research and development. A comprehensive and unified presentation of statistical concepts and practical applications, this book includes a well-balanced summary of current and emerging clinical issues, regulatory requirements, and recently developed statistical methodologies for sample size calculation. Features: Compares the relative merits and disadvantages of statistical methods for sample size calculations Explains how the formulae and procedures for sample size calculations can be used in a variety of clinical research and development stages Presents real-world examples from several therapeutic areas, including cardiovascular medicine, the central nervous system, anti-infective medicine, oncology, and women’s health Provides sample size calculations for dose response studies, microarray studies, and Bayesian approaches This new edition is updated throughout, includes many new sections, and five new chapters on emerging topics: two stage seamless adaptive designs, cluster randomized trial design, zero-inflated Poisson distribution, clinical trials with extremely low incidence rates, and clinical trial simulation.

Population Genomics with R

Author : Emmanuel Paradis
Publisher : CRC Press
ISBN 13 : 0429882424
Total Pages : 326 pages
Book Rating : 4.4/5 (298 download)

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Book Synopsis Population Genomics with R by : Emmanuel Paradis

Download or read book Population Genomics with R written by Emmanuel Paradis and published by CRC Press. This book was released on 2020-03-13 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: Population Genomics With R presents a multidisciplinary approach to the analysis of population genomics. The methods treated cover a large number of topics from traditional population genetics to large-scale genomics with high-throughput sequencing data. Several dozen R packages are examined and integrated to provide a coherent software environment with a wide range of computational, statistical, and graphical tools. Small examples are used to illustrate the basics and published data are used as case studies. Readers are expected to have a basic knowledge of biology, genetics, and statistical inference methods. Graduate students and post-doctorate researchers will find resources to analyze their population genetic and genomic data as well as help them design new studies. The first four chapters review the basics of population genomics, data acquisition, and the use of R to store and manipulate genomic data. Chapter 5 treats the exploration of genomic data, an important issue when analysing large data sets. The other five chapters cover linkage disequilibrium, population genomic structure, geographical structure, past demographic events, and natural selection. These chapters include supervised and unsupervised methods, admixture analysis, an in-depth treatment of multivariate methods, and advice on how to handle GIS data. The analysis of natural selection, a traditional issue in evolutionary biology, has known a revival with modern population genomic data. All chapters include exercises. Supplemental materials are available on-line (http://ape-package.ird.fr/PGR.html).

Handbook of Statistical Bioinformatics

Author : Henry Horng-Shing Lu
Publisher : Springer Nature
ISBN 13 : 3662659026
Total Pages : 406 pages
Book Rating : 4.6/5 (626 download)

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Book Synopsis Handbook of Statistical Bioinformatics by : Henry Horng-Shing Lu

Download or read book Handbook of Statistical Bioinformatics written by Henry Horng-Shing Lu and published by Springer Nature. This book was released on 2022-12-08 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology.

Data Analysis for the Life Sciences with R

Author : Rafael A. Irizarry
Publisher : CRC Press
ISBN 13 : 1498775861
Total Pages : 537 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Data Analysis for the Life Sciences with R by : Rafael A. Irizarry

Download or read book Data Analysis for the Life Sciences with R written by Rafael A. Irizarry and published by CRC Press. This book was released on 2016-10-04 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained.

Bioinformatics and Computational Biology Solutions Using R and Bioconductor

Author : Robert Gentleman
Publisher : Springer Science & Business Media
ISBN 13 : 0387293620
Total Pages : 478 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Bioinformatics and Computational Biology Solutions Using R and Bioconductor by : Robert Gentleman

Download or read book Bioinformatics and Computational Biology Solutions Using R and Bioconductor written by Robert Gentleman and published by Springer Science & Business Media. This book was released on 2005-12-29 with total page 478 pages. Available in PDF, EPUB and Kindle. Book excerpt: Full four-color book. Some of the editors created the Bioconductor project and Robert Gentleman is one of the two originators of R. All methods are illustrated with publicly available data, and a major section of the book is devoted to fully worked case studies. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.

Statistical Analysis of Microbiome Data

Author : Somnath Datta
Publisher : Springer Nature
ISBN 13 : 3030733513
Total Pages : 349 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Statistical Analysis of Microbiome Data by : Somnath Datta

Download or read book Statistical Analysis of Microbiome Data written by Somnath Datta and published by Springer Nature. This book was released on 2021-10-27 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: Microbiome research has focused on microorganisms that live within the human body and their effects on health. During the last few years, the quantification of microbiome composition in different environments has been facilitated by the advent of high throughput sequencing technologies. The statistical challenges include computational difficulties due to the high volume of data; normalization and quantification of metabolic abundances, relative taxa and bacterial genes; high-dimensionality; multivariate analysis; the inherently compositional nature of the data; and the proper utilization of complementary phylogenetic information. This has resulted in an explosion of statistical approaches aimed at tackling the unique opportunities and challenges presented by microbiome data. This book provides a comprehensive overview of the state of the art in statistical and informatics technologies for microbiome research. In addition to reviewing demonstrably successful cutting-edge methods, particular emphasis is placed on examples in R that rely on available statistical packages for microbiome data. With its wide-ranging approach, the book benefits not only trained statisticians in academia and industry involved in microbiome research, but also other scientists working in microbiomics and in related fields.

Omic Association Studies with R and Bioconductor

Author : Juan R. González
Publisher : CRC Press
ISBN 13 : 0429803362
Total Pages : 356 pages
Book Rating : 4.4/5 (298 download)

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Book Synopsis Omic Association Studies with R and Bioconductor by : Juan R. González

Download or read book Omic Association Studies with R and Bioconductor written by Juan R. González and published by CRC Press. This book was released on 2019-06-14 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: After the great expansion of genome-wide association studies, their scientific methodology and, notably, their data analysis has matured in recent years, and they are a keystone in large epidemiological studies. Newcomers to the field are confronted with a wealth of data, resources and methods. This book presents current methods to perform informative analyses using real and illustrative data with established bioinformatics tools and guides the reader through the use of publicly available data. Includes clear, readable programming codes for readers to reproduce and adapt to their own data. Emphasises extracting biologically meaningful associations between traits of interest and genomic, transcriptomic and epigenomic data Uses up-to-date methods to exploit omic data Presents methods through specific examples and computing sessions Supplemented by a website, including code, datasets, and solutions

Open Source Software in Life Science Research

Author : Lee Harland
Publisher : Elsevier
ISBN 13 : 1908818247
Total Pages : 583 pages
Book Rating : 4.9/5 (88 download)

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Book Synopsis Open Source Software in Life Science Research by : Lee Harland

Download or read book Open Source Software in Life Science Research written by Lee Harland and published by Elsevier. This book was released on 2012-10-31 with total page 583 pages. Available in PDF, EPUB and Kindle. Book excerpt: The free/open source approach has grown from a minor activity to become a significant producer of robust, task-orientated software for a wide variety of situations and applications. To life science informatics groups, these systems present an appealing proposition - high quality software at a very attractive price. Open source software in life science research considers how industry and applied research groups have embraced these resources, discussing practical implementations that address real-world business problems.The book is divided into four parts. Part one looks at laboratory data management and chemical informatics, covering software such as Bioclipse, OpenTox, ImageJ and KNIME. In part two, the focus turns to genomics and bioinformatics tools, with chapters examining GenomicsTools and EBI Atlas software, as well as the practicalities of setting up an 'omics' platform and managing large volumes of data. Chapters in part three examine information and knowledge management, covering a range of topics including software for web-based collaboration, open source search and visualisation technologies for scientific business applications, and specific software such as DesignTracker and Utopia Documents. Part four looks at semantic technologies such as Semantic MediaWiki, TripleMap and Chem2Bio2RDF, before part five examines clinical analytics, and validation and regulatory compliance of free/open source software. Finally, the book concludes by looking at future perspectives and the economics and free/open source software in industry. - Discusses a broad range of applications from a variety of sectors - Provides a unique perspective on work normally performed behind closed doors - Highlights the criteria used to compare and assess different approaches to solving problems

Evolution of Translational Omics

Author : Institute of Medicine
Publisher : National Academies Press
ISBN 13 : 0309224187
Total Pages : 354 pages
Book Rating : 4.3/5 (92 download)

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Book Synopsis Evolution of Translational Omics by : Institute of Medicine

Download or read book Evolution of Translational Omics written by Institute of Medicine and published by National Academies Press. This book was released on 2012-09-13 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.